Opposition based initialization in particle swarm optimization (O-PSO)

Abstract

Particle Swarm Optimization, a population based optimization technique has been used in wide number of application areas to solve optimization problems. This paper presents a new algorithm for initialization of population in standard PSO called Opposition based Particle Swarm Optimization (O-PSO). The performance of proposed initialization algorithm is compared with the existing PSO variants on several benchmark functions and the experimental results reveal that O-PSO outperforms existing approaches to a large extent.

Energy Aware Swarm Optimization with Intercluster Search for Wireless Sensor Network

“…PSO uses the cognition model to perform the local search while it uses its social skills to perform global search. During each iteration, the next position of the particle is computed based on its cognition and social skills [ 35 ].…”

A time performance comparison of particle swarm optimization in mobile devices

“…Another variation consider the initialization of the particles as the most important element in order to improve the performance of the algorithm [17]. Opposition-based learning, a term that describes an individual's exact contrary, has also been considered as an enhanced variant of PSO which accelerates convergence [18][19][20] by replacing individuals which are far to the optimal solution by their opposite, which is closer in distance to the solution.…”

Opposition based computing — A survey

“…Jabeen et al [43] tested the effect of using opposition-based population initialization in more details. Four benchmark functions were used to test OP SO.…”