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Differential Evolution (DE) algorithm has been shown to be powerful for many real optimization problems. In traditional DE and its variant algorithms, there are three or more candidates used in the step of mutation, which is one of the three typical operation steps: mutation, crossover and selection. To simplify the mutation, we propose a novel evolution algorithm which needs only two individuals...
In recent years, the Particle Swarm Optimization has rapidly gained increasing popularity and many variants and hybrid approaches have been proposed to improve it. Motivated by the behavior and the proximity characteristics of the social and cognitive experience of each particle in the swarm, we develop a hybrid approach that combines the Particle Swarm Optimization and the Differential Evolution...
Differential evolution is a well-known optimization technique to deal with nonlinear and complex problems. However, it suffers from some difficulties, such as expensive computation, problem-dependent parameters, etc. In order to tackle these problems, this paper presents a hybrid DE algorithm, called SAODE, by employing opposition-based learning (OBL) and a self-adapting mechanism to adjust parameters...
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