Cellular PSO: A PSO for Dynamic Environments

Many optimization problems in real world are dynamic in the sense that the global optimum value and the shape of fitness function may change with time. The task for the optimization algorithm in these environments is to find global optima quickly after the change in environment is detected. In this paper, we propose a new hybrid model of particle swarm optimization and cellular automata which addresses this issue. The main idea behind our approach is to utilized local interactions in cellular automata and split the population of particles into different groups across cells of cellular automata. Each group tries to find an optimum locally which results in finding the global optima. Experimental results show that cellular PSO outperforms mQSO, a well known PSO model in literature, both in accuracy and complexity in a dynamic environment where peaks change in width and height quickly or there are many peaks.