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

By Qamar Askari, Irfan Younas, Mehreen Saeed. Knowledge-Based Systems, 2020

518 citations recorded by the source corpus.

Abstract 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.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/2d3615af05dbea32f342c5e589317e7ce22c6112

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Swagatam Das

Citations in the corpus

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges (2023, background): “(2019) 197 Fire Hawk Optimizer (FHO) Azizi et al.”
Spider wasp optimizer: a novel meta-heuristic optimization algorithm (2023, background): “1 3 optimization (Shi 2011), bus transportation algorithm (Bodaghi and Samieefar 2019), and political optimizer (Askari et al. 2020).”
Tuna Swarm Optimization: A Novel Swarm-Based Metaheuristic Algorithm for Global Optimization (2021, background): “Other humanbased metaheuristics include: social evolution and learning optimization (SELO) [37], group teaching optimization algorithm (GTOA) [38], heap-based optimizer (HBO) [39], political optimizer (PO) [40], etc.”