2023
DOI: 10.1109/access.2023.3327732
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IMSCSO: An Intensified Sand Cat Swarm Optimization With Multi-Strategy for Solving Global and Engineering Optimization Problems

Xuewei Li,
Yonglan Qi,
Qian Xing
et al.

Abstract: Optimization challenges are becoming more complex as the world advances. Since deterministic and heuristic approaches are no longer sufficient to deal with such complex problems, metaheuristics have recently emerged as a viable option to address optimization difficulties. Since Sand Cat Swarm Optimization (SCSO) is a famous meta-heuristic algorithm, SCSO has a weak ability to balance search between exploration and exploitation and slow convergence, so it may not be effective in finding the global optima, parti… Show more

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Cited by 4 publications
(1 citation statement)
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“…In addition, the fusion immunization algorithm proposes a novel approach for updating locations in order to boost convergence accuracy, expedite the convergence process, avoid being trapped in local optimum solutions, and enhance the overall performance of the algorithm. Li et al [24] proposed the intensified sand cat swarm optimization algorithm with multi-strategy strengthening (IMSCSO). IMSCSO uses a dynamic stochastic search technique to enhance the algorithm's convergence speed.…”
Section: Introductionmentioning
confidence: 99%
“…In addition, the fusion immunization algorithm proposes a novel approach for updating locations in order to boost convergence accuracy, expedite the convergence process, avoid being trapped in local optimum solutions, and enhance the overall performance of the algorithm. Li et al [24] proposed the intensified sand cat swarm optimization algorithm with multi-strategy strengthening (IMSCSO). IMSCSO uses a dynamic stochastic search technique to enhance the algorithm's convergence speed.…”
Section: Introductionmentioning
confidence: 99%