Opposition based Particle Swarm Optimization with exploration and exploitation through gbest

Particle Swarm Optimizer is a swarm intelligent algorithm which simulates the behaviour of bird´s flocking and fish schooling. This paper presents an improved opposition based Particle Swarm Optimizer. In the proposed method, generalized opposition based learning is incorporated first in population initialization and particle´s personal best position. Second, a controlled mechanism of exploration and exploitation is employed through global best position of the swarm. The proposed method is applied on 28 CEC2013 benchmark problems. A comparative study is made with standard Particle Swarm Optimizer and its other opposition based variants. The experimental results show that the proposed method statistically outperforms other methods.