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Using Artificial Plant Optimization Algorithm with Dynamic Population and Cluster Methods to Optimize the Performance of DV-Hop

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Artificial plant optimization algorithm is a novel stochastic population-based evolutionary algorithm by simulating the plant growing process. In this paper, a new variant, which is called APOA-DC is proposed by incorporating with dynamic population size and cluster methods, furthermore, to investigate the performance, APOA-DC is applied to optimize the DV-Hop localization algorithm, simulation results show it achieves better performance than the DV-Hop algorithm.
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Keywords: ARTIFICIAL PLANT OPTIMIZATION ALGORITHM; CLUSTER; DV-HOP; DYNAMIC POPULATION

Document Type: Research Article

Publication date: August 1, 2014

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