Abstract
The salp swarm algorithm (SSA) is a bio-heuristic optimization algorithm proposed in 2017. It has been proved that SSA has competitive results compared to several other well-known meta-heuristic algorithms on various optimization problem. However, like most meta-heuristic algorithms, SSA is prone to problems such as local optimal solution and a slow convergence rate. To solve these problems, a chaotic salp swarm algorithm based on opposition-based learning (OCSSA) is proposed. The application of opposition-based learning (OBL) guarantees a better convergence speed and better develops the search space. The chaotic local search (CLS) method is also introduced, which can improve the performance of the algorithm to obtain the global optimal solution. The performance of OCSSA is compared with that of the original SSA and some other meta-heuristic algorithms on 28 benchmark functions with unimodal or multimodal characteristics. The experimental results show that the performance of OCSSA, with an appropriate chaotic map, is better than or comparable with the SSA and other meta-heuristic algorithms.
Highlights
Meta-heuristic algorithms have become popular due to their advantages of simple and easy implementation, effective avoidance of local optimization, and good scalability
The opposition-based learning (OBL) is introduced in the proposed algorithm to approximate the closer candidate solution to the global optima and chaotic local search (CLS) is employed for the exploitation of promising search regions of search space
The simulation results show that OCSSA performs better than salp swarm algorithm (SSA) in optimizing 28 benchmark functions and maintains a fair balance between exploration and exploitation, which makes it robust
Summary
Meta-heuristic algorithms have become popular due to their advantages of simple and easy implementation, effective avoidance of local optimization, and good scalability. Many meta-heuristic algorithms have shown efficient and powerful performance in solving high-dimensional and nonlinear optimization problems [1]. A meta-heuristic algorithm can to some extent be divided into the two main phases of exploration and exploitation [2]. Algorithms conduct random expansion exploration on the whole search space to increase the diversity of solutions. The exploitation phase aims to improve the quality of the solution by performing local searches around promising areas that have been identified during the exploration phase. It is important to maintain a good balance
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