Design and application of improved sparrow search algorithm based on sine cosine and firefly perturbation

Xiangyang Ren; Shuai Chen; Kunyuan Wang; Juan Tan; Xiangyang Ren; Shuai Chen; Kunyuan Wang; Juan Tan

doi:10.3934/mbe.2022533

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 11: 11422-11452. doi: 10.3934/mbe.2022533

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Research article Special Issues

Design and application of improved sparrow search algorithm based on sine cosine and firefly perturbation

1.
School of Management Engineering and Business, Hebei University of Engineering, Handan 056038, China
2.
School of Marxism, Hebei University of Engineering, Handan 056038, China

Academic Editor: Jeffrey Yi-Lin Forrest

Received: 18 April 2022 Revised: 03 August 2022 Accepted: 04 August 2022 Published: 10 August 2022

Swarm intelligence algorithms are relatively simple and highly applicable algorithms, especially for solving optimization problems with high reentrancy, high stochasticity, large scale, multi-objective and multi-constraint characteristics. The sparrow search algorithm (SSA) is a kind of swarm intelligence algorithm with strong search capability, but SSA has the drawback of easily falling into local optimum in the iterative process. Therefore, a sine cosine and firefly perturbed sparrow search algorithm (SFSSA) is proposed for addressing this deficiency. Firstly, the Tent chaos mapping is invoked in the initialization population stage to improve the population diversity; secondly, the positive cosine algorithm incorporating random inertia weights is introduced in the discoverer position update, so as to improve the probability of the algorithm jumping out of the local optimum and speed up the convergence; finally, the firefly perturbation is used to firefly perturb the sparrows, and all sparrows are updated with the optimal sparrows using the firefly perturbation method to improve their search-ability. Thirteen benchmark test functions were chosen to evaluate SFSSA, and the results were compared to those computed by existing swarm intelligence algorithms, as well as the proposed method was submitted to the Wilcoxon rank sum test. Furthermore, the aforesaid methods were evaluated in the CEC 2017 test functions to further validate the optimization efficiency of the algorithm when the optimal solution is not zero. The findings show that SFSSA is more favorable in terms of algorithm performance, and the method's searchability is boosted. Finally, the suggested algorithm is used to the locating problem of emergency material distribution centers to further validate the feasibility and efficacy of SFSSA.
- sparrow search algorithm,
- Tent chaotic mapping,
- positive cosine algorithm,
- firefly perturbation strategy,
- contingency site selection
Citation: Xiangyang Ren, Shuai Chen, Kunyuan Wang, Juan Tan. Design and application of improved sparrow search algorithm based on sine cosine and firefly perturbation[J]. Mathematical Biosciences and Engineering, 2022, 19(11): 11422-11452. doi: 10.3934/mbe.2022533

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Abstract

Swarm intelligence algorithms are relatively simple and highly applicable algorithms, especially for solving optimization problems with high reentrancy, high stochasticity, large scale, multi-objective and multi-constraint characteristics. The sparrow search algorithm (SSA) is a kind of swarm intelligence algorithm with strong search capability, but SSA has the drawback of easily falling into local optimum in the iterative process. Therefore, a sine cosine and firefly perturbed sparrow search algorithm (SFSSA) is proposed for addressing this deficiency. Firstly, the Tent chaos mapping is invoked in the initialization population stage to improve the population diversity; secondly, the positive cosine algorithm incorporating random inertia weights is introduced in the discoverer position update, so as to improve the probability of the algorithm jumping out of the local optimum and speed up the convergence; finally, the firefly perturbation is used to firefly perturb the sparrows, and all sparrows are updated with the optimal sparrows using the firefly perturbation method to improve their search-ability. Thirteen benchmark test functions were chosen to evaluate SFSSA, and the results were compared to those computed by existing swarm intelligence algorithms, as well as the proposed method was submitted to the Wilcoxon rank sum test. Furthermore, the aforesaid methods were evaluated in the CEC 2017 test functions to further validate the optimization efficiency of the algorithm when the optimal solution is not zero. The findings show that SFSSA is more favorable in terms of algorithm performance, and the method's searchability is boosted. Finally, the suggested algorithm is used to the locating problem of emergency material distribution centers to further validate the feasibility and efficacy of SFSSA.

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