Research article Special Issues

A literature review on latest developments of Harmony Search and its applications to intelligent manufacturing

  • Received: 14 December 2018 Accepted: 19 February 2019 Published: 11 March 2019
  • The harmony search (HS) algorithm is one of the most popular meta-heuristic algorithms. The basic idea of HS was inspired by the music improvisation process in which the musicians continuously adjust the pitch of their instruments to generate wonderful harmony. Since its inception in 2001, HS has attracted the attention of many researchers from all over the world, resulting in a lot of improved variants and successful applications. Even for today, the research on improved HS variants design and innovative applications are still hot topics. This paper provides a detailed review of the basic concept of HS and a survey of its latest variants for function optimization. It also provides a survey of the innovative applications of HS in the field of intelligent manufacturing based on about 40 recently published articles. Some potential future research directions for both HS and its applications to intelligent manufacturing are also analyzed and summarized in this paper.

    Citation: Jin Yi, Chao Lu, Guomin Li. A literature review on latest developments of Harmony Search and its applications to intelligent manufacturing[J]. Mathematical Biosciences and Engineering, 2019, 16(4): 2086-2117. doi: 10.3934/mbe.2019102

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  • The harmony search (HS) algorithm is one of the most popular meta-heuristic algorithms. The basic idea of HS was inspired by the music improvisation process in which the musicians continuously adjust the pitch of their instruments to generate wonderful harmony. Since its inception in 2001, HS has attracted the attention of many researchers from all over the world, resulting in a lot of improved variants and successful applications. Even for today, the research on improved HS variants design and innovative applications are still hot topics. This paper provides a detailed review of the basic concept of HS and a survey of its latest variants for function optimization. It also provides a survey of the innovative applications of HS in the field of intelligent manufacturing based on about 40 recently published articles. Some potential future research directions for both HS and its applications to intelligent manufacturing are also analyzed and summarized in this paper.


    It is a significant task to identify the authenticity of an image in several scenarios, such as the media industry, digital image forensics and academic appraisal. It is important to know if an image was tampered because people need to ensure if a certain image can serve as effective evidence for a case or a real result of an experiment. There are different kinds of tampering processing including but not limited to copy-paste, blurring and scale transformation. We want to make a fast and reliable preliminary judgment on tampering. And the widely used JPEG compression algorithm gives us a good chance, we can design an algorithm based on it to achieve our goal. Identification of JPEG compression history has received more and more attention in recent years. When an image is saved in bitmap format but has been compressed by JPEG method, we can not access to the jpg file header which contains the information about compression but we still need to know its compression history sometimes.

    Among all of the lossy compression algorithms, JPEG (Joint Photographic Experts Group) is one of the most popular and widely used standards. Almost all software provide JPEG compression operations choice when saving digital images. Sometimes images have been compressed by the JPEG method are saved into bitmaps. And we can not get the information whether images have been compressed from images files themselves directly because we do not have any access to the JPEG file headers after it has been saved as bitmaps.

    However, this information may be crucial in some cases, in the field of digital image forensics for instance. If the JPEG compression history is efficiently exposed, we can make a preliminary judgment that the image may have been tampered. That is why we need to detect the compression history. Thus, methods for detecting the compression history of bitmaps has become an important issue and received widespread attention.

    Many efforts have been attempted in this aspect, and many decent results have been achieved. Most of these works are related to JPEG coefficient, JPEG quantization table, DCT transformation and wavelet transformation. Based on these, different approaches were proposed.

    Thanh et al. [1] has proposed a method based on the combination of the quantization effect and the statistics of discrete cosine transform coefficient characterized by the statistical model. Hernandez et al. [2] has proposed a method which can avoid giving false results. When their method can not get the quantization table, it means this bitmap may not be compressed or it is not compressed by the JPEG algorithm. These methods have shown some characters of JPEG coefficients which are very meaningful for further works in this aspect.

    And there are some JPEG history detection methods which do not need to estimate the quantization table. Fan et al. [3] proposed a detection method based on the feature of block antifacts in the pixel domain, as the pixel values between the blocks should be inherent if an image was compressed before comparing with uncompressed images. But Fan's method [3] has a relatively high computational complexity. Yang et al. [4] used factor histogram to detect the JPEG compression history of bitmaps, because the decreasing values in factor histogram are observed with the increase of its bin index for uncompressed bitmaps, while no obvious decrease is found in uncompressed images. But Yang's method [4] gets a sudden drop in the accuracy when the compression quality factor is high because the block antifacts phenomenon is not obvious under such circumstances. Especially when the quality factor is 98 or higher the accuracy can go below 50%. Zhang et al. [5] found the tetrolet transformation proposed by Krommweh et al. [6] can be used to exploit the structure of images. Tetrolet transformation is a kind of Harr wavelet transformation which uses at most 177 different kinds of tetrolet as components to disassemble images. The authors proposed a detection method based on the tetrolet transformation to distinguish the uncompressed bitmap image from the decoded JPEG image. As far as we know, Zhang's method [5] has the highest accuracy until now.

    Because JPEG compression algorithm is a kind of lossy compression, the compressed image will lose some kind of information after the compression. Proposed in [7], the number of zeros of the JPEG coefficient is a major factor affecting the compression quality of JPEG. For the same bitmap image, the image quality will continue to improve with the increase of JPEG compression quality factor, while the percentage of zeros of the 64 JPEG coefficients will decrease. And we present a method based on this observation.

    In this paper, we propose a fast, and reliable method to detect the compression history of bitmaps based on image information loss. Our method is faster than most existing similar methods because we do not need to compress the test image in the processing. A lot of methods proposed contain the compression step because they need a comparison version for obtaining the results. Instead of making a compressed image with quality factor 100 in [5], we obtain an estimated original image which is firstly created based on the test image. This processing costs much less time than compression. Extensive experimental results have been achieved which demonstrate that our proposed method outperforms the state of art respect to the detection accuracy and computational complexity. And the accuracy of our method is relatively high, especially when the quality factor of the test images are below 97. Even when the quality factors are as high as 98 and 99, our method still gives acceptable results. What is more, the proposed method can be generally used no matter the test image uses standard or non-standard JPEG quantization table during the compression which means as long as the image was compressed by JPEG method our detection is effective.

    The remaining of the paper is organized as follows. In Section 2, we introduce the relationship of the JPEG coefficients and the image information loss caused by JPEG compression. And the method to create the estimated original image is also described. The framework and the details of the algorithm are stated in Section 3. In Section 4, the experimental results are shown and discussed. And conclusions will be drawn in section 5.

    In this paper, the quality factor Q is an important factor that determines the quality of the JPEG image, and the DCT coefficient after quantization is called the JPEG coefficient, which can be read directly from the JPEG image file. The number of zero of the JPEG coefficient is a major factor affecting the compression quality of JPEG images. Through extensive experiments, we find that the proportion of zero JPEG coefficients on the 64 DCT positions show downward trends as the image compression quality factor increase. In other words, for the same bitmap image, the higher the compression quality is, the less the image information loss is and the lower the percentage of zero among 64 JPEG coefficients is. So, the percentage of zero JPEG coefficients on different frequencies can be defined as the index of the amount of information loss after bitmaps were compressed by the JPEG method.

    When an image is compressed by JPEG, it will firstly be separated into several 8 × 8 blocks. Then each block is operated by DCT respectively. For each block, there are 64 positions. The first step of our method is doing statistics on the number of zero on 64 positions among all blocks. Note n(j) as the total number of zeros on jth position and m is the number of blocks. Then the amount of image information loss on the 64 DCT positions can be expressed as:

    p(j)=n(j)m,j=1,2,...,64 (2.1)

    and the average image information loss is expressed as:

    averageloss=64j=1p(j)/64 (2.2)

    Figure 1 illustrates the result of the average information loss of a bitmap after compressed into JPEG images with the quality factor varying from 60 to 100. The average image information loss decreases as the growth of quality factor Q.

    Figure 1.  The curve of average information loss with the increase of quality factor Q.

    Respecting to this observation, we obtain JPEG images from an uncompressed image Ibmp with different quality factors. And then the JPEG images are decoded to decompressed bitmap images. The uncompressed image and the decompressed images are JPEG compressed with the quality factor 100 to obtain IJPEG1 and IJPEG2 respectively. Obviously, the IJPEG1 is a single JPEG compressed image and IJPEG2 undergoes double JPEG compression. We can compare the amount of image information loss between IJPEG1 and IJPEG2 to achieve the goal of making a primary judge on compression. The higher difference between IJPEG1 and IJPEG2 means higher information loss.

    But please notice that in the example there is an assumption that we have the original lossless image and then compress it. And we make the judgement according to the contrast. But in the real case, the original lossless image is usually unacquirable. Therefore, we have to estimate the original image first.

    As proposed in [8,9], the image will be separated into blocks, when it undergoes JPEG compression. Then these blocks are operated separately. And to shrink the file size we tolerant some information loss during the quantization. Certain frequency signals are abandoned in quantization step especially for those high-frequency harmonics which only causes very little even no change for the human visual system (HVS). These signals mean redundancy to the human visual system, while they contain a lot of information. That is why they are important to the detection of compression. The image which has not been compressed or just been compressed with relatively high-quality factors remains more information which is a series of signals having different frequencies. Normally, a compressed image has lost a considerable amount of harmonics. Most high-frequency components are set to zero and some low-frequency components are also set to zero if they are small enough. It is true that we can not get what has been abandoned in the previous processing again because of the lossy JPEG compression. But it is still possible to estimate that information existing in the original image of the test image. During the JPEG compression, DCT and quantization are used on each block but not on the full image, which has been discussed in [10]. So, even those harmonics are lost in each separated 8 × 8 blocks but they are still existing among the full-size image. If we want to expose this information, we need to break the existing block artifacts. A method of cutting 4 rows and 4 columns of the test image widely used in image steganalysis [11] is employed to estimate the counterpart of the original image.

    The removal of left-top 4 rows and columns has been approved as an excellent way to estimate the counterpart of the original image from compression, which means similar statistic features, as the cut destroys the block-based structure of JPEG. The row and column cutting are illustrated in Figure 2.

    Figure 2.  Original image estimation.

    Based on the image information loss, we propose a novel algorithm to detect the JPEG compression history as Figure 3 illustrated. The idea of extracting feature from the JPEG file is based on [12].

    Figure 3.  The framework of algorithm based on image information loss.

    The whole processing is as following:

    ⅰ. To obtain IJPEG1, the test bitmap image is JPEG compressed with quality factor Q = 100.

    ⅱ. The counterpart of the original image is estimated by cutting 4 rows and 4 columns from the test image. The IJPEG2 is acquired by compressing the counterpart with quality factor Q = 100 as well.

    ⅲ. The features related to the image information loss are extracted from the two JPEG images, and then fed into the classifier to detect whether the test bitmap image has been compressed.

    Considering the test image as a decomposed JPEG image, as Figure 4 illustrated. IJPEG1 actually undergoes double JPEG compression with an unknown previous quality factor and the latter quality factor of 100. And because of the counterpart estimated by cutting rows and columns, the IJPEG2 could be considered as a single JPEG compressed image with the quality factor of 100. For the percentage of zero of JPEG coefficients among the 64 DCT positions are defined as the indexes of the amount of information loss after the bitmap image is JPEG compressed, there are disparities between the corresponding indexes IJPEG1 of IJPEG2 and on the 64 DCT positions, as shown in Figure 5. A higher information loss is expected for a JPEG compressed test image. On the contrary, if the test image is uncompressed, there should be no obvious differences between indexes on corresponding positions, as shown in Figure 6.

    Figure 4.  The original decompressed image.
    Figure 5.  The comparison of testing image and estimated original image in the case that the test image is decompressed from the JPEG image with quality factor Q = 90.
    Figure 6.  The comparison of testing image and estimated original image in the case that the test image is uncompressed.

    p1(j) denotes the indexes of the image information loss of IJPEG1. p2(j) denotes the indexes of the image information loss of IJPEG2. Then we describe the difference of information loss as

    pdif(j)=p1(j)p2(j),j=1,2,...,64 (3.1)
    pdif_average(j)=64j=1pdif(j)/64 (3.2)

    pdif_average indicates how much details are found in the estimation counterpart comparing with the test image. If the testing image is uncompressed, the value of pdif_average will be close to 1 which means there is no obvious difference between the test image and the estimation original image. If the test image was compressed, this value will be much greater than 1 which means the bias is observed between the IJPEG1 and IJPEG2 after breaking the 8 × 8 blocks in the test image by cutting. After extracting this feature of images, an SVM classifier is trained. And then we can detect the bitmap JPEG compression history with this model.

    Two image databases are used in our experiments to evaluate the performance of the proposed method. Firstly, 1338 uncompressed images from the UCID image database are used in our experiments. These images are saved in Tif format with the resolution of 512 × 384. And a series of standard JPEG quality factors (60, 70, 75, 80, 85, 90, 95, 96, 97, 98, 99) are applied to the images to obtain JPEG compression images of different qualities. The JPEG images with different quality are resaved in Tif format for evaluating the proposed algorithm. In the following, this image dataset is named as dataset1.

    The other 480 images come from the well-known Dresden database. Different from the UCID database, the images from the Dresden database are captured by consumer cameras and saved as JPEG image originally. In our experiments, we use 480 JPEG images from 4 different cameras, which are Agfa DC-830i, Canon PowerShotA640, Nikon D200 and Sony DSC-W170, 120 images from each camera. Different from the JPEG images obtained in dataset1, these images are compressed with different consumer-defined JPEG quantization tables with various camera models. Also, the images, named as dataset2 with the resolution of 3872 × 2592, are resaved as bitmap images for the experiments.

    We take 500 uncompressed images and 11 × 500 decompressed JPEG images from dataset1as the labeled samples to train the SVM classifier with RBF kernel. After we get the model we use it to test the rest images with different quality factors (60, 70, 75, 80, 85, 90, 95, 96, 97, 98, 99). We also compare our proposed method with Yang's [4], Fan's [3] and Zhang's [5] methods in terms of detection accuracy and algorithm complexity respectively. The results are shown in Table 1 to Table 3.

    Table 1.  Identification accuracy (%) of the proposed method and baselines for dataset1.
    Methods Q original
    60 70 80 85 90 95 96 97 98 99
    Fan's 97.10 96.68 96.00 95.14 89.78 69.14 59.80 48.33 25.53 17.27 84.10
    Yang's 99.90 100 100 100 99.80 98.69 96.58 88.74 78.16 39.79 96.59
    Zhang's 100 100 100 100 100 100 100 99.93 99.10 95.65 99.88
    Proposed 100 100 100 100 100 100 99.93 99.48 99.03 89.31 99.92

     | Show Table
    DownLoad: CSV
    Table 2.  Identification accuracy (%) of the proposed method and the baseline for images in dataset2.
    Method Accuracy(%)
    Zhangs 34.08
    Proposed 100

     | Show Table
    DownLoad: CSV
    Table 3.  Average time cost.
    Method Time cost(s)
    (image with resolution of 384 × 512)
    Fans 2.73
    Yangs 0.91
    Zhangs 9.64
    Proposed 0.60

     | Show Table
    DownLoad: CSV

    As shown in Table 1, Fan's method can give relatively good results when the quality factor is less than 85. The detection accuracy goes below 90% when the quality factors are greater than 90. Yang's method has a similar shortcoming. It performs well when the quality is 96 but the accuracy goes below 90% when the quality factors are greater. Zhang's method has a really good result. The detection accuracy is 95.65% when the quality factor is as high as 99. Our method outperforms Fan's and Yang's methods. And similar detection results are observed between Zhang's and the proposed method. While Zhang's method works better when the quality factor is 99, our method can give results in the shortest time, as shown in Table 3. Also, it can be proved that average cost time for each pixel is stable by simple computation. Time cost may be not the most important index in this aspect. But we can get reliable results within less time indeed. This may have a great advantage in some cases.

    Another comparison experiment is implemented between the proposed and Zhang's methods [5]. We take 480 compressed images from dataset2 to prove that our method can work on all JPEG compressed images. All of these bitmap images are not compressed using standard JPEG quantization but took by cameras which means they were compressed using customer-defined JPEG quantization tables. The results are shown in Table 2. Zhang's method is found only effective to images compressed using the standard JPEG quantization tables. And the proposed method still performs well.

    The issue of detecting the compression history of images receives more and more attention in recent years. In this paper, we propose a novel and fast detecting method based on novel feature respect to image information loss. According to this, the proportion of zero JPEG coefficients on 64 DCT positions falls down as well. We estimate the image counterpart by cutting 4 rows and 4 columns from the original image and calculate the differences between the values of the 64 DCT positions respectively. The feature extracted from the differences is fed into the SVM to train a mode to classify the test bitmap images. Extensive experiments and the results demonstrate that our proposed method outperforms the state of art, especially in the cases of high compression quality factors and customer-defined quality factors. And also the proposed algorithm indicates a lower computational complexity compared to the previous works.

    This work is supported by the National Science Foundation of China (No. 61502076, No. 61772111).

    All authors declare no conflicts of interest in this paper.



    [1] Z.W. Geem, Music-inspired harmony search algorithm: theory and applications, Springer, 2009.
    [2] J. Kennedy, Particle swarm optimization, in Encyclopedia of machine learning, Springer, 2011, 760–766.
    [3] M. Dorigo, V. Maniezzo and A. Colorni, Ant system: optimization by a colony of cooperating agents, IEEE T. Syst. Man. Cy. B., 26 (1996), 29–41.
    [4] D. Karaboga and B. Basturk, A powerful and efficient algorithm for numerical function optimization: artificial bee colony (abc) algorithm, J. Global. Optim., 39 (2007), 459–471.
    [5] X. S. Yang and S. Deb, Cuckoo search via lévy flights, in Nature & Biologically Inspired Computing, 2009. NaBIC 2009. World Congress on, IEEE, 2009, 210–214.
    [6] J. H. Holland, Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence, MIT press, 1992.
    [7] R. Storn and K. Price, Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces, J. Global. Optim., 11 (1997), 341–359.
    [8] S. Kirkpatrick, Optimization by simulated annealing: Quantitative studies, J. Stat. Phy., 34 (1984), 975–986.
    [9] A. Y. Lam and V. O. Li, Chemical-reaction-inspired metaheuristic for optimization, IEEE Trans. Evol. Comput., 14 (2010), 381–399.
    [10] Z. W. Geem, J. H. Kim and G. V. Loganathan, A new heuristic optimization algorithm: harmony search, Simulation, 76 (2001), 60–68.
    [11] J. Yi, X. Li, L. Gao, et al., Optimal design of photovoltaic-wind hybrid renewable energy system using a discrete geometric selective harmony search, in Computer Supported Cooperative Work in Design (CSCWD), 2015 IEEE 19th International Conference on, IEEE, (2015), 499–504.
    [12] A. Chauhan and R. Saini, Discrete harmony search based size optimization of integrated renewable energy system for remote rural areas of uttarakhand state in india, Renew. Energy, 94 (2016), 587–604.
    [13] Z. W. Geem and Y. Yoon, Harmony search optimization of renewable energy charging with energy storage system, Int. J. Electr. Power Energy Syst., 86 (2017), 120–126.
    [14] C. Camacho-Gómez, S. Jiménez-Fernández, R. Mallol-Poyato, et al., Optimal design of microgrid's network topology and location of the distributed renewable energy resources using the harmony search algorithm, Soft Comput., 1 (2018), 1–16.
    [15] H. B. Ouyang, L. Q. Gao, S. Li, et al., Improved novel global harmony search with a new relaxation method for reliability optimization problems, Inform. Sci., 305 (2015), 14–55.
    [16] W. Zeng, J. Yi, X. Rao, et al., A two-stage path planning approach for multiple car-like robots based on ph curves and a modified harmony search algorithm, Eng. Optimiz., 49 (2017), 1995– 2012.
    [17] S. Kundu and D. R. Parhi, Navigation of underwater robot based on dynamically adaptive harmony search algorithm, Memet. Comput., 8 (2016), 125–146.
    [18] J. P. Papa, W. Scheirer and D. D. Cox, Fine-tuning deep belief networks using harmony search, Appl. Soft. Comput., 46 (2016), 875–885.
    [19] W. Y. Lee, S. M. Park and K. B. Sim, Optimal hyperparameter tuning of convolutional neural networks based on the parameter-setting-free harmony search algorithm, Optik, 172 (2018), 359– 367.
    [20] S. Kulluk, L. Ozbakir and A. Baykasoglu, Training neural networks with harmony search algorithms for classification problems, Eng. Appl. Artif. Intell., 25 (2012), 11–19.
    [21] S. Mun and Y. H. Cho, Modified harmony search optimization for constrained design problems, Expert. Syst. Appl., 39 (2012), 419–423.
    [22] V. R. Pandi and B. K. Panigrahi, Dynamic economic load dispatch using hybrid swarm intelligence based harmony search algorithm, Expert. Syst. Appl., 38 (2011), 8509–8514.
    [23] A. Kusiak, Intelligent manufacturing systems., Prentice Hall Press, 200 Old Tappan Toad, Old Tappan, NJ 07675, USA, (1990), 448.
    [24] T. Ghosh, S. Sengupta, M. Chattopadhyay, et al., Meta-heuristics in cellular manufacturing: A state-of-the-art review, Int. J. Ind. Eng. Comput., 2 (2011), 87–122.
    [25] M. Alavidoost, M. F. Zarandi, M. Tarimoradi, et al., Modified genetic algorithm for simple straight and u-shaped assembly line balancing with fuzzy processing times, J. Intell. Manuf., 28 (2017), 313–336.
    [26] C. L. Kuo, C. H. Chu, Y. Li, et al., Electromagnetism-like algorithms for optimized tool path planning in 5-axis flank machining, Comput. Ind. Eng., 84 (2015), 70–78.
    [27] X. Li, C. Lu, L. Gao, et al., An effective multi-objective algorithm for energy efficient scheduling in a real-life welding shop, IEEE T. Ind. Inform., 14 (2018), 5400–5409.
    [28] C. Lu, L. Gao, X. Li, et al., A multi-objective approach to welding shop scheduling for makespan, noise pollution and energy consumption, J. Cleaner. Prod., 196 (2018), 773–787.
    [29] C. Lu, L. Gao, Q. Pan, et al., A multi-objective cellular grey wolf optimizer for hybrid flowshop scheduling problem considering noise pollution, Appl. Soft. Comput., 75 (2019), 728–749.
    [30] X. S. Yang, Harmony search as a metaheuristic algorithm, in Music-inspired harmony search algorithm, Springer, (2009), 1–14.
    [31] G. Ingram and T. Zhang, Overview of applications and developments in the harmony search algorithm, in Music-inspired harmony search algorithm, Springer, (2009), 15–37.
    [32] O. Moh'd Alia and R. Mandava, The variants of the harmony search algorithm: an overview, Artif. Intell. Rev., 36 (2011), 49–68.
    [33] X. Wang, X. Z. Gao and K. Zenger, The variations of harmony search and its current research trends, in An Introduction to Harmony Search Optimization Method, Springer, (2015), 21–30.
    [34] A. Askarzadeh, Solving electrical power system problems by harmony search: a review, Artif. Intell. Rev., 47 (2017), 217–251.
    [35] A. Askarzadeh and E. Rashedi, Harmony search algorithm: Basic concepts and engineering applications, in Intelligent Systems: Concepts, Methodologies, Tools, and Applications, 1–30.
    [36] J. Yi, X. Li, C. H. Chu, et al., Parallel chaotic local search enhanced harmony search algorithm for engineering design optimization, J. Intell. Manuf., 30 (2019), 405–428.
    [37] M. Mahdavi, M. Fesanghary and E. Damangir, An improved harmony search algorithm for solving optimization problems, Appl. Math. Comput., 188 (2007), 1567–1579.
    [38] M. G. Omran and M. Mahdavi, Global-best harmony search, Appl. Math. Comput., 198 (2008), 643–656.
    [39] Q. K. Pan, P. N. Suganthan, M. F. Tasgetiren, et al., A self-adaptive global best harmony search algorithm for continuous optimization problems, Appl. Math. Comput., 216 (2010), 830–848.
    [40] D. Zou, L. Gao, J.Wu, et al., Novel global harmony search algorithm for unconstrained problems, Neurocomputing, 73 (2010), 3308–3318.
    [41] J. Chen, Q. K. Pan and J. Q. Li, Harmony search algorithm with dynamic control parameters, Appl. Math. Comput., 219 (2012), 592–604.
    [42] R. Enayatifar, M. Yousefi, A. H. Abdullah, et al., Lahs: a novel harmony search algorithm based on learning automata, Commun. Nonlinear Sci. Numer. Simul., 18 (2013), 3481–3497.
    [43] A. Kattan and R. Abdullah, A dynamic self-adaptive harmony search algorithm for continuous optimization problems, Appl. Math. Comput., 219 (2013), 8542–8567.
    [44] K. Luo, A novel self-adaptive harmony search algorithm, J. Appl. Math., 2013 (2013), 1–16.
    [45] X.Wang and X. Yan, Global best harmony search algorithm with control parameters co-evolution based on pso and its application to constrained optimal problems, Appl. Math. Comput., 219 (2013), 10059–10072.
    [46] J. Contreras, I. Amaya and R. Correa, An improved variant of the conventional harmony search algorithm, Appl. Math. Comput., 227 (2014), 821–830.
    [47] M. Khalili, R. Kharrat, K. Salahshoor, et al., Global dynamic harmony search algorithm: Gdhs, Appl. Math. Comput., 228 (2014), 195–219.
    [48] V. Kumar, J. K. Chhabra and D. Kumar, Parameter adaptive harmony search algorithm for unimodal and multimodal optimization problems, J. Comput. Sci., 5 (2014), 144–155.
    [49] G. Li and Q. Wang, A cooperative harmony search algorithm for function optimization, Math. Probl. Eng., 2014 (2014), 1–14.
    [50] I. Amaya, J. Cruz and R. Correa, Harmony search algorithm: a variant with self-regulated fretwidth, Appl. Math. Comput., 266 (2015), 1127–1152.
    [51] J. Kalivarapu, S. Jain and S. Bag, An improved harmony search algorithm with dynamically varying bandwidth, Eng. Optimiz., 48 (2016), 1091–1108.
    [52] Y. Wang, Z. Guo and Y. Wang, Enhanced harmony search with dual strategies and adaptive parameters, Soft Comput., 21 (2017), 4431–4445.
    [53] Z. Guo, H. Yang, S. Wang, et al., Adaptive harmony search with best-based search strategy, Soft Comput., 22 (2018), 1335–1349.
    [54] M. A. Al-Betar, I. A. Doush, A. T. Khader, et al., Novel selection schemes for harmony search, Appl. Math. Comput., 218 (2012), 6095–6117.
    [55] M. A. Al-Betar, A. T. Khader, Z. W. Geem, et al., An analysis of selection methods in memory consideration for harmony search, Appl. Math. Comput., 219 (2013), 10753–10767.
    [56] M. Castelli, S. Silva, L. Manzoni, et al., Geometric selective harmony search, Inform. Sci., 279 (2014), 468–482.
    [57] X. Gao, X. Wang, S. Ovaska, et al., A hybrid optimization method of harmony search and opposition-based learning, Eng. Optimiz., 44 (2012), 895–914.
    [58] A. Kaveh and M. Ahangaran, Social harmony search algorithm for continuous optimization, Iran. J. Sci. Technol. Trans. B-Eng., 36 (2012), 121–137.
    [59] P. Yadav, R. Kumar, S. K. Panda, et al., An intelligent tuned harmony search algorithm for optimisation, Inform. Sci., 196 (2012), 47–72.
    [60] M. A. Al-Betar, A. T. Khader, M. A. Awadallah, et al., Cellular harmony search for optimization problems, J. Appl. Math., 2013 (2013), 1–20.
    [61] S. Ashrafi and A. Dariane, Performance evaluation of an improved harmony search algorithm for numerical optimization: Melody search (ms), Eng. Appl. Artif. Intell., 26 (2013), 1301–1321.
    [62] M. El-Abd, An improved global-best harmony search algorithm, Appl. Math. Comput., 222 (2013), 94–106.
    [63] B. H. F. Hasan, I. A. Doush, E. Al Maghayreh, et al., Hybridizing harmony search algorithm with different mutation operators for continuous problems, Appl. Math. Comput., 232 (2014), 1166–1182.
    [64] E. Valian, S. Tavakoli and S. Mohanna, An intelligent global harmony search approach to continuous optimization problems, Appl. Math. Comput., 232 (2014), 670–684.
    [65] A. M. Turky and S. Abdullah, A multi-population harmony search algorithm with external archive for dynamic optimization problems, Inform. Sci., 272 (2014), 84–95.
    [66] M. A. Al-Betar, M. A. Awadallah, A. T. Khader, et al., Island-based harmony search for optimization problems, Expert. Syst. Appl., 42 (2015), 2026–2035.
    [67] J. Yi, L. Gao, X. Li, et al., An efficient modified harmony search algorithm with intersect mutation operator and cellular local search for continuous function optimization problems, Appl. Intell., 44 (2016), 725–753.
    [68] B. Keshtegar and M. O. Sadeq, Gaussian global-best harmony search algorithm for optimization problems, Soft Comput., 21 (2017), 7337–7349.
    [69] H. B. Ouyang, L. Q. Gao, S. Li, et al., Improved harmony search algorithm: Lhs, Appl. Soft. Comput., 53 (2017), 133–167.
    [70] E. A. Portilla-Flores, Á . Sańchez-Maŕquez, L. Flores-Pulido, et al., Enhancing the harmony search algorithm performance on constrained numerical optimization, IEEE Access, 5 (2017), 25759–25780.
    [71] S. Tuo, L. Yong and T. Zhou, An improved harmony search based on teaching-learning strategy for unconstrained optimization problems, Math. Probl. Eng., 2013 (2013), 1–21.
    [72] G. Wang, L. Guo, H. Duan, et al., Hybridizing harmony search with biogeography based optimization for global numerical optimization, J. Comput. Theor. Nanosci., 10 (2013), 2312–2322.
    [73] G. G.Wang, A. H. Gandomi, X. Zhao, et al., Hybridizing harmony search algorithm with cuckoo search for global numerical optimization, Soft Comput., 20 (2016), 273–285.
    [74] X. Yuan, J. Zhao, Y. Yang, et al., Hybrid parallel chaos optimization algorithm with harmony search algorithm, Appl. Soft. Comput., 17 (2014), 12–22.
    [75] F. Zhao, Y. Liu, C. Zhang, et al., A self-adaptive harmony pso search algorithm and its performance analysis, Expert. Syst. Appl., 42 (2015), 7436–7455.
    [76] A. Fouad, D. Boukhetala, F. Boudjema, et al., A novel global harmony search method based on ant colony optimisation algorithm, J. Exp. Theor. Artif. Intell., 28 (2016), 215–238.
    [77] G. Zhang and Y. Li, A memetic algorithm for global optimization of multimodal nonseparable problems, IEEE T. Cy., 46 (2016), 1375–1387.
    [78] A. Assad and K. Deep, A hybrid harmony search and simulated annealing algorithm for continuous optimization, Inform. Sci., 450 (2018), 246–266.
    [79] A. Sadollah, H. Sayyaadi, D. G. Yoo, et al., Mine blast harmony search: A new hybrid optimization method for improving exploration and exploitation capabilities, Appl. Soft. Comput., 68 (2018), 548–564.
    [80] L. Wang, H. Hu, R. Liu, et al., An improved differential harmony search algorithm for function optimization problems, Soft Comput., (2018), 1–26.
    [81] B. L. Miller and D. E. Goldberg, Genetic algorithms, selection schemes, and the varying effects of noise, Evol. Comput., 4 (1996), 113–131.
    [82] Y. Shi, H. Liu, L. Gao, et al., Cellular particle swarm optimization, Inform. Sci., 181 (2011), 4460–4493.
    [83] C. Lu, L. Gao and J. Yi, Grey wolf optimizer with cellular topological structure, Expert. Syst. Appl., 107 (2018), 89–114.
    [84] A. Turky, S. Abdullah and A. Dawod, A dual-population multi operators harmony search algorithm for dynamic optimization problems, Comput. Ind. Eng., 117 (2018), 19–28.
    [85] G. Wang and L. Guo, A novel hybrid bat algorithm with harmony search for global numerical optimization, J. Appl. Math., 2013 (2013), 1–21.
    [86] K. Z. Gao, P. N. Suganthan, Q. K. Pan, et al., Pareto-based grouping discrete harmony search algorithm for multi-objective flexible job shop scheduling, Inform. Sci., 289 (2014), 76–90.
    [87] K. Z. Gao, P. N. Suganthan, Q. K. Pan, et al., An effective discrete harmony search algorithm for flexible job shop scheduling problem with fuzzy processing time, Int. J. Prod. Res., 53 (2015), 5896–5911.
    [88] K. Z. Gao, P. N. Suganthan, Q. K. Pan, et al., Discrete harmony search algorithm for flexible job shop scheduling problem with multiple objectives, J. Intell. Manuf., 27 (2016), 363–374.
    [89] K. Gao, L.Wang, J. Luo, et al., Discrete harmony search algorithm for scheduling and rescheduling the reprocessing problems in remanufacturing: a case study, Eng. Optimiz., 50 (2018), 965– 981.
    [90] L. Liu and H. Zhou, Hybridization of harmony search with variable neighborhood search for restrictive single-machine earliness/tardiness problem, Inform. Sci., 226 (2013), 68–92.
    [91] Y. Yuan, H. Xu and J. Yang, A hybrid harmony search algorithm for the flexible job shop scheduling problem, Appl. Soft. Comput., 13 (2013), 3259–3272.
    [92] F. Zammori, M. Braglia and D. Castellano, Harmony search algorithm for single-machine scheduling problem with planned maintenance, Comput. Ind. Eng., 76 (2014), 333–346.
    [93] Y. Li, X. Li and J. N. Gupta, Solving the multi-objective flowline manufacturing cell scheduling problem by hybrid harmony search, Expert. Syst. Appl., 42 (2015), 1409–1417.
    [94] C. Garcia-Santiago, J. Del Ser, C. Upton, et al., A random-key encoded harmony search approach for energy-efficient production scheduling with shared resources, Eng. Optimiz., 47 (2015), 1481–1496.
    [95] A. Maroosi, R. C. Muniyandi, E. Sundararajan, et al., A parallel membrane inspired harmony search for optimization problems: A case study based on a flexible job shop scheduling problem, Appl. Soft. Comput., 49 (2016), 120–136.
    [96] Z. Guo, L. Shi, L. Chen, et al., A harmony search-based memetic optimization model for integrated production and transportation scheduling in mto manufacturing, Omega, 66 (2017), 327– 343.
    [97] F. Zhao, Y. Liu, Y. Zhang, et al., A hybrid harmony search algorithm with efficient job sequence scheme and variable neighborhood search for the permutation flow shop scheduling problems, Eng. Appl. Artif. Intell., 65 (2017), 178–199.
    [98] M. Gaham, B. Bouzouia and N. Achour, An effective operations permutation-based discrete harmony search approach for the flexible job shop scheduling problem with makespan criterion, Appl. Intell., 48 (2018), 1423–1441.
    [99] F. Zhao, S. Qin, G. Yang, et al., A differential-based harmony search algorithm with variable neighborhood search for job shop scheduling problem and its runtime analysis, IEEE Access, 6 (2018), 76313–76330.
    [100] S. M. Lee and S. Y. Han, Topology optimization scheme for dynamic stiffness problems using harmony search method, Int. J. Precis. Eng. Manuf., 17 (2016), 1187–1194.
    [101] S. M. Lee and S. Y. Han, Topology optimization based on the harmony search method, J. Mech. Sci. Technol., 31 (2017), 2875–2882.
    [102] J. Yi, X. Li, M. Xiao, et al., Construction of nested maximin designs based on successive local enumeration and modified novel global harmony search algorithm, Eng. Optimiz., 49 (2017), 161–180.
    [103] B. Keshtegar, P. Hao, Y. Wang, et al., Optimum design of aircraft panels based on adaptive dynamic harmony search, Thin-Walled Struct., 118 (2017), 37–45.
    [104] B. Keshtegar, P. Hao, Y. Wang, et al., An adaptive response surface method and gaussian globalbest harmony search algorithm for optimization of aircraft stiffened panels, Appl. Soft. Comput., 66 (2018), 196–207.
    [105] H. Ouyang, W. Wu, C. Zhang, et al., Improved harmony search with general iteration models for engineering design optimization problems, Soft Comput., 0 (2018), 1–36.
    [106] X. Li, K. Qin, B. Zeng, et al., Assembly sequence planning based on an improved harmony search algorithm, Int. J. Adv. Manuf. Tech., 84 (2016), 2367–2380.
    [107] X. Li, K. Qin, B. Zeng, et al., A dynamic parameter controlled harmony search algorithm for assembly sequence planning, Int. J. Adv. Manuf. Tech., 92 (2017), 3399–3411.
    [108] G. Li, B. Zeng, W. Liao, et al., A new agv scheduling algorithm based on harmony search for material transfer in a real-world manufacturing system, Adv. Mech. Eng., 10 (2018), 1–13.
    [109] G. Li, X. Li, L. Gao, et al., Tasks assigning and sequencing of multiple agvs based on an improved harmony search algorithm, J. Ambient Intell. Humaniz., 0 (2018), 1–14.
    [110] M. B. B. Mahaleh and S. A. Mirroshandel, Harmony search path detection for vision based automated guided vehicle, Robot. Auton. Syst., 107 (2018), 156–166.
    [111] M. Ayyıldız and K. C¸ etinkaya, Comparison of four different heuristic optimization algorithms for the inverse kinematics solution of a real 4-dof serial robot manipulator, Neural Comput. Appl., 27 (2016), 825–836.
    [112] O. Zarei, M. Fesanghary, B. Farshi, et al., Optimization of multi-pass face-milling via harmony search algorithm, J. Mater. Process. Technol., 209 (2009), 2386–2392.
    [113] K. Abhishek, S. Datta and S. S. Mahapatra, Multi-objective optimization in drilling of cfrp (polyester) Measurement, 77 (2016), 222–239.
    [114] S. Kumari, A. Kumar, R. K. Yadav, et al., Optimisation of machining parameters using grey relation analysis integrated with harmony search for turning of aisi d2 steel, Materials Today: Proceedings, 5 (2018), 12750–12756.
    [115] J. Yi, C. H. Chu, C. L. Kuo, et al., Optimized tool path planning for five-axis flank milling of ruled surfaces using geometric decomposition strategy and multi-population harmony search algorithm, Appl. Soft. Comput., 73 (2018), 547–561.
    [116] S. Atta, P. R. S. Mahapatra and A. Mukhopadhyay, Solving tool indexing problem using harmony search algorithm with harmony refinement, Soft Comput., (2018), 1–17.
    [117] C. C. Lin, D. J. Deng, Z. Y. Chen, et al., Key design of driving industry 4.0: Joint energy-efficient deployment and scheduling in group-based industrial wireless sensor networks, IEEE Commun. Mag., 54 (2016), 46–52.
    [118] B. Zeng and Y. Dong, An improved harmony search based energy-efficient routing algorithm for wireless sensor networks, Appl. Soft. Comput., 41 (2016), 135–147.
    [119] L. Wang, L. An, H. Q. Ni, et al., Pareto-based multi-objective node placement of industrial wireless sensor networks using binary differential evolution harmony search, Adv. Manuf., 4 (2016), 66–78.
    [120] O. Moh'd Alia and A. Al-Ajouri, Maximizing wireless sensor network coverage with minimum cost using harmony search algorithm, IEEE Sens. J., 17 (2017), 882–896.
    [121] C. C. Lin, D. J. Deng, J. R. Kang, et al., Forecasting rare faults of critical components in led epitaxy plants using a hybrid grey forecasting and harmony search approach, IEEE Trans. Ind. Inform., 12 (2016), 2228–2235.
    [122] S. Kang and J. Chae, Harmony search for the layout design of an unequal area facility, Expert. Syst. Appl., 79 (2017), 269–281.
    [123] J. Lin, M. Liu, J. Hao, et al., Many-objective harmony search for integrated order planning in steelmaking-continuous casting-hot rolling production of multi-plants, Int. J. Prod. Res., 55 (2017), 4003–4020.
    [124] Y. H. Kim, Y. Yoon and Z. W. Geem, A comparison study of harmony search and genetic algorithm for the max-cut problem, Swarm Evol. Comput., 44 (2018), 130–135.
    [125] B. Naderi, R. Tavakkoli-Moghaddam, et al., Electromagnetism-like mechanism and simulated annealing algorithms for flowshop scheduling problems minimizing the total weighted tardiness and makespan, Knowledge-Based Syst., 23 (2010), 77–85.
    [126] M. R. Garey, D. S. Johnson and R. Sethi, The complexity of flowshop and jobshop scheduling, Math. Oper. Res., 1 (1976), 117–129.
    [127] P. J. Van Laarhoven, E. H. Aarts and J. K. Lenstra, Job shop scheduling by simulated annealing, Oper. Res., 40 (1992), 113–125.
    [128] M. Dell'Amico and M. Trubian, Applying tabu search to the job-shop scheduling problem, Ann. Oper. Res., 41 (1993), 231–252.
    [129] I. Kacem, S. Hammadi and P. Borne, Approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems, IEEE T. Syst. Man. Cy. C., 32 (2002), 1–13.
    [130] W. Xia and Z. Wu, An effective hybrid optimization approach for multi-objective flexible jobshop scheduling problems, Comput. Ind. Eng., 48 (2005), 409–425.
    [131] G. Zhang, X. Shao, P. Li, et al., An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem, Comput. Ind. Eng., 56 (2009), 1309–1318.
    [132] G. Zhang, L. Gao and Y. Shi, An effective genetic algorithm for the flexible job-shop scheduling problem, Expert. Syst. Appl., 38 (2011), 3563–3573.
    [133] Q. Lin, L. Gao, X. Li, et al., A hybrid backtracking search algorithm for permutation flow-shop scheduling problem, Comput. Ind. Eng., 85 (2015), 437–446.
    [134] C. Lu, L. Gao, X. Li, et al., Energy-efficient permutation flow shop scheduling problem using a hybrid multi-objective backtracking search algorithm, J. Cleaner. Prod., 144 (2017), 228–238.
    [135] C. Viergutz and S. Knust, Integrated production and distribution scheduling with lifespan constraints, Ann. Oper. Res., 213 (2014), 293–318.
    [136] R. T. Lund, Remanufacturing, Technol. Rev., 87 (1984), 18.
    [137] Y. Liu, H. Dong, N. Lohse, et al., An investigation into minimising total energy consumption and total weighted tardiness in job shops, J. Cleaner. Prod., 65 (2014), 87–96.
    [138] M. Mashayekhi, E. Salajegheh and M. Dehghani, Topology optimization of double and triple layer grid structures using a modified gravitational harmony search algorithm with efficient member grouping strategy, Comput. Struct., 172 (2016), 40–58.
    [139] M. F. F. Rashid, W. Hutabarat and A. Tiwari, A review on assembly sequence planning and assembly line balancing optimisation using soft computing approaches, Int. J. Adv. Manuf. Tech., 59 (2012), 335–349.
    [140] D. Ghosh, A new genetic algorithm for the tool indexing problem, Technical report, Indian Institute of Management Ahmedabad, 2016.
    [141] D. Ghosh, Exploring Lin Kernighan neighborhoods for the indexing problem, Technical report, Indian Institute of Management Ahmedabad, 2016.
    [142] M. Hermann, T. Pentek and B. Otto, Design principles for industrie 4.0 scenarios, in System Sciences (HICSS), 2016 49th Hawaii International Conference on, IEEE, (2016), 3928–3937.
    [143] S. Das, A. Mukhopadhyay, A. Roy, et al., Exploratory power of the harmony search algorithm: analysis and improvements for global numerical optimization, IEEE T. Syst. Man. Cy. B., 41 (2011), 89–106.
    [144] L. Q. Gao, S. Li, X. Kong, et al., On the iterative convergence of harmony search algorithm and a proposed modification, Appl. Math. Comput., 247 (2014), 1064–1095.
    [145] T. G. Dietterich, Ensemble methods in machine learning, in International workshop on multiple classifier systems, Springer, 2000, 1–15.
    [146] S. Mahdavi, M. E. Shiri and S. Rahnamayan, Metaheuristics in large-scale global continues optimization: A survey, Inform. Sci., 295 (2015), 407–428.
    [147] G. Karafotias, M. Hoogendoorn and Á . E. Eiben, Parameter control in evolutionary algorithms: Trends and challenges, IEEE Trans. Evol. Comput., 19 (2015), 167–187.
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