A differential evolutionary algorithm improvement framework based on artificial potential fields

Zhe Yang

doi:10.1016/j.asoc.2026.114714

A differential evolutionary algorithm improvement framework based on artificial potential fields

2026-01-24

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DOI：10.1016/j.asoc.2026.114714

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• Linked APF pathfinding principles to EA optimization processes. • Novel APF application enhances EA performance, targeting DE mutation. • Developed a mutation mechanism utilizing APF-derived attractive and repulsive forces. • Validated the framework’s effectiveness on varying real-world engineering problems.

A differential evolutionary algorithm improvement framework based on artificial potential fields

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