Greedy Politics Optimization: Metaheuristic inspired by political strategies adopted during state assembly elections

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.

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