Improved ant colony optimization algorithm by path crossover for optimal path planning

Joan Woo Lee, Jeong Jung Kim, Ju Jang Lee

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Scopus citations

Abstract

In this paper, an improved Ant Colony Optimization (ACO) algorithm is proposed to solve path planning problems. These problems are to find a collision-free and optimal path from a start point to a goal point in environment of known obstacles. There are many ACO algorithms for path planning. However, it take a lot of time to get the solution and it is not to easy to obtain the optimal path every time. It is also difficult to apply to the complex and big size maps. Therefore, we study to solve these problems using the ACO algorithm improved by the path crossover scheme. The path crossover scheme is two-point crossover paths found by ants. The best path is stored and is compared with new path every time. The path crossover scheme is used at this time. When the two parts compared and exchanged, the better part updates the best path. We also propose that the pheromone update rule is modified as compared with previous our paper.

Original languageEnglish
Title of host publicationProceedings - IEEE ISIE 2009, IEEE International Symposium on Industrial Electronics
Pages1996-2000
Number of pages5
DOIs
StatePublished - 2009
EventIEEE International Symposium on Industrial Electronics, IEEE ISIE 2009 - Seoul, Korea, Republic of
Duration: 5 Jul 20098 Jul 2009

Publication series

NameIEEE International Symposium on Industrial Electronics

Conference

ConferenceIEEE International Symposium on Industrial Electronics, IEEE ISIE 2009
Country/TerritoryKorea, Republic of
CitySeoul
Period5/07/098/07/09

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