TLMPA: Teaching-learning-based Marine Predators algorithm

Keyu Zhong; Qifang Luo; Yongquan Zhou; Ming Jiang; Keyu Zhong; Qifang Luo; Yongquan Zhou; Ming Jiang

doi:10.3934/math.2021087

AIMS Mathematics

2021, Volume 6, Issue 2: 1395-1442. doi: 10.3934/math.2021087

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Research article

TLMPA: Teaching-learning-based Marine Predators algorithm

1.
College of Artificial Intelligenc, Guangxi University for Nationalities, Nanning 530006, China
2.
School of Computer and Electronics and Information, Guangxi University, Nanning 530004, China
3.
Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis, Nanning 530006, China
4.
Guangxi Institute of Digital Technology, Nanning 530000, China

Received: 06 September 2020 Accepted: 21 October 2020 Published: 19 November 2020
MSC : 68W50, 68T20, 90C59

Marine Predators algorithm (MPA) is a newly proposed nature-inspired metaheuristic algorithm. The main inspiration of this algorithm is based on the extensive foraging strategies of marine organisms, namely Lévy movement and Brownian movement, both of which are based on random strategies. In this paper, we combine the marine predator algorithm with Teaching-learning-based optimization algorithm, and propose a hybrid algorithm called Teaching-learning-based Marine Predator algorithm (TLMPA). Teaching-learning-based optimization (TLBO) algorithm consists of two phases: the teacher phase and the learner phase. Combining these two phases with the original MPA enables the predators to obtain prey information for foraging by learning from teachers and interactive learning, thus greatly increasing the encounter rate between predators and prey. In addition, effective mutation and crossover strategies were added to increase the diversity of predators and effectively avoid premature convergence. For performance evaluation TLMPA algorithm, it has been applied to IEEE CEC-2017 benchmark functions and four engineering design problems. The experimental results show that among the proposed TLMPA algorithm has the best comprehensive performance and has more outstanding performance than other the state-of-the-art metaheuristic algorithms in terms of the performance measures.
- Marine Predators algorithm,
- Teaching-learning-based optimization,
- mutation and crossover,
- hybrid metaheuristic algorithm
Citation: Keyu Zhong, Qifang Luo, Yongquan Zhou, Ming Jiang. TLMPA: Teaching-learning-based Marine Predators algorithm[J]. AIMS Mathematics, 2021, 6(2): 1395-1442. doi: 10.3934/math.2021087

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Abstract

Marine Predators algorithm (MPA) is a newly proposed nature-inspired metaheuristic algorithm. The main inspiration of this algorithm is based on the extensive foraging strategies of marine organisms, namely Lévy movement and Brownian movement, both of which are based on random strategies. In this paper, we combine the marine predator algorithm with Teaching-learning-based optimization algorithm, and propose a hybrid algorithm called Teaching-learning-based Marine Predator algorithm (TLMPA). Teaching-learning-based optimization (TLBO) algorithm consists of two phases: the teacher phase and the learner phase. Combining these two phases with the original MPA enables the predators to obtain prey information for foraging by learning from teachers and interactive learning, thus greatly increasing the encounter rate between predators and prey. In addition, effective mutation and crossover strategies were added to increase the diversity of predators and effectively avoid premature convergence. For performance evaluation TLMPA algorithm, it has been applied to IEEE CEC-2017 benchmark functions and four engineering design problems. The experimental results show that among the proposed TLMPA algorithm has the best comprehensive performance and has more outstanding performance than other the state-of-the-art metaheuristic algorithms in terms of the performance measures.

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