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引用次数: 32

摘要

几何粒子群优化(GPSO)是传统粒子群优化(PSO)的一种新形式推广,它自然地适用于连续空间和组合空间。微分进化(DE)类似于粒子群算法,但它使用不同的方程来控制粒子的运动。本文将DE算法推广到组合搜索空间,将其几何解释扩展到这些空间,类似于传统的粒子群算法。利用几何微分进化(GDE)这一形式化算法,我们正式推导了与二进制字符串相关的Hamming空间的特定GDE,并给出了一个标准基准问题的实验结果。
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Geometric differential evolution
Geometric Particle Swarm Optimization (GPSO) is a recently introduced formal generalization of traditional Particle Swarm Optimization (PSO) that applies naturally to both continuous and combinatorial spaces. Differential Evolution (DE) is similar to PSO but it uses different equations governing the motion of the particles. This paper generalizes the DE algorithm to combinatorial search spaces extending its geometric interpretation to these spaces, analogously as what was done for the traditional PSO algorithm. Using this formal algorithm, Geometric Differential Evolution (GDE), we formally derive the specific GDE for the Hamming space associated with binary strings and present experimental results on a standard benchmark of problems.
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