Implementing Particle Swarm Optimization with Aging Leader and Challengers – Applying Velocity Initialization Strategies

Abstract

Particle Swarm Optimization with Aging Leader and Challengers (ALC-PSO) is an optimization technique which uses the concept of aging. Aging is a vital process that comes to all. This mechanism is applied to the Particle Swarm Optimization Algorithm, to find the optimal solution to a difficult problem. The ALC-PSO algorithm uses the concept of a leader, leading the swarm and another particle challenging the position of the leader, based on its efficiency, performance, lifespan and leading power. When Aging mechanism is applied to PSO, the premature convergence is overcome and the efficiency of the algorithm is increased. Whenever during the search process, any particle tends to leave the boundaries of the search space, much effort is wasted in searching for the best solution if the particle which could find best solution, has gone out of the search space. In such a situation, it becomes essential to re-initialize the particle’s velocity, to make it come back into the search space, so that the optimal solution be found efficiently and in lesser time. There are mainly three velocity update strategies which can be used in the algorithm for its better performance. These include: Velocity initialization to zero, velocity initialization within a specified domain, velocity initialization to a random value near zero. This paper presents the impact of applying various velocity initialization strategies on the ALC-PSO Algorithm.