Modified Particle Swarm Optimization with novel modulated inertia for velocity update

Particle swarm optimization (PSO) is a population-based stochastic search algorithm for searching the optimal regions from multidimensional space, inspired by the social behaviour of some animal species.However, it has its limitations such as being trapped into a local optima and having a slow rate of convergence.In this paper, a new method of creating a combination of a developed Accelerated PSO and a new modulated inertia coefficient for the velocity update has been proposed.Random term based on particle neighbourhood has been added in the position update formula, inspired by the Artificial Bee Colony (ABC) algorithm.To verify the proposed modified PSO, experiments were conducted on several benchmark optimization problems.The results show that the proposed algorithm is superior in comparison with standard PSO and accelerated PSO algorithms.