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Political Optimizer: A novel socio-inspired meta-heuristic for global optimization

机译：政治优化器：全球优化的新型社会启发式荟萃启发式

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This paper proposes a novel global optimization algorithm called Political Optimizer (PO), inspired by the multi-phased process of politics. PO is the mathematical mapping of all the major phases of politics such as constituency allocation, party switching, election campaign, inter-party election, and parliamentary affairs. The proposed algorithm assigns each solution a dual role by logically dividing the population into political parties and constituencies, which facilitates each candidate to update its position with respect to the party leader and the constituency winner. Moreover, a novel position updating strategy called recent past-based position updating strategy (RPPUS) is introduced, which is the mathematical modeling of the learning behaviors of the politicians from the previous election. The proposed algorithm is benchmarked with 50 unimodal, multimodal, and fixed dimensional functions against 15 state of the art algorithms. We show through experiments that PO has an excellent convergence speed with good exploration capability in early iterations. Root cause of such behavior of PO is incorporation of RPPUS and logical division of the population to assign dual role to each candidate solution. Using Wilcoxon rank-sum test, PO demonstrates statistically significant performance over the other algorithms. The results show that PO outperforms all other algorithms, and consistency in performance on such a comprehensive suite of benchmark functions proves the versatility of the algorithm. Furthermore, experiments demonstrate that PO is invariant to function shifting and performs consistently in very high dimensional search spaces. Finally, the applicability on real-world applications is demonstrated by efficiently solving four engineering optimization problems. (C) 2020 Elsevier B.V. All rights reserved.
机译：本文提出了一种新的全球优化算法，称为政治优化器（PO），灵感来自于政治的多分阶段过程。 PO是政治所有主要阶段的数学映射，如选区分配，党转换，选举活动，党派选举和议会事务。所提出的算法通过将人口划分为政党和选区来分配双重作用，这促进了各位候选人更新其关于党领导人和选区冠军的地位。此外，介绍了一种新的位置更新策略，称为近期的基于位置更新策略（RPPU），这是从前选举的政治家的学习行为的数学建模。该算法采用50个单向，多模式和固定尺寸函数的基准测试，其用于15个现有技术的算法。我们通过实验表明，PO在早期迭代中具有良好的勘探能力的优良收敛速度。 PO的这种行为的根本原因是纳入人口的RPPU和逻辑划分，为每个候选解决方案分配双重角色。使用Wilcoxon Rank-Sum测试，Po通过其他算法展示了统计上显着的性能。结果表明，PO优于所有其他算法，在这种全面的基准函数套件上的性能一致性证明了算法的多功能性。此外，实验表明，PO不变于在非常高维搜索空间中始终运行和执行。最后，通过有效解决四种工程优化问题，证明了对现实世界应用的适用性。 （c）2020 Elsevier B.v.保留所有权利。

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