A modified ant system to achieve better balance between intensification and diversification for the traveling salesman problem |
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| Affiliation: | 1. School of Computer and Software Engineering, Xihua University, Chengdu 610039, China;2. School of Digital Media, Jiangnan University, Wuxi 214122, China;1. School of Information Science and Engineering, Central South University, Changsha, Hunan 410083, China;2. School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha, Hunan 410004, China;3. Collaborative Innovation Center of Resource-conserving & Environment-friendly Society and Ecological Civilization, Changsha, Hunan 410083, China;1. Mepco Schlenk Engineering College (Autonomous), Sivakasi, India;2. Department of IT, Mepco Schlenk Engineering College (Autonomous), Sivakasi, India;3. Department of CSE, Mepco Schlenk Engineering College (Autonomous), Sivakasi, India;4. Department of CSE, Ramco Institute of Technology, Rajapalayam, India;1. Programa de Pós-Graduação em Engenharia Elétrica, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6627, Belo Horizonte, MG, Brazil;2. Departamento de Engenharia Elétrica, Centro Federal de Educação Tecnológica de Minas Gerais, Av. Amazonas 7675, Belo Horizonte, MG, Brazil;3. Operations Research and Complex Systems Laboratory (ORCS Lab.), Departamento de Engenharia Elétrica, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6627, Belo Horizonte, MG, Brazil;1. Department of Information Engineering, I-Shou University, Kaohsiung 84001, Taiwan;2. Department of Electrical Engineering, I-Shou University, Kaohsiung 84001, Taiwan |
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| Abstract: | This paper presents a new variant of Ant Colony Optimization (ACO) for the Traveling Salesman Problem (TSP). ACO has been successfully used in many combinatorial optimization problems. However, ACO has a problem in reaching the global optimal solutions for TSPs, and the algorithmic performance of ACO tends to deteriorate significantly as the problem size increases. In the proposed modification, adaptive tour construction and pheromone updating strategies are embedded into the conventional Ant System (AS), to achieve better balance between intensification and diversification in the search process. The performance of the proposed algorithm is tested on randomly generated data and well-known existing data. The computational results indicate the proposed modification is effective and efficient for the TSP and competitive with Ant Colony System (ACS), Max-Min Ant System (MMAS), and Artificial Bee Colony (ABC) Meta-Heuristic. |
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| Keywords: | Ant Colony Optimization Adaptive tour construction Adaptive pheromone updating Ant System Traveling salesman problem |
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