A fine tuning hybrid particle swarm optimization algorithm

Abstract

Particle swarm optimization (PSO) has shown itsgood performance in many optimization problems. This paper introduces a new approach called hybrid particle swarm optimization like algorithm (HPSO) with fine tuning operators to solve optimisation problems. This method combines the merits of the parameter-free PSO (pf-PSO) and the extrapolated particle swarm optimization like algorithm (ePSO). The performance of all the three PSO algorithms is considerably improved with various fine tuning operators and sometimes more competitive than the recently developed PSO algorithms. This paper is organised as follows: in Section 2, a brief review of cPSO and GLBest PSO algorithms is presented; in Section 3, the proposed hybrid PSO algorithm is described, discuss the design of the fme tuning elements such as mutation, cross-over; in Sections 4, the performance of the cPSO, GLBest PSO, pf-PSO, ePSO and HPSO algorithms with and without the fme tuning elements are analysed and compared for three difficult benchmark problems; fmally the conclusions are given in Section 5The TVIW which is developed is given in equation (3).The GLBest PSO method [8] in which, the inertia weight and acceleration coefficients are neither set to a constant value nor set as a linearly decreasing time varying function. Instead, they are defmed as a function of local best (pbest) and global best (gbest) values of the particles in each generation. The II. REVIEW OF cPSO AND GLBESTPSO ALGORITHMSThe cPSO algorithm considered in this paper comprises standard PSO algorithm[5][6] with TVIW and time varying acceleration coefficient (TVAC). The velocity and position equation for this method is given in equations (1) and (2) (3)(2)where WI and w 2 are the initial and fmal values of the inertia weight respectively, iter is the current iteration number and maxiter is the maximum number of allowable iterations. (4) and (5). Ratnaweera et al.[7] introduced a TVAC which is given in equations