Differential evolution algorithm with adaptive crossover and sorting mutation strategies for high-dimensional optimization

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The DEASCSM algorithm effectively finds optimal solutions for up to 1000-dimensional problems, indicating its efficiency.
Performance evaluations show superior results compared to traditional optimization methods on various benchmark functions.
The algorithm adapts its search strategy based on problem landscapes, enhancing convergence rates in diverse scenarios.
Scalability analysis reveals differential performance characteristics depending on the specific problem features addressed.

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Adaptive algorithm demonstrates improved convergence and scalability in high-dimensional optimization tasks.

Wetweerapong et al. (2025) studied this question.

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