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提出一种克隆多尺度协同开采的离散微粒群算法.多尺度变异概率根据粒子适应值大小进行动态调节,在算法初期通过大尺度概率变异增加算法多样性,后期通过逐渐减小的小尺度变异提高算法在最优解附近的局部精确解搜索性能,对当前最优解进行克隆选择,可进一步增强算法逃出局部极小解的能力以及所求解的精度.将算法应用于5个benchmark函数优化问题并与其他算法比较,结果表明该算法不仅能增强全局解搜索性能,同时最优解的精度也有所提高.
This paper proposes a discrete particle swarm optimization algorithm for multi-scale cooperative mining of clones. The probability of multi-scale mutation is dynamically adjusted according to the particle adaptation value. The diversity of algorithms is increased by large-scale probability mutation in the early stage of the algorithm, and gradually increased by small- The local exact search performance of the algorithm in the vicinity of the optimal solution and the clonal selection of the current optimal solution can further enhance the ability of the algorithm to escape from the local minimum solution and the accuracy of the solution.The algorithm is applied to five benchmark function optimization problems Compared with other algorithms, the results show that this algorithm not only enhances the performance of global solution, but also improves the accuracy of the optimal solution.