Solving Engineering Optimization Design Problems Based on Improved Salp Swarm Algorithm

doi:10.16182/j.issn1004731x.joss.19-0645

Abstract

Abstract: In order to better solve the engineering optimization design problem and improve the optimization performance of salp swarm algorithm, an adaptive dynamic role salp swarm algorithm with effective scaling and random crossover strategy is proposed. A pareto distribution and chaotic map are introduced into the leader position updating formula to make global search more efficient. In the selection of global and local search, a leader-follower adaptive adjustment strategy is introduced to improve the convergence accuracy. A random crossover strategy is introduced in local search to increase population diversity. The improved algorithm is applied to engineering optimization problems with different typical complexity. The test results show that its optimization results, problem adaptability and solution stability are better than other algorithms.

Key words: salp swarm algorithm, pareto distribution function, chaotic maps, random crossover strategy, adaptive adjustment strategy, engineering optimization design

CLC Number:

TP301.6
TB21

Liu Jingsen, Yuan Mengmeng, Li Yu. Solving Engineering Optimization Design Problems Based on Improved Salp Swarm Algorithm[J]. Journal of System Simulation, 2021, 33(4): 854-866.

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