Grammatical Swarm Based-Adaptable Velocity Update Equations in Particle Swarm Optimizer

In this work, a new method for creating diversity in Particle Swarm Optimization is devised. The key feature of this method is to derive velocity update equation for each particle in Particle Swarm Optimizer using Grammatical Swarm algorithm. Grammatical Swarm is a Grammatical Evolution algorithm based on Particle Swarm Optimizer. Each particle updates its position by updating velocity. In classical Particle Swarm Optimizer, same velocity update equation for all particles is responsible for creating diversity in the population. Particle Swarm Optimizer has quick convergence but suffers from premature convergence in local optima due to lack in diversity. In the proposed method, different velocity update equations are evolved using Grammatical Swarm for each particles to create the diversity in the population. The proposed method is applied on 8 well-known benchmark unconstrained optimization problems and compared with Comprehensive Learning Particle Swarm Optimizer. Experimental results show that the proposed method performed better than Comprehensive Learning Particle Swarm Optimizer.