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An adaptive differential evolution algorithm based on probability-driven multiple mutation strategies and dynamic seed restart

delete2026-08-11
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PRE
AI
C
Chengao Yang
W
Weijun Li
DOI：10.1016/j.asoc.2026.116188delete
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Abstract

Abstract

En 中文
• Fixed mutation strategies limit search balance and convergence performance. • Probability-driven multi-mutation improves adaptive search behavior. • A success-failure dual archive mitigates premature convergence. • Dynamic seed restart enhances population diversity.

Journal

Applied Soft Computing cover
Applied Soft Computing
IF:
6.6
Papers:
1.4W
Citations:
4.8W

Organization

X
Xiamen University of Technology
Scholars:
3.5K
Papers: 2.4K
Citations: 5.1K
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fujian police college
Scholars:
127
Papers: 95
Citations: 1
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