Padding the dimensions for knowledge transfer in the dynamic vector evaluated particle swarm optimisation algorithm

Mardé Helbig
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Abstract

Most real world problems have more than one objective, with at least two objectives in conflict with one another and at least one objective that is dynamic in nature. The dynamic vector evaluated particle swarm optimisation (DVEPSO) algorithm is a co-operative algorithm, where each sub-swarm solves only one objective function and therefore, each sub-swarm optimises only a sub-set of decision variables. Knowledge is shared amongst the sub-swarms when the particles' velocity is updated, by using the position of the global guide of the subswarm or of another sub-swarm. The global guide can only provide information about the decision variables that are applicable to the objective function that its sub-swarm is optimising. Therefore, padding is required for the other decision variables. This paper investigates various padding approaches, namely using the sub-swarm's global best, using the personal best (pbest) of another particle in the sub-swarm, using the global best (gbest) of another sub-swarm or performing parent-centric crossover on another particle's position, pbest and gbest. Results indicate that using a random gbest or pbest performed well in fast changing environments, and using the sub-swarm's gbest performed well in slowly changing environments.
在动态向量中填充知识传递的维度,评估粒子群优化算法
大多数现实世界的问题都有不止一个目标,至少有两个目标相互冲突,至少有一个目标本质上是动态的。动态向量评估粒子群优化算法(DVEPSO)是一种协作算法,其中每个子群只求解一个目标函数,因此每个子群只优化决策变量的子集。当粒子的速度更新时,通过使用子群或另一个子群的全局向导的位置,在子群之间共享知识。全局向导只能提供适用于其子群正在优化的目标函数的决策变量信息。因此,需要填充其他决策变量。本文研究了各种填充方法,即使用子群的全局最佳,使用子群中另一个粒子的个人最佳(pbest),使用另一个子群的全局最佳(gbest)或在另一个粒子的位置,pbest和gbest上进行以父母为中心的交叉。结果表明,在快速变化的环境中,使用随机gbest或pbest的效果较好,而在缓慢变化的环境中,使用子群的gbest效果较好。
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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