Abstract
Heuristic search techniques are powerful tools to find an optimal solution for traveling salesman problem (TSP). Application of TSP is found in many areas such as semiconductor manufacturing, logistics, and transportation. This chapter focuses to develop a heuristic technique for TSP by combining two popular optimization methods “genetic algorithm (GA) and particle swarm optimization(PSO)”. In hybrid GA-PSO algorithm, new individuals are created through GA operators— crossover and mutation as well as mechanism of PSO. Hybrid GA-PSO algorithm performance was examined against GA and PSO for 10 standard TSPs with respect to find optimal and computational time. Computational results indicate that hybrid GA-PSO algorithm has significant advancement over GA and PSO for TSPs.
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Gupta, I.K., Shakil, S., Shakil, S. (2019). A Hybrid GA-PSO Algorithm to Solve Traveling Salesman Problem. In: Verma, N., Ghosh, A. (eds) Computational Intelligence: Theories, Applications and Future Directions - Volume I. Advances in Intelligent Systems and Computing, vol 798. Springer, Singapore. https://doi.org/10.1007/978-981-13-1132-1_35
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DOI: https://doi.org/10.1007/978-981-13-1132-1_35
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