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Abstract
Metaheuristic algorithms are often applied to global function optimization problems. To overcome the poor real-time performance and low precision of the basic salp swarm algorithm, this paper introduces a novel hybrid algorithm inspired by the perturbation weight mechanism. The proposed perturbation weight salp swarm algorithm has the advantages of a broad search scope and a strong balance between exploration and exploitation and retains a relatively low computational complexity when dealing with numerous large-scale problems. A new coefficient factor is introduced to the basic salp swarm algorithm, and new update strategies for the leader position and the followers are introduced in the search phase. The new leader position updating strategy has a specific bounded scope and strong search performance, thus accelerating the iteration process. The new follower updating strategy maintains the diversity of feasible solutions while reducing the computational load. This paper describes the application of the proposed algorithm to low-dimension and variable-dimension functions. This paper also presents iteration curves, box-plot charts, and search-path graphics to verify the accuracy of the proposed algorithm. The experimental results demonstrate that the perturbation weight salp swarm algorithm offers a better search speed and search balance than the basic salp swarm algorithm in different environments.
Highlights
In many engineering fields, there are numerous optimization problems that must be solved under complicated constraints, over large search domains and high complexities [1,2,3]
To overcome the above problems and enhance the performance of SSA, this paper describes the perturbation weight salp swarm algorithm (PWSSA)
Benchmark function testing is a popular and common way to indicate the performance of intelligent algorithms. is paper introduces benchmark functions to exhibit the superior performance of the proposed algorithm, and the proposed algorithm will be evaluated on classical benchmark functions
Summary
There are numerous optimization problems that must be solved under complicated constraints, over large search domains and high complexities [1,2,3]. Wu et al [50] proposed an improved salp swarm algorithm based on weight factor and adaptive mutation, and testing results showed the good convergence performance of escaping local optimum when compared with basic SSA. E leader salp searches for the best solution to the given problems using the difference between the lower searching bound and the upper searching bounds, which causes that the local optimum cannot be sufficiently utilized for the optimization procedure in basic SSA. To overcome the above problems and enhance the performance of SSA, this paper describes the perturbation weight salp swarm algorithm (PWSSA). E food source, which can be seen as the best solution in functions, is set to be present in the searching area and is targeted by the salp swarm chain. E food source, which can be seen as the best solution in functions, is set to be present in the searching area and is targeted by the salp swarm chain. e leader updates its position according to the food source position. e position of the leader can be represented as x1d
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