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Ensemble of Two New Mutation Strategies for History-Based Parameter Adaptation Differential Evolution

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The proposed ESTMDE improves solution quality in optimization problems compared to L-SHADE variants.
New mutation strategies DE/current-to-Aqmean/1 and DE/current-to-pqbest balance exploration and exploitation.
Experimental testing on CEC2017 benchmark functions demonstrates the algorithm's enhanced performance.
This research supports the development of more effective differential evolution algorithms in complex problem-solving.

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Proposed ensemble method improves solution quality in optimization problems, indicating advantages over L-SHADE variants.

Song-Chol et al. (2025) studied this question.

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