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Qinghua Wu
Wuhan Institute of Technology

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An Improved Genetic Algorithm and Its Application Xuesong Yan; Qinghua Wu; Can Zhang; Wei Li; Wei Chen; Wenjing Luo
Indonesian Journal of Electrical Engineering and Computer Science Vol 10, No 5: September 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Traditional genetic algorithm trapped into the local minimum easily. Therefore, based on a simple genetic algorithm and combine the base ideology of orthogonal design method then applied it to the population initialization, using the intergenerational elite mechanism, as well as the introduction of adaptive local search operator to prevent trapped into the local minimum and improve the convergence speed to form a new genetic algorithm. Through the series of numerical experiments, the new algorithm has been proved to be efficiency. DOI: http://dx.doi.org/10.11591/telkomnika.v10i5.1265
An Improved Constrained Engineering Optimization Design Algorithm Yuxin Sun; Qinghua Wu; Xuesong Yan
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 11: November 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i11.pp7079-7978

Abstract

Many engineering optimization problems can be state as function optimization with constrained, intelligence optimization algorithm can solve these problems well. Particle Swarm Optimization (PSO) algorithm was developed under the inspiration of behavior laws of bird flocks, fish schools and human communities. In this paper, aim at the disadvantages of standard Particle Swarm Optimization algorithm like being trapped easily into a local optimum, we improves the standard PSO and proposes a new algorithm to solve the overcomes of the standard PSO. The new algorithm keeps not only the fast convergence speed characteristic of PSO, but effectively improves the capability of global searching as well. Experiment results reveal that the proposed algorithm can find better solutions when compared to other heuristic methods and is a powerful optimization algorithm for engineering optimization problems.
Co-Authors Can Zhang Wei Chen Wei Li Wenjing Luo Xuesong Yan Yuxin Sun