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In most differential evolution (DE) algorithms, little work for the design of the mutation operator is directly relevant to the information of fitness landscape of the problem being solved. As the previous studies show, different mutation strategies are suitable for different problems with different fitness landscapes, and the performance of the mutation strategy is tightly linked to the fitness landscape...
With the help of the cooperative co-evolution, differential evolution (DE) has been applied successfully from low-dimensional problems to large scale optimization. In this paper, we propose a preferred learning cooperative coevolution DE algorithm (LDECC-DG) which focuses on the basic optimizer for large scale optimization using cooperative coevolution. The proposed LDECC-DG builds on the differential...
In most of the problems, finding global and local optima in multi-modal optimization problems are important. Standard Meta-heuristic algorithms are converging to an optimum, while the goal is finding all optima. (DE) is a well-known and powerful optimization. In this paper we proposed a novel method to consider solving the problems of multimodal in high dimensional by very good accuracy. Mechanism...
Differential evolution (DE) is an efficient and powerful population-based stochastic direct search method for solving optimization problems over continuous space. It uses both crossover and mutation for producing offspring. Mutation is rotation-invariant while crossover is not rotation-invariant. As a result, the performance of DE degrades in problems with strong linkage among variables. In this paper,...
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