Improving solution characteristics of particle swarm optimization using digital pheromones

In this paper, a new approach to particle swarm optimization (PSO) using digital pheromones to coordinate swarms within an n–dimensional design space is presented. In a basic PSO, an initial randomly generated population swarm propagates toward the global optimum over a series of iterations. The direction of the swarm movement in the design space is based on an individual particle's best position in its history trail (pBest), and the best particle in the entire swarm (gBest). This information is used to generate a velocity vector indicating a search direction toward a promising location in the design space. The premise of the research presented in this paper is based on the fact that the search direction for each swarm member is dictated by only two candidates, pBest and gBest, which are not efficient to locate the global optimum, particularly in multi–modal optimization problems. In addition, poor move sets specified by pBest in the initial stages of optimization can trap the swarm in a local minimum or cause slow convergence. This paper presents the use of digital pheromones for aiding communication within the swarm to improve the search efficiency and reliability, resulting in improved solution quality, accuracy, and efficiency. With empirical proximity analysis, the pheromone strength in a region of the design space is determined. The swarm then reacts accordingly based on the probability that this region may contain an optimum. The additional information from pheromones causes the particles within the swarm to explore the design space thoroughly and locate the solution more efficiently and accurately than a basic PSO. This paper presents the development of this method and results from several multi–modal test cases.