Improved heterogeneous particle swarm optimization
Volume 38, 2017 - Issue 3-4, Pages 481-499
Journal of Information and Optimization Sciences
In this paper, we propose an improved heterogeneous particle swarm optimization (IHPSO) with enhanced exploration and exploitation. Heterogeneous PSOs allow particles to have different position and velocity updates.
In the proposed IHPSO, auto regulation mechanism is proposed. The new positions update are based on the experience of the particles’ local best position and global best position, as well as the current fitness potential over the population fitness scale as an extra control parameter.
The employment of the proposed algorithm and other PSO variants with 25 benchmark test functions with complex structures from the literature, show the efficiency of the proposed method which outperforms all the presented PSO variants. The use of the proposed IHPSO to identify the parameters of a suspension system shows that the algorithm successfully determine the parameters and provide a good model to the system.
Particle swarm optimization, Exploration, Exploitation, Fitness potential