一种改进的蝙蝠算法研究
首发时间:2015-12-30
摘要:蝙蝠算法( BA)是一类新型的搜索全局最优解的随机优化算法,针对这些问题标准的编蝠算法存在着寻优精度不高、后期收敛速度慢、易陷入局部最优等问题,本文提出了一种基于powell机制的改进的蝙蝠算法(POBA),从而提高编蝠算法的局部搜索能力,避免种群个体陷入局部最优,增强算法全局寻优能力。在MATLAB环境下,运用5个标准测试函数进行实验仿真,结果表明,与BA算法相比,该算法(POBA)的收敛速度及精度均有明显提高。
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Study on an improved echanisms bat algorithm
Abstract:Bat algorithm (BA) are a new class of stochastic optimization algorithms search the global optimal solution, but the standard compilation bat optimization algorithm exists accuracy is not high, late slow convergence, easy to fall into local optimum problems. To solve these problems, an improved algorithm is proposed bat powell mechanism based (POBA). Compiled bat algorithm to improve the local search ability, to avoid falling into local optimum populations of individuals, and enhance the overall algorithm optimization. In the MATLAB environment, the use of five standard test functions simulation experiments, the results show that compared with BA algorithm, (POBA) convergence of the algorithm has significantly improved the speed and accuracy.
Keywords: Bats algorithm Powell Convergence rate Simulation
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