Title: An effective improved co-evolution ant colony optimisation algorithm with multi-strategies and its application
Authors: Wu Deng; Junjie Xu; Yingjie Song; Huimin Zhao
Addresses: College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China; Traction Power State Key Laboratory, Southwest Jiaotong University, Chengdu 610031, China ' College of Computer Science and Technology, Civil Aviation University of China, Tianjin 300300, China ' Co-innovation Center of Shandong Colleges and Universities: Future Intelligent Computing, Shandong Technology and Business University, Yantai 264005, China ' College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China; The State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China
Abstract: In this paper, an effective improved co-evolution ant colony optimisation (MSICEAO) algorithm is presented to solve complex optimisation problem. In the MSICEAO, the multi-population co-evolution strategy is used to divide initial population into several sub-populations to interchange and share information. The weighted initial pheromone distribution strategy is used to improve the efficiency and adjust the pheromone factor and distance factor. The elitist retention strategy is used to improve the solution quality. The adaptive dynamic update strategy for pheromone evaporation rate is used to balance the convergence speed and solution quality. The aggregation pheromone diffusion mechanism is used to enhance the cooperative effect and highlight the cooperative idea of swarm intelligence. In order to verify the effectiveness of the MSICEAO, the experiments have been carried out on eight TSPs and one actual gate allocation problem. The MSICEAO is compared with five state-of-the-art algorithms of TS, GA, PSO, ACO and PSACO. The experiment results demonstrate that the MSICEAO is significantly better than the compared methods.
Keywords: ant colony optimisation; ACO; multi-population co-evolution; elitist retention; pheromone control strategy; adaptive dynamic update; gate allocation.
International Journal of Bio-Inspired Computation, 2020 Vol.16 No.3, pp.158 - 170
Received: 14 Feb 2019
Accepted: 07 Feb 2020
Published online: 03 Nov 2020 *