<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si19.svg"><mml:mrow><mml:mi>D</mml:mi><mml:mi>E</mml:mi><mml:mo linebreak="goodbreak">/</mml:mo><mml:mi>c</mml:mi><mml:mi>u</mml:mi><mml:mi>r</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mi>n</mml:mi><mml:mi>t</mml:mi><mml:mo linebreak="goodbreak">−</mml:mo><mml:mi>t</mml:mi><mml:mi>o</mml:mi><mml:mo linebreak="goodbreak">−</mml:mo><mml:mi>b</mml:mi><mml:mi>e</mml:mi><mml:mi>t</mml:mi><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi>

Zuo, Mingcheng; Guo, Chuangxin

doi:10.1016/j.iswa.2022.200063

Cited by 5 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The DE/current-to-pbest/1 strategy enhanced global exploration by using top-performing individuals, expedited local convergence by leveraging past exploration, and dynamically adjusted mutation parameters, resulting in improved performance when tackling complex optimization problems [51]. The good performance of the DE/current-to-pbest/1 strategy motivated many researchers to base their work on it [52]. However, the primary drawback of the DE/current-to-pbest/1 mutation operator was its tendency to limit the diversity within the population.…”

Section: Adaptive Mutation Strategymentioning

confidence: 99%

An Adaptive Differential Evolution with Multiple Crossover Strategies for Optimization Problems

Farda,

Thammano

2024

HighTech. Innov. J.

View full text Add to dashboard Cite

The efficiency of a Differential Evolution (DE) algorithm largely depends on the control parameters of the mutation strategy. However, fixed-value control parameters are not effective for all types of optimization problems. Furthermore, DE search capability is often restricted, leading to limited exploration and poor exploitation when relying on a single strategy. These limitations cause DE algorithms to potentially miss promising regions, converge slowly, and stagnate in local optima. To address these drawbacks, we proposed a new Adaptive Differential Evolution Algorithm with Multiple Crossover Strategy Scheme (ADEMCS). We introduced an adaptive mutation strategy that enabled DE to adapt to specific optimization problems. Additionally, we augmented DE with a powerful local search ability: a hunting coordination operator from the reptile search algorithm for faster convergence. To validate ADEMCS effectiveness, we ran extensive experiments using 32 benchmark functions from CEC2015 and CEC2016. Our new algorithm outperformed nine state-of-the-art DE variants in terms of solution quality. The integration of the adaptive mutation strategy and the hunting coordination operator significantly enhanced DE's global and local search capabilities. Overall, ADEMCS represented a promising approach for optimization, offering adaptability and improved performance over existing variants. Doi: 10.28991/HIJ-2024-05-02-02 Full Text: PDF

show abstract

Section: Adaptive Mutation Strategymentioning

confidence: 99%