Advancing Truss Structure Optimization— A Multi-Objective Weighted Average Algorithm with Enhanced Convergence and Diversity

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104241 - 104241

Published: Feb. 1, 2025

Language: Английский

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Mirage search optimization: Application to path planning and engineering design problems

Advances in Engineering Software, Journal Year: 2025, Volume and Issue: 203, P. 103883 - 103883

Published: Feb. 18, 2025

Language: Английский

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Three stage based reinforcement learning for combining multiple metaheuristic algorithms

Swarm and Evolutionary Computation, Journal Year: 2025, Volume and Issue: 95, P. 101935 - 101935

Published: April 14, 2025

Language: Английский

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An Innovative Differentiated Creative Search Based on Collaborative Development and Population Evaluation

Biomimetics, Journal Year: 2025, Volume and Issue: 10(5), P. 260 - 260

Published: April 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.

Language: Английский

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Improved Cyclic System Based Optimization Algorithm (ICSBO)

Computers, materials & continua/Computers, materials & continua (Print), Journal Year: 2025, Volume and Issue: 0(0), P. 1 - 10

Published: Jan. 1, 2025

Cyclic-system-based optimization (CSBO) is an innovative metaheuristic algorithm (MHA) that draws inspiration from the workings of human blood circulatory system.However, CSBO still faces challenges in solving complex problems, including limited convergence speed and a propensity to get trapped local optima.To improve performance further, this paper proposes improved cyclic-system-based (ICSBO).First, venous circulation, adaptive parameter changes with evolution introduced balance between diversity stage enhance exploration search space.Second, simplex method strategy incorporated into systemic pulmonary circulations, which improves update formulas.A learning aimed at optimal individual, combined straightforward opposition-based approach, employed population while preserving diversity.Finally, novel external archive utilizing supplementation mechanism diversity, maximize use superior genes, lower risk being optima.Testing on CEC2017 benchmark set shows compared original eight other outstanding MHAs, ICSBO demonstrates remarkable advantages speed, precision, stability.

Language: Английский

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Multi-strategy enterprise development optimizer for numerical optimization and constrained problems

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 27, 2025

Abstract Enterprise Development Optimizer (EDO) is a meta-heuristic algorithm inspired by the enterprise development process with strong global search capability. However, analysis of EDO shows that it suffers from defects rapidly decreasing population diversity and weak exploitation ability when dealing complex optimization problems, while its algorithmic structure has room for further enhancement in process. In order to solve these challenges, this paper proposes multi-strategy optimizer called MSEDO based on basic EDO. A leader-based covariance learning strategy proposed, aiming strengthen quality agents alleviate later stage through guiding role dominant group modifying leader. To dynamically improve local capability algorithm, fitness distance-based leader selection proposed. addition, reconstructed diversity-based restart presented. The utilized assist jump out optimum stuck stagnation. Ablation experiments verify effectiveness strategies algorithm. performance confirmed comparing five different types improved metaheuristic algorithms. experimental results CEC2017 CEC2022 show effective escaping optimums favorable exploration capabilities. ten engineering constrained problems competently real-world problems.

Language: Английский

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A leaded sine–cosine artificial protozoa optimizer algorithm for solving multi-objective optimization problems

Cluster Computing, Journal Year: 2025, Volume and Issue: 28(5)

Published: April 28, 2025

Language: Английский

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Leader Selection Based Multi-Objective Flow Direction Algorithm (MOFDA): A novel approach for engineering design problems

Results in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 103670 - 103670

Published: Dec. 1, 2024

Language: Английский

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A Review of Enhancing Sine Cosine Algorithm: Common Approaches for Improved Metaheuristic Algorithms

Archives of Computational Methods in Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 24, 2024

Language: Английский

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