A comparative study of the improvement of performance using a PSO modified by ACO applied to TSP |
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| Affiliation: | 1. REGIM-lab: Research Groups on Intelligent Machines, University of Sfax, National Engineering School of Sfax (ENIS), BP 1173, Sfax 3038, Tunisia;2. Technical Trainers College (TTC), German International Cooperation (GIZ), P.O. Box 2730, Riyadh 11461, Saudi Arabia;3. Machine Intelligence Research Labs (MIR Labs), P.O. Box 2259, Auburn, WA 98071-2259, USA;1. Dept of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwavidyapeetham,Bengaluru ,India;1. TED University, Computer Engineering Department, Ankara, Turkey;2. University of THK, Computer Engineering Department, Ankara, Turkey;1. College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, Hunan, China;2. Institude of Astronautical Systems Engineering, Beijing, China |
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| Abstract: | Swarm-inspired optimization has become very popular in recent years. Particle swarm optimization (PSO) and Ant colony optimization (ACO) algorithms have attracted the interest of researchers due to their simplicity, effectiveness and efficiency in solving complex optimization problems. Both ACO and PSO were successfully applied for solving the traveling salesman problem (TSP). Performance of the conventional PSO algorithm for small problems with moderate dimensions and search space is very satisfactory. As the search, space gets more complex, conventional approaches tend to offer poor solutions. This paper presents a novel approach by introducing a PSO, which is modified by the ACO algorithm to improve the performance. The new hybrid method (PSO–ACO) is validated using the TSP benchmarks and the empirical results considering the completion time and the best length, illustrate that the proposed method is efficient. |
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| Keywords: | Swarm intelligence Ant colony optimization Particle swarm optimization Traveling salesman problem Multi-objective optimization |
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