Intelligent Decision Making System for Solving Traveling Salesman Problems: MPSO
9 Pages Posted: 12 Jun 2019
Date Written: March 14, 2019
Abstract
Multi swarm Particle swarm optimization (MPSO) is a variant of Particle Swarm Optimization (PSO) where various sub-swarms involved instead of single swarm, thereby balancing the exploitation and exploration. The progression of gbest is achieved by passing the best fitness value obtained from the child swarm and further progression of gbest is achieved by using gbest of parent swarm. TSP is an NP hard problems and its objectives is to find the minimum distance for a given cities and the constraints is to visit all the cities exactly ones to reach the final destination. In this paper we proposed MPSO approach to solve combinatorial optimization problem using BVA techniques comprising (Region_bc(Rg), Replicate_bc(Rp) and Evade_bc(Ev)), the performance of the algorithm is evaluated in terms of computation time, optimal fitness, error rate, convergence rate, convergence diversity and average convergence diversity for particle 30, the proposed techniques outperformance well with minimum computation time for optimal fitness value and error rate. In future we plan to implement the proposed technique in cloud computing environment for job scheduling problems.
Keywords: Multi swarm Particle swarm optimization, Boundary value Analysis, Genetic Algorithm
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