Crowding-based local differential evolution with speciation-based memory archive for dynamic multimodal optimization
Souvik Kundu, Subhodip Biswas, Swagatam Das, Ponntuthurai N. Suganthan · 2013
In the real world, many problems are multimodal as well as dynamic. Such problems require optimizers which not only locate multiple optima in a single run but also track the changing optima positions in the dynamic environments. In this paper a niching parameter free algorithm is designed which can locate multiple optima in changing environments. The proposed algorithm integrates the crowding concept with a competent Evolutionary Algorithm (EA) called Differential Evolution (DE) for maintaining the multiple peaks in a single run. To avoid the use of niching parameter that requires prior knowledge about the fitness landscape, the authors have used local mutation for searching the solution space. A speciation-based memory archive is integrated for regeneration of population after an environmental change is detected. Experimental analysis is conducted on the Moving Peaks Benchmark problem and the performance of the proposed algorithm is compared with other peer algorithms to highlight the overall effectiveness of our work.