A DE and PSO based hybrid algorithm for dynamic optimization problems

Many real world optimization problems are dynamic in which the fitness landscape is time dependent and the optima change over time. Such problems challenge traditional optimization algorithms. For such problems, optimization algorithms not only have to find the global optimum but also need to closely track its trajectory. In this paper, a new hybrid algorithm integrating a differential evolution (DE) and a particle swarm optimization (PSO) is proposed for dynamic optimization problems. Multi-population strategy is adopted to enhance the diversity and try to keep each subpopulation on a different peak in the fitness landscape. A hybrid operator combining DE and PSO is designed, in which each individual is sequentially carried out DE and PSO operations. An exclusion scheme is proposed that integrates the distance based exclusion scheme with the hill-valley function to track the adjacent peaks. The algorithm is applied to the set of benchmark functions used in CEC 2009 competition for dynamic environment. Experimental results show that it is more effective in terms of overall performance than other comparative algorithms.