Adaptive Differential Evolution with Locality based Crossover for Dynamic Optimization

Abstract

Real life problems which deal with time varying landscape dynamics often pose serious challenge to the mettle of researchers in the domain of Evolutionary Computation. Classified as Dynamic Optimization problems (DOPs), these deal with candidate solutions which vary their dominance over dynamic change instances. The challenge is to efficiently recapture the dominant solution or the global optimum in each varying landscape. Differential Evolution (DE) algorithm with modifications of adaptability have been widely used to deal with the complexities of a dynamic landscape, yet problems persist unless dedicated structuring is done to exclusively deal with DOPs. In Adaptive Differential Evolution with Locality based Crossover (ADE-LbX) the mutation operation has been entrusted to a locality based scheme that retains traits of Euclidean distance based closest individuals around a potential solution. Diversity maintenance is further enhanced by incorporation of local best crossover scheme that renders the landscape independent of direction and empowers the algorithm with an explorative ability. An even distribution of solutions in different regions of landscape calls for a solution retention technique that adapts this algorithm to dynamism by using its previous information in diverse search domains. To evaluate the performance of ADE-LbX, it has been tested over Dynamic Problem instance proposed as in CEC 09 and compared with State-of-the-arts. The algorithm enjoys superior performance in varied problem configurations of the problem.