Abstract. Many real-world optimization problems have a large number of decision variables. In order to enhance the ability of DE for these problems, a novel local search operation was proposed. This operation combines orthogonal crossover and opposition-based learning strategy. During the evolution of DE, one individual was randomly chosen to undergo this operation. Thus it does not need much computing time, but can improve the search ability of DE. The performance of the proposed method is compared with two other competitive algorithms with benchmark problems. The compared results show the new method's effectiveness and efficiency.Keywords: Large scale optimization · Differential evolution · Orthogonal crossover · Quasi-opposition learning
IntroductionDifferential evolution (DE), proposed by Storn and Price, is a simple yet efficient algorithm for global optimization problems in continuous domain [1].It has been widely used in various applications [2].However, DE still suffers from the "curse of dimensionality", which implies that the performance of DE will deteriorate rapidly while the scale of the search space increases [3]. Thus DE usually fails to find the optimal solutions to large scale optimization problems. Much work have been tried to enhance the performance of DE for large scale optimization problems. One way to improve the performance of DE is by using new crossover operators. Noman et al. proposed a crossover-based adaptive local search operation to enhance the performa-nce of standard DE algorithm [2]. Wang et al. used an orthogonal crossover to enhance the search ability of DE [4]. These works have improved the performance of DE for low dimensional problems. However, when the scale size of problem grows up to 1000 or even more, they can not avoid being trapped into local minimum. Another promising approach to deal with large scale problems is opposition-based learning (OBL) [5,6]. OBL has been successfully applied to enhance the performance of DE. The key concept of OBL is to evaluate the current solutions and their opposite ones simultaneously. And the central opposition theorem has proved that the probability that the opposite of solution is closer to the global optimum is higher than the probability of a second random guess [7]. In recent years, one paradigm that received much attention is
A novel integer encoding Differential Evolution (IEDE) algorithm was proposed for integer optimization problems in this paper. Based on the standard framework of the traditional DE, the population was encoding with integer. The IEDE inherited the crossover operator and selection operator from the traditional DE directly. And a new integer mutation operator was defined to deal with the integer encoding individual. Several initial simulation results show it is effective and efficient in solving the integer optimization problems. The IEDE is a new effective way for the integer optimization problems.
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