A novel effective particle swarm optimization like algorithm via extrapolation technique

A novel competitive approach to particle swarm optimization (PSO) algorithms is proposed in this paper. The proposed method uses extrapolation technique with PSO (ePSO) for solving optimization problems. By considering the basics of the PSO algorithm, the current particle position is updated by extrapolating the global best particle position and the current particle positions in the search space. The position of the particles in each iteration is updated directly without using the velocity equation. The position equation is formulated with the global best (gbest) position, personal or local best position (pbest) and the current position of the particle. The proposed method is tested with a set of five standard optimization bench mark problems and the results are compared with those obtained through three PSO algorithms, the canonical PSO (cPSO), the global-local best PSO (GLBest-PSO) and the proposed ePSO method. The cPSO includes a time varying inertia weight (TVIW) and time varying acceleration coefficients (TVAC) while the GLBest PSO consists of global-local best inertia weight (GLBest 1W) with global-local best acceleration coefficient (GLBestAC). The simulation results clearly elucidate that the proposed method produces the near global optimal solution. It is also observed from the comparison of the proposed method with cPSO and GLBest-PSO, the ePSO is capable of producing a quality of optimal solution with faster convergence rate. To strengthen the comparison and prove the efficacy of the proposed method, analysis of variance and hypothesis t-test are also carried out. All the results indicate that the proposed ePSO method is competitive to the existing PSO algorithms.