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This research aims to enhance the optimization performance of the differential evolution algorithm.

A novel adaptive differential evolution algorithm with local optimum region marking strategy

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Novel adaptive differential evolution algorithm improves optimization performance, suggesting it efficiently solves complex problems.

This research aims to enhance the optimization performance of the differential evolution algorithm.
Introduced an adaptive differential evolution algorithm with local optimum region marking strategy (MDE).
Implemented improvement strategies including a chaotic sequence simulation and historical information adjustment.
Improved mutation operator DE/current-to-pbest/1 using inverse roulette wheel selection.
MDE demonstrates competitive optimization performance on continuous optimization problems compared to five advanced DE variants.
MDE is effective in solving combinatorial optimization problems like the traveling salesman problem, indicating significant potential.

Liu et al. (2025) studied this question.

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