Modified Sparrow Search Algorithm by Incorporating Multi-Strategy for Solving Mathematical Optimization Problems DOI Creative Commons
Yunpeng Ma,

Wang Meng,

Xiaolu Wang

и другие.

Biomimetics, Год журнала: 2025, Номер 10(5), С. 299 - 299

Опубликована: Май 8, 2025

The Sparrow Search Algorithm (SSA), proposed by Jiankai Xue in 2020, is a swarm intelligence optimization algorithm that has received extensive attention due to its powerful optimization-seeking ability and rapid convergence. However, similar other algorithms, the SSA problem of being prone falling into local optimal solutions during process, which limits application effectiveness. To overcome this limitation, paper proposes Modified (MSSA), enhances algorithm’s performance integrating three strategies. Specifically, Latin Hypercube Sampling (LHS) method employed achieve uniform distribution initial population, laying solid foundation for global search. An adaptive weighting mechanism introduced producer update phase dynamically adjust search step size, effectively reducing risk optima later iterations. Meanwhile, cat mapping perturbation Cauchy mutation operations are integrated further enhance exploration development efficiency, accelerating convergence process improving quality solutions. This study systematically validates MSSA through multi-dimensional experiments. demonstrates excellent on 23 benchmark test functions CEC2019 standard function set. Its practical engineering problems, namely design welded beams, reducers, cantilever successfully verifies effectiveness real-world scenarios. By comparing it with deterministic algorithms such as DIRET BIRMIN, based five-dimensional generated GKLS generator, thoroughly evaluated. In addition, successful robot path planning highlights advantages complex Experimental results show that, compared original SSA, achieved significant improvements terms speed, accuracy, robustness, providing new ideas methods research algorithms.

Язык: Английский

EPKO: Enhanced pied kingfisher optimizer for numerical optimization and engineering problems DOI

Benfeng Hu,

Xiaoliang Zheng, Wenhao Lai

и другие.

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 127416 - 127416

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

1
An Innovative Differentiated Creative Search Based on Collaborative Development and Population Evaluation DOI Creative Commons
Xinyu Cai, Chaoyong Zhang

Biomimetics, Год журнала: 2025, Номер 10(5), С. 260 - 260

Опубликована: Апрель 23, 2025

In real-world applications, many complex problems can be formulated as mathematical optimization challenges, and efficiently solving these is critical. Metaheuristic algorithms have proven highly effective in addressing a wide range of engineering issues. The differentiated creative search recently proposed evolution-based meta-heuristic algorithm with certain advantages. However, it also has limitations, including weakened population diversity, reduced efficiency, hindrance comprehensive exploration the solution space. To address shortcomings DCS algorithm, this paper proposes multi-strategy (MSDCS) based on collaborative development mechanism evaluation strategy. First, that organically integrates estimation distribution to compensate for algorithm’s insufficient ability its tendency fall into local optimums through guiding effect dominant populations, improve quality efficiency at same time. Secondly, new strategy realize coordinated transition between exploitation fitness distance. Finally, linear size reduction incorporated DCS, which significantly improves overall performance by maintaining large initial stage enhance capability extensive space, then gradually decreasing later capability. A series validations was conducted CEC2018 test set, experimental results were analyzed using Friedman Wilcoxon rank sum test. show superior MSDCS terms convergence speed, stability, global optimization. addition, successfully applied several constrained problems. all cases, outperforms basic fast strong robustness, emphasizing efficacy practical applications.

Язык: Английский

Процитировано

0
Modified Sparrow Search Algorithm by Incorporating Multi-Strategy for Solving Mathematical Optimization Problems DOI Creative Commons
Yunpeng Ma,

Wang Meng,

Xiaolu Wang

и другие.

Biomimetics, Год журнала: 2025, Номер 10(5), С. 299 - 299

Опубликована: Май 8, 2025

The Sparrow Search Algorithm (SSA), proposed by Jiankai Xue in 2020, is a swarm intelligence optimization algorithm that has received extensive attention due to its powerful optimization-seeking ability and rapid convergence. However, similar other algorithms, the SSA problem of being prone falling into local optimal solutions during process, which limits application effectiveness. To overcome this limitation, paper proposes Modified (MSSA), enhances algorithm’s performance integrating three strategies. Specifically, Latin Hypercube Sampling (LHS) method employed achieve uniform distribution initial population, laying solid foundation for global search. An adaptive weighting mechanism introduced producer update phase dynamically adjust search step size, effectively reducing risk optima later iterations. Meanwhile, cat mapping perturbation Cauchy mutation operations are integrated further enhance exploration development efficiency, accelerating convergence process improving quality solutions. This study systematically validates MSSA through multi-dimensional experiments. demonstrates excellent on 23 benchmark test functions CEC2019 standard function set. Its practical engineering problems, namely design welded beams, reducers, cantilever successfully verifies effectiveness real-world scenarios. By comparing it with deterministic algorithms such as DIRET BIRMIN, based five-dimensional generated GKLS generator, thoroughly evaluated. In addition, successful robot path planning highlights advantages complex Experimental results show that, compared original SSA, achieved significant improvements terms speed, accuracy, robustness, providing new ideas methods research algorithms.

Язык: Английский

Процитировано

0