Improved Evolutionary Strategy Genetic Algorithm for Nonlinear Programming Problems

Hui-xia Zhu (), Fu-lin Wang, Wen-tao Zhang and Qian-ting Li
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Hui-xia Zhu: Northeast Agriculture University
Fu-lin Wang: Northeast Agriculture University
Wen-tao Zhang: Northeast Agriculture University
Qian-ting Li: Northeast Agriculture University

Chapter Chapter 105 in The 19th International Conference on Industrial Engineering and Engineering Management, 2013, pp 993-1003 from Springer

Abstract: Abstract Genetic algorithms have unique advantages in dealing with optimization problems. In this paper the main focus is on the improvement of a genetic algorithm and its application in nonlinear programming problems. In the evolutionary strategy algorithm, the optimal group preserving method was used and individuals with low fitness values were mutated. The crossover operator uses the crossover method according to the segmented mode of decision variables. This strategy ensured that each decision variable had the opportunity to produce offspring by crossover, thus, speeding up evolution. In optimizing the nonlinear programming problem with constraints, the correction operator method was introduced to improve the feasible degree of infeasible individuals. MATLAB simulation results confirmed the validity of the proposed method. The method can effectively solve nonlinear programming problems with greatly improved solution quality and convergence speed, making it an effective, reliable and convenient method.

Keywords: Nonlinear programming; Genetic algorithm; Improved evolutionary strategy; Correction operator method (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1007/978-3-642-38391-5_105

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