集合(抽象数据类型)
群体行为
多群优化
排名(信息检索)
计算机科学
灵敏度(控制系统)
帕累托原理
元启发式
多目标优化
功能(生物学)
粒子群优化
数学优化
工程类
数据挖掘
人工智能
数学
算法
进化生物学
电子工程
生物
程序设计语言
作者
Tapabrata Ray,Pankaj Saini
标识
DOI:10.1080/03052150108940941
摘要
Abstract In this paper a new swarm algorithm for single objective design optimization problems is presented. A swarm is a collection of individuals having a common goal to reach the best value (minimum or maximum) of a function. Among the individuals in a swarm, there are some better performers (leaders) who set the direction of search for the rest of the individuals. An individual that is not in the better performer list (BPL) improves its performance by deriving information from its closest neighbour in the BPL. In an unconstrained problem, the objective values are used to generate the BPL while a multilevel Pareto ranking scheme is implemented to generate the BPL for constrained problems. The information sharing strategy also ensures that all the individuals in the swarm are unique as in a real swarm, where at a given time instant two individuals cannot share the same location. The uniqueness among the individuals result in a set of near optimal individuals at the final stage that is useful for sensitivity analysis. Three well-studied engineering design examples are solved to illustrate the benefits of the proposed swarm strategy Keywords: Pareto rankingConstrained optimizationSwarm strategy Additional informationNotes on contributorsTAPABRATA RAY Corresponding author. e-mail: mtray@ntu.edu.sg
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