An Optimistic Web Service Selection using Multi Colony – Particle Swarm Optimization (MC – PSO) algorithm

Evolutionary algorithm and Swarm Intelligence algorithm (EA, SI), a part of Bio inspired optimization algorithm, have been widely used to solve numerous optimization problem in various science and engineering domains. This paper proposes a Multi Swarm Particle Swarm Optimization (MS-PSO) algorithm inspired by the animal collective behavior, the movement of the swarm and the intelligence of the swarm. The main concept of MS-PSO is to extend the single population PSO to the interacting multiswarm model. Through this multi-swarm cooperative approach, diversity in the whole swarm community can be maintained. Simultaneously, the swarm-to-swarm mechanism, drastically speeds up the swarm community to converge to the global optimum.MS-PSO algorithm solves the premature convergence problem. MS-PSO algorithm is tested by various benchmark functions.MS-PSO algorithm has competitive performance to other algorithms like Genetic algorithm (GA), Particle Swarm Optimization algorithm (PSO) in terms of accuracy and convergence speed.MS-PSO algorithm is specially designed to solve NP-Hard problems such as Optimization problem, Decision problem, Search problem, etc... The MS-PSO algorithm is also applied to Web Service Selection Problem (WSS). The WSS is an NP-Hard problem. Keywords— Animal collective behavior, Optimization, Evolutionary algorithm, Swarm Intelligence, Benchmark