Human memory optimization algorithm: A memory-inspired optimizer for global optimization problems

Expert Systems with Applications, Год журнала: 2023, Номер 237, С. 121597 - 121597

Опубликована: Сен. 16, 2023

Язык: Английский

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Improved sparrow search algorithm with adaptive multi-strategy hierarchical mechanism for global optimization and engineering problems

Cluster Computing, Год журнала: 2025, Номер 28(3)

Опубликована: Янв. 28, 2025

Язык: Английский

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Manta ray foraging optimization based on mechanics game and progressive learning for multiple optimization problems

Applied Soft Computing, Год журнала: 2023, Номер 145, С. 110561 - 110561

Опубликована: Июнь 21, 2023

Язык: Английский

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An improved sparrow search algorithm with multi-strategy integration

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Янв. 26, 2025

Addressing the shortcomings of Sparrow Search Algorithm (SSA), such as low accuracy convergence and tendency falling into local optimum, a Multi-strategy Integrated (MISSA) is proposed. In this method, by improving black-winged kite algorithm applying it to producer's position update formula, an improved search strategy (ISS) firstly proposed enhance ability. Secondly, new inspired Coot algorithm, called group follow (GFS), improve ability jump out optimum. Finally, random opposition-based learning (ROBLS) applied population after each iteration its diversity. To verify MISSA's effectiveness, extensive testing conducted on 24 benchmark functions well CEC 2017 functions. The experimental results, complemented Wilcoxon rank-sum tests, conclusively demonstrate that MISSA outperforms SSA other advanced optimization algorithms, exhibiting superior overall performance.

Язык: Английский

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A step gravitational search algorithm for function optimization and STTM’s synchronous feature selection-parameter optimization

Artificial Intelligence Review, Год журнала: 2025, Номер 58(6)

Опубликована: Март 21, 2025

Язык: Английский

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Particle Swarm Optimization Algorithm Using Velocity Pausing and Adaptive Strategy

Symmetry, Год журнала: 2024, Номер 16(6), С. 661 - 661

Опубликована: Май 27, 2024

Particle swarm optimization (PSO) as a intelligence-based algorithm has been widely applied to solve various real-world problems. However, traditional PSO algorithms encounter issues such premature convergence and an imbalance between global exploration local exploitation capabilities when dealing with complex tasks. To address these shortcomings, enhanced incorporating velocity pausing adaptive strategies is proposed. By leveraging the search characteristics of terminal replacement mechanism, problem inherent in standard mitigated. The further refines controls space particle through time-varying inertia coefficients, symmetric cooperative swarms concepts, strategies, balancing exploitation. performance VASPSO was validated on 29 functions from Cec2017, comparing it against five variants seven intelligence algorithms. Experimental results demonstrate that exhibits considerable competitiveness compared 12 relevant code can be found our project homepage.

Язык: Английский

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Probabilistic optimal planning of multiple photovoltaics and battery energy storage systems in distribution networks: A boosted equilibrium optimizer with time-variant load models

Journal of Energy Storage, Год журнала: 2023, Номер 73, С. 108986 - 108986

Опубликована: Сен. 18, 2023

Язык: Английский

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An Improved Heterogeneous Comprehensive Learning Symbiotic Organism Search for Optimization Problems

Knowledge-Based Systems, Год журнала: 2023, Номер 285, С. 111351 - 111351

Опубликована: Дек. 28, 2023

Язык: Английский

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A novel hippo swarm optimization: for solving high-dimensional problems and engineering design problems

Journal of Computational Design and Engineering, Год журнала: 2024, Номер 11(3), С. 12 - 42

Опубликована: Апрель 10, 2024

Abstract In recent years, scholars have developed and enhanced optimization algorithms to tackle high-dimensional engineering challenges. The primary challenge of lies in striking a balance between exploring wide search space focusing on specific regions. Meanwhile, design problems are intricate come with various constraints. This research introduces novel approach called Hippo Swarm Optimization (HSO), inspired by the behavior hippos, designed address real-world HSO encompasses four distinct strategies based hippos different scenarios: starvation search, alpha margination, competition. To assess effectiveness HSO, we conducted experiments using CEC2017 test set, featuring highest dimensional problems, CEC2022 constrained problems. parallel, employed 14 established as control group. experimental outcomes reveal that outperforms well-known algorithms, achieving first average ranking out them CEC2022. Across classical consistently delivers best results. These results substantiate highly effective algorithm for both

Язык: Английский

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Solving non-linear fixed-charge transportation problems using nature inspired non-linear particle swarm optimization algorithm

Applied Soft Computing, Год журнала: 2023, Номер 146, С. 110699 - 110699

Опубликована: Авг. 2, 2023

Язык: Английский

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