An Adaptive Differential Evolution Algorithm Based on Fuzzy Modeling The appropriate parameter setting can substantially determine the performance of differential evolution (DE), so parameter design is a very crucial and challenging task in DE. In response to the realistic demands, a novel adjust strategy for adaptive parameter is developed for DE in this paper. By way of the strategy of fuzzy modeling, the phases of optimization are designed as follows, i.e., exploration, exploitation and convergence. The adaptive adjust of F and CR, the control parameters, is determined by the phases of optimization. Meanwhile, an auxiliary movement technique is designed for the convergence population. This technique will help the best individual to avoid the risk of falling into the potential local optima. The proposed algorithm, namely FMDErand1, has been assessed under eight unimodal and multimodal benchmark functions. Results from experiments illustrate that the proposed FMDErand1 is a promising optimization algorithm which will greatly enhance the performance on effectiveness and dynamic.

Differential Evolution Using Linkage Identification Optimization problems become more difficult as the number of decision variables increases. A problem, however, can be divided into multiple independent subproblems in many cases. Therefore, it is possible to efficiently search for the optimal solution or a nearoptimal one by performing optimization for each subproblem. In genetic algorithms (GAs), the relationship between two decision variables is called a linkage, and checking whether there is the linkage is called linkage identification. Identifying linkages enables GAs to perform the optimization for each subproblem. Such GAs use a crossover that does not damage the linkage. However, differential evolution (DE) has better search performance than GAs. Moreover, DE has a procedure similar to the crossover of GA. In this paper, we propose DE using the linkage identification. The effectiveness of the proposed method is evaluated through numerical experiments of comparing with DE not using the linkage identification and GA using the linkage identification.