Weighted Differential Evolution Algorithm with Implementation Examples
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In this paper, we present the complete source code implementation of a weighted differential evolution algorithm and demonstrate its effectiveness across diverse optimization problems. The algorithm features weighted mutation operations that dynamically adjust search strategies based on population diversity, enhancing convergence properties. Key implementation aspects include fitness-based weight assignment, adaptive crossover rate control, and elitism preservation mechanisms. We provide comprehensive discussions on algorithmic variants and improvement techniques, such as parameter adaptation methods and constraint-handling approaches. Through detailed code explanations and practical application case studies - including benchmark function optimization and engineering design problems - readers gain deeper understanding of this powerful optimization technique's implementation nuances and real-world applicability.
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