Many-Objective Particle Swarm Optimization by Gradual Leader Selection
Many-Objective Particle Swarm Optimization by Gradual Leader Selection
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DOI：
10.1007/978-3-540-71618-1_36
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发表时间：
2007-04
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影响因子：
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通讯作者：
M. Köppen;Kaori Yoshida
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文献类型：
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作者：
M. Köppen;Kaori Yoshida
多目标优化是指目标数量远远大于两个或三个的多目标优化问题。本文为粒子群优化（PSO）在多目标优化问题中的应用做出了贡献。多目标粒子群算法通常依赖于所谓的一组引导者，这些引导者将标准粒子群算法中使用的全局最佳粒子泛化。随着目标数量的增加，在搜索空间中发现非支配点的概率呈指数下降，这给从这组领导者中进行选择带来了问题，并使多目标pso容易失效。渐进式Pareto优势关系可以用来克服这一问题。该方法将通过最小化到若干点的欧几里德距离的问题来研究，其中每个点的距离被认为是一个独立的目标。该问题的Pareto集是点集的凸闭包。实验证明了所提出方法的有效性，并表明所提出的粒子群变异与标准粒子群具有较高的相似性。
Many-objective optimization refers to multi-objective optimization problems with a number of objectives considerably larger than two or three. This papers contributes to the use of Particle Swarm Optimization (PSO) for the handling of such many-objective optimization problems. Multi-objective PSO approaches typically rely on the employment of a so-called set of leaders that generalizes the global best particle used in the standard PSO algorithm. The exponentially decreasing probability of finding non-dominated points in search spaces with increasing number of objectives poses a problem for the selection from this set of leaders, and renders multi-objective PSOs easily unusable. Gradual Pareto dominance relation can be used to overcome this problem. The approach will be studied by means of the problem to minimize the Euclidian distances to a number of points, where each distance to the points is considered an independent objective. The Pareto set of this problem is the convex closure of the set of points. The conducted experiments demonstrate the usefulness of the proposed approach and also show the higher resemblance of the proposed PSO variation with the standard PSO.