A multi-strategy upgraded Harris Hawk optimization algorithm for solving nonlinear inequality constrained optimization problems

Juhe Sun; Guolin Huang; Li Wang; Chuanjun Yin; Ning Ma; Juhe Sun; Guolin Huang; Li Wang; Chuanjun Yin; Ning Ma

doi:10.3934/math.2025533

AIMS Mathematics

2025, Volume 10, Issue 5: 11783-11812. doi: 10.3934/math.2025533

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Research article

A multi-strategy upgraded Harris Hawk optimization algorithm for solving nonlinear inequality constrained optimization problems

1.
School of Science, Shenyang Aerospace University, Shenyang 110136, China
2.
School of Mechatronics Engineering, Shenyang Aerospace University, Shenyang 110136, China

Received: 08 April 2025 Revised: 09 May 2025 Accepted: 16 May 2025 Published: 22 May 2025
MSC : 68W50, 90C30

This study presented an upgraded version of the Harris Hawk optimization algorithm (UHHO) designed to overcome the inherent limitations of the original algorithm, especially in solving nonlinear constrained optimization problems that tend to converge prematurely and fall into local optima. First, the initial population generated in a random way was replaced by a good point set strategy. Second, we replaced the linear strategy with a nonlinear strategy in the intermediate stage in order to optimize the global search process. Furthermore, the sine-cosine strategy and L-C cascade chaos strategy were introduced in the development stage to perturb the population's position. This aimed to better explore the neighborhood of Harris Hawk optimal individuals in depth, enhance the local search ability of the algorithm, and avoid the algorithm falling into local optima. Some numerical experiments for solving nonlinear inequality constrained optimization problems are presented at the end of this paper. The simulation results show that the multi-strategy upgraded Harris Hawk algorithm can effectively avoid the problem of the standard Harris Hawk optimization algorithm falling into local optima.
- multi-strategy upgrade,
- local optimality,
- good point set strategy,
- nonlinear factor strategy,
- sine-cosine strategy,
- L-C chaos strategy,
- nonlinear constrained optimization problems
Citation: Juhe Sun, Guolin Huang, Li Wang, Chuanjun Yin, Ning Ma. A multi-strategy upgraded Harris Hawk optimization algorithm for solving nonlinear inequality constrained optimization problems[J]. AIMS Mathematics, 2025, 10(5): 11783-11812. doi: 10.3934/math.2025533

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Abstract

This study presented an upgraded version of the Harris Hawk optimization algorithm (UHHO) designed to overcome the inherent limitations of the original algorithm, especially in solving nonlinear constrained optimization problems that tend to converge prematurely and fall into local optima. First, the initial population generated in a random way was replaced by a good point set strategy. Second, we replaced the linear strategy with a nonlinear strategy in the intermediate stage in order to optimize the global search process. Furthermore, the sine-cosine strategy and L-C cascade chaos strategy were introduced in the development stage to perturb the population's position. This aimed to better explore the neighborhood of Harris Hawk optimal individuals in depth, enhance the local search ability of the algorithm, and avoid the algorithm falling into local optima. Some numerical experiments for solving nonlinear inequality constrained optimization problems are presented at the end of this paper. The simulation results show that the multi-strategy upgraded Harris Hawk algorithm can effectively avoid the problem of the standard Harris Hawk optimization algorithm falling into local optima.

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