Two Phased Cellular PSO: A New Collaborative Cellular Algorithm for Optimization in Dynamic Environments

Many real world optimization problems are dynamic in which the fitness landscape is time dependent and the optima change over time such as dynamic economic modeling, dynamic resource scheduling, and dynamic vehicle routing. Such problems challenge traditional optimization methods as well as conventional evolutionary optimization algorithms. For such environments, optimization algorithms not only have to find the global optimum but also closely track its trajectory. In this paper, we propose a collaborative version of cellular PSO, named Two Phased cellular PSO to address dynamic optimization problems. The proposed algorithm introduces two search phases in order to create a more efficient balance between exploration and exploitation in cellular PSO. The conventional PSO in cellular PSO is replaced by a proposed PSO to increase the exploration capability and an exploitation phase is added to increase exploitation is the promising cells. Moreover, the cell capacity threshold which is a key parameter of cellular PSO is eliminated due to these modifications. To demonstrate the performance and robustness of the proposed algorithm, it is evaluated in various dynamic environment modeled by Moving Peaks Benchmark. The results show that for all the experimented dynamic environments, TP-CPSO outperforms all compared algorithms including cellular PSO.