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Qinghua Wu
Wuhan Institute of Technology, Wuhan

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An Improved Robot Path Planning Algorithm Based on Genetic Algorithm Xuesong Yan; Qinghua Wu; Hammin Liu
Indonesian Journal of Electrical Engineering and Computer Science Vol 10, No 8: December 2012
Publisher : Institute of Advanced Engineering and Science

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Abstract

Robot path planning is a NP problem; traditional optimization methods are not very effective to solve it. 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. We also use the proposed algorithm to solve the robot path planning problem and the experiment results indicated that the new algorithm is efficiency for solving the robot path planning problems and the best path usually can be found. DOI: http://dx.doi.org/10.11591/telkomnika.v10i8.1191 
Orthogonal Particle Swarm Optimization Algorithm and Its Application in Circuit Design Xuesong Yan; Qinghua Wu; Hammin Liu
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 6: June 2013
Publisher : Institute of Advanced Engineering and Science

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Abstract

In this paper, aim at the disadvantages of standard Particle Swarm Optimization (PSO) 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 the standard particle swarm optimization algorithm. We use the proposed algorithm for digital circuit optimization design, and the final circuit is optimized in terms of complexity (with the minimum number of gates). DOI: http://dx.doi.org/10.11591/telkomnika.v11i6.2135
Co-Authors Hammin Liu Hammin Liu Xuesong Yan Xuesong Yan