Particle swarm optimization with spatially meaningful neighbours

James Lane, A. Engelbrecht, J. Gain
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引用次数: 52

Abstract

Neighbourhood topologies in particle swarm optimization (PSO) are typically random in terms of the spatial positions of connected neighbours. This study explores the use of spatially meaningful neighbours for PSO. An approach is designed which uses heuristics to leverage the natural neighbours computed with Delaunay triangulation. The approach is compared to standard PSO sociometries and fitness distance ratio approaches. Although intrinsic properties of Delaunay triangulation limit the practical application of this approach to low dimensions results show that it is a successful particle swarm optimizer.
具有空间有意义邻居的粒子群优化
粒子群优化(PSO)中的邻域拓扑在连接邻居的空间位置方面通常是随机的。本研究探讨了空间意义邻居在PSO中的应用。设计了一种利用启发式方法利用Delaunay三角剖分法计算的自然邻域的方法。将该方法与标准的PSO社会计量和适应度距离比方法进行了比较。虽然Delaunay三角剖分的固有性质限制了该方法在低维空间的实际应用,但结果表明它是一种成功的粒子群优化方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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