Self regulating particle swarm optimization algorithm

In this paper, we propose a new particle swarm optimization algorithm incorporating the best human learning strategies for finding the optimum solution, referred to as a Self Regulating Particle Swarm Optimization (SRPSO) algorithm. Studies in human cognitive psychology have indicated that the best planners regulate their strategies with respect to the current state and their perception of the best experiences from others. Using these ideas, we propose two learning strategies for the PSO algorithm. The first one uses a self-regulating inertia weight and the second uses the self-perception on the global search direction. The self-regulating inertia weight is employed by the best particle for better exploration and the self-perception of the global search direction is employed by the rest of the particles for intelligent exploitation of the solution space. SRPSO algorithm has been evaluated using the 25 benchmark functions from CEC2005 and a real-world problem for a radar system design. The results have been compared with six state-of-the-art PSO variants like Bare Bones PSO (BBPSO), Comprehensive Learning PSO (CLPSO), etc. The two proposed learning strategies help SRPSO to achieve faster convergence and provide better solutions in most of the problems. Further, a statistical analysis on performance evaluation of the different algorithms on CEC2005 problems indicates that SRPSO is better than other algorithms with a 95% confidence level.