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An Improved ABC Algorithm Based on Initial Population and Neighborhood Search

机译：一种基于初始种群和邻域搜索的改进ABC算法

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摘要

The traditional artificial bee colony algorithm has the disadvantages of insufficient population diversity, strong equation-searching ability but weak developing capacity, which leads to poor quality of solution, local optimum and slow global convergence. This paper increases the population diversity by unlearning initialization, improves the quality of the solution, as well as avoids the local optimum. What’s more, we introduce the cross-operation and the global optimal value into the search process so that it can generate candidate solution next to the global optimal. Thus, it accelerates global convergence speed. The simulation results show that the optimization performance of different optimal function algorithm is better when the cross-factor is about 0.5. An improved ABC algorithm based on initial population and neighborhood search results show that the optimization accuracy is improved by about 2 times, which avoids the local optimum generally. Meanwhile, the number of iteration decreases about 8% to 15%, accelerating the global convergence speed.
机译：传统的人工蜂群算法存在种群多样性不足，方程搜索能力强，开发能力弱的缺点，导致求解质量差，局部最优，全局收敛慢。本文通过取消学习初始化来增加总体多样性，提高了解决方案的质量，并避免了局部最优。此外，我们将交叉运算和全局最优值引入搜索过程，以便它可以在全局最优值旁边生成候选解决方案。因此，它加快了全局收敛速度。仿真结果表明，当交叉因子约为0.5时，不同最优函数算法的优化性能较好。基于初始种群和邻域搜索结果的改进的ABC算法表明，优化精度提高了约2倍，通常避免了局部最优。同时，迭代次数减少了大约8％至15％，从而加快了全局收敛速度。

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