Improved Political Optimizer (IPO)

Improved Political Optimizer

Political Optimizer (PO) is a recently proposed meta-heuristic with excellent convergence speed and exploitation
capability. However, it is found that PO prematurely converges for complex problems because of not giving
enough time to the exploration. In this paper, the exploration capability and balance of PO are improved by
making multiple modifications to propose an Improved Political Optimizer (IPO). To improve the exploration
capability, the condition of an equal number of parties and constituencies is relaxed, and switching with a
random member of a random party is incorporated in the party-switching phase. Moreover, the balance between
exploration and exploitation is enhanced by modifying the position-updating strategy (RPPUS) in the election
campaign phase and replacing the tunable party-switching rate with a self-adaptive parameter. The exploitation
is further improved by utilizing the best solution of the population in the parliamentary affairs phase. In addition
to improvement in PO, this paper also highlights a correction in the party-switching phase of the original PO. The
performance of IPO is evaluated using 30 CEC-2014 benchmarks, 29 CEC-BC-2017 benchmarks, and 6 mechanical
engineering problems. It is shown through non-parametric statistical Wilcoxon’s rank-sum test that IPO
significantly outperforms PO. Moreover, IPO is also compared with 10 of the well-cited and 14 latest optimization
algorithms published in 2020. It is shown by using the Friedman mean-rank test that IPO secures the first
rank for both types of benchmark functions. Moreover, the comparison of IPO with PO and a few well-known
algorithms for 6 of the engineering problems shows that IPO performs better or equivalently to the compared
optimization algorithms.

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