Algorithm for Solving Constrained Optimization Problems
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Resource Overview
A hybrid algorithm combining differential evolution, genetic algorithm, and particle swarm optimization for constrained optimization problems. This implementation successfully obtains optimal solutions for all 13 standard test functions from reference [7] (T.P. Runarsson and X. Yao, "Stochastic ranking for constrained evolutionary optimization," IEEE Trans. Evol. Comput., vol. 4, no. 3, pp. 284-294, Sep. 2000). The algorithm features constraint handling through stochastic ranking and adaptive parameter tuning. For technical inquiries, please visit http://2shi.phphubei.com
Detailed Documentation
This text presents a hybrid approach combining differential evolution, genetic algorithm, and particle swarm optimization to solve constrained optimization problems. The implementation includes specialized constraint-handling mechanisms and population management strategies. According to reference [7] (T.P. Runarsson and X. Yao, "Stochastic ranking for constrained evolutionary optimization," IEEE Trans. Evol. Comput., vol. 4, no. 3, pp. 284-294, Sep. 2000), we validated our algorithm on 13 standard test functions and successfully obtained optimal solutions for all problems. The code incorporates adaptive mutation rates, crossover operations, and fitness evaluation with penalty functions. If you have any technical questions about the implementation details or algorithm parameters, please post your inquiries at http://2shi.phphubei.com.cn/index.php where I will provide comprehensive explanations.
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