caa a22 4500 605469377 CHVBK 20210128100322.0 cr unu---uuuuu 210128e20150301xx s 000 0 eng 10.1007/s00500-014-1294-9 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1294-9 An improved CACO algorithm based on adaptive method and multi-variant strategies [Elektronische Daten] [Wu Deng, Huimin Zhao, Jingjing Liu, Xiaolin Yan, Yuanyuan Li, Lifeng Yin, Chuanhua Ding] Chaotic ant colony optimization (CACO) algorithm is an effective optimization algorithm that simulates the self-organization and chaotic behavior of ants. However, in the research and application of the CACO algorithm for solving complex optimization problems, the CACO algorithm presents some disadvantages. In order to resolve these disadvantages, an improved CACO algorithm based on adaptive multi-variant strategies (CACOAMS) is proposed in this paper. The CACOAMS algorithm takes full advantage of multi-population strategy, the neighborhood comprehensive learning strategy, the fine search strategy, the chaotic optimization strategy, the super excellent ant strategy, the punishment strategy and min-max ant strategy in order to avoid the local optimization solution and stagnation, guarantee learning rate of the different dimensions for each ant and the diversity of the search, eliminate the self-locking trap between environmental boundary and obstacles, improve the search efficiency, search accuracy and robustness of the algorithm. In order to testify to the performance of the CACOAMS algorithm, the CACOAMS algorithm is applied to test the benchmark functions and dynamically adjust the values of PID parameters. The simulation results show that the CACOAMS algorithm takes on the strong flexibility, adaptability and robustness. It can effectively improve system control precision and guarantee feasibility and effectiveness. Springer-Verlag Berlin Heidelberg, 2014 Chaotic ant colony algorithm nationallicence Pheromone nationallicence Adaptive multi-variant strategies nationallicence PID control nationallicence Parameter optimization nationallicence Deng Wu Software Institute, Dalian Jiaotong University, 116028, Dalian, China aut Zhao Huimin Software Institute, Dalian Jiaotong University, 116028, Dalian, China aut Liu Jingjing Software Institute, Dalian Jiaotong University, 116028, Dalian, China aut Yan Xiaolin Software Institute, Dalian Jiaotong University, 116028, Dalian, China aut Li Yuanyuan Software Institute, Dalian Jiaotong University, 116028, Dalian, China aut Yin Lifeng Software Institute, Dalian Jiaotong University, 116028, Dalian, China aut Ding Chuanhua Software Institute, Dalian Jiaotong University, 116028, Dalian, China aut Soft Computing Springer Berlin Heidelberg 19/3(2015-03-01), 701-713 1432-7643 19:3<701 2015 19 500 https://doi.org/10.1007/s00500-014-1294-9 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1294-9 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Deng Wu Software Institute, Dalian Jiaotong University, 116028, Dalian, China aut NATIONALLICENCE 700 1- Zhao Huimin Software Institute, Dalian Jiaotong University, 116028, Dalian, China aut NATIONALLICENCE 700 1- Liu Jingjing Software Institute, Dalian Jiaotong University, 116028, Dalian, China aut NATIONALLICENCE 700 1- Yan Xiaolin Software Institute, Dalian Jiaotong University, 116028, Dalian, China aut NATIONALLICENCE 700 1- Li Yuanyuan Software Institute, Dalian Jiaotong University, 116028, Dalian, China aut NATIONALLICENCE 700 1- Yin Lifeng Software Institute, Dalian Jiaotong University, 116028, Dalian, China aut NATIONALLICENCE 700 1- Ding Chuanhua Software Institute, Dalian Jiaotong University, 116028, Dalian, China aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/3(2015-03-01), 701-713 1432-7643 19:3<701 2015 19 500