Basic Ant Colony Optimization

摘要：

Recently, the application of Ant Colony Optimization is much wider and it is always the highlight of key algorithm. There are also many improvements about the ACO algorithm, such as: the improvements of algorithm in self-adaptive, the improvements of increasing the diversity of various group, the improvements of enhancing local search, combining with the global optimization algorithm, and combining with deterministic local optimization algorithm, etc.However, with the developments of the theory and technology in multicore computing, how to implement ACO efficiently and parallel in a multicore computing environment being a new challenge for all researchers. This paper will propose a new ACO algorithm based on the multicore computing environment. The ACO is the probability algorithm used for searching optimization paths. It was proposed by Marco Dorigo in his doctoral dissertation in 1992, and the idea was from the activities that ants explore ways when they are looking for food. ACO is a kind of simulated evolutionary algorithm, and it has many advantages based on the previous research. Point to the issue of the parameter optimized design in PID control, comparing the results of ACO design to the results of genetic algorithm design, the final results present that the ACO has the effectiveness and application value of a new simulated evolutionary optimization method1-3. Recently, the development of ACO has been researched and paid more attention by researchers in various fields4-8. There are also many improvements to the ACO algorithm, including: the improvements of algorithm in self-adaptive, the improvements of increasing the diversity of various group, the improvements of enhancing local search, combining with the global optimization algorithm, and combining with deterministic local optimization algorithm, etc. However, with the developments of the theory and technology in multicore computing, how to implement ACO efficiently and paralleled in a multicore computing environment being a new challenge for all researchers. This paper will propose a new ACO algorithm which is oriented the multicore computing based on the multicore computing environment.

关键词： component Ant Colony Optimization multy-core

作者: Ying Pei Wenbo Wang Song Zhang

作者单位: Ji Lin University Chang Chun, China

会议类型: 国际会议

会议名称: 2012 International Conference on Computer Science and Electronic Engineering(2012 IEEE计算机科学与电子工程国际会议 ICCSEE 2012)

会议地点: 杭州

会议语种:英文

页码: 665-667

在线出版日期: 2012-03-23（万方平台首次上网日期，不代表论文的发表时间）