Opposition based Particle Swarm Optimization with exploration and exploitation through gbest
Biplab Mandal, Tapas Si · 2015
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.