Journal of System Simulation
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.
Recommended Citation
Liu, Jingsen; Yuan, Mengmeng; and Yu, Li
(2021)
"Solving Engineering Optimization Design Problems Based on Improved Salp Swarm Algorithm,"
Journal of System Simulation: Vol. 33:
Iss.
4, Article 12.
DOI: 10.16182/j.issn1004731x.joss.19-0645
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol33/iss4/12
First Page
854
Revised Date
2020-02-20
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.19-0645
Last Page
866
CLC
TP301.6;TB21
Recommended Citation
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.
DOI
10.16182/j.issn1004731x.joss.19-0645
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