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Hybrid Hierarchical Backtracking Search Optimization Algorithm and Its Application

机译：混合层次回溯搜索优化算法及其应用

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摘要

As a young intelligence optimization algorithm, backtracking search optimization algorithm (BSA) has been used to solve many optimization problems successfully. However, BSA has some disadvantages such as being easy to fall into local optimum, lacking the learning from the optimal individual, and being difficult to adjust the control parameter F. Motivated by these analyses, to improve the optimization performance of the original BSA, a new hybrid hierarchical backtracking search optimization algorithm (HHBSA) is proposed in this paper. In the proposed method, a two-layer hierarchy structure of population and a randomized regrouping strategy are introduced in the proposed HHBSA for improving the diversity of population, a mutation strategy is used to help the population when the evolution is stagnant and an adaptive control parameter is presented to increase the learning ability of the BSA. To verify the performance of the proposed approaches, 48 benchmark functions and three real-world optimization problems are evaluated to test the performance of the proposed approach. Experiment results indicate that HHBSA is competitive to some existing EAs.
机译：回溯搜索优化算法（BSA）作为一种年轻的智能优化算法，已经成功地解决了许多优化问题。然而，BSA具有一些缺点，例如容易陷入局部最优，缺乏从最优个体的学习，难以调整控制参数F的原因。这些分析的目的是提高原始BSA的优化性能。提出了一种新的混合层次回溯搜索优化算法（HHBSA）。该方法将种群的两层层次结构和随机重组策略引入到所提出的HHBSA中，以改善种群的多样性，当进化停滞时使用变异策略来帮助种群，并采用自适应控制参数旨在提高BSA的学习能力。为了验证所提出方法的性能，评估了48个基准函数和三个实际优化问题，以测试所提出方法的性能。实验结果表明HHBSA相对于某些现有EA具有竞争力。

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