期刊
INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION
卷 16, 期 3, 页码 158-170出版社
INDERSCIENCE ENTERPRISES LTD
DOI: 10.1504/IJBIC.2020.111267
关键词
ant colony optimisation; ACO; multi-population co-evolution; elitist retention; pheromone control strategy; adaptive dynamic update; gate allocation
资金
- National Natural Science Foundation of China [61771087, 51605068, 51475065]
- Open Project Program of State Key Laboratory of Mechanical Transmissions of Chongqing University [SKLMT-KFKT-201803]
- Traction Power State Key Laboratory of Southwest Jiaotong University [TPL2002]
- Open Subject Project of the Key Laboratory of Air Traffic Control Operation Planning and Safety Technology of CAUC [600001010932]
- Liaoning Provincial Natural Science Foundation Guidance Project [2019-ZD-0099, 20170540145]
- Science and Technology Project of Liaoning Provincial Department of Education [JDL2019025]
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.
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