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Greedy Politics Optimization: Metaheuristic Inspired by Political Strategies adopted during State Assembly Elections

机译：贪婪的政治优化：在国会选举中采用的政治策略启发的核心思考

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摘要

Similar to biology inspired optimization algorithms, this paper proposes a novel metaheuristic Greedy Politics Optimization (GPO), inspired by political strategies adopted by politicians to contest in elections and form the government. Interestingly, the performance of the algorithm was found to improve when unethical practices adopted by greedy politicians were taken into account. Several benchmark multi-dimensional test functions were optimized using the proposed algorithm and the accuracy of GPO proved efficient compared to other classical metaheuristics. Performance analysis also reveals that the convergence efficiency of GPO is highly superior compared to particle swarm optimization.
机译：本文类似于生物学启发优化算法，提出了一种新的沟哲学贪婪政治优化（GPO），受到政治家通过的政治策略来参加选举并形成政府的政治策略。有趣的是，当考虑到贪婪政治家采用的不道德实践时，发现算法的表现得到改善。使用所提出的算法优化了多个基准多维测试功能，与其他古典殖民学相比，GPO的准确性得到了优化。性能分析还表明，与粒子群优化相比，GPO的收敛效率高度优势。

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