Computer Science ›› 2017, Vol. 44 ›› Issue (4): 269-274, 311.doi: 10.11896/j.issn.1002-137X.2017.04.056

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Application of Bacteria Foraging Algorithm in High-dimensional Optimization Problems

LI Jun and DANG Jian-wu   

  • Online:2018-11-13 Published:2018-11-13

Abstract: Excessive empirical parameters in the former self-adaptation step formula caused the defect in terms of failing to achieve self-adaptation in bacteria foraging optimization algorithm.Therefore,a revised step formula has been proposed,which enables step length to be relevant to the present evolution generation of individual bacteria as well as the optimal range of the problem to be solved,in order to achieve the step length self-adaption.Besides,the combination of chaotic thoughts and differential evolution thoughts with bacteria foraging algorithm can improve both the initial process and optimal process of the algorithm.This method increased the diversity of groups,preventing the algorithm from falling into the local optima due to the precocious.In the optimal process of high-dimensional problem,fractal dimension optimization is used to replace the former method.The fractal dimension optimization means that the information of every dimension will be updated one by one on the basis of whether the new position of every dimension changes comparing to the fitness value.Dealing with the problems in different dimensions can boost the precision and efficiency of the algorithm obviously.Experiments show that through the testing of multiple standard test functions in the hyperspace,the revised algorithm optimizing in the hyperspace has several benefits,such as fast speed,high precision and the simple process of solving.It has improved manifestly in terms of precision when comparing to other modified programs.

Key words: Bacteria foraging optimization algorithm(BFO),Adaptive step size,Chaos theory,Differential evolution(DE),High-dimensional optimization

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