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. PO assigns each solution a dual role by logically dividing the population into political parties and constituencies. Moreover, a novel position updating strategy called recent past-based position updating strategy (RPPUS) is introduced, which is the mathematical modeling of the learning behavior of the politicians from the previous election. This is the source code of "Askari Q, Younas I, Saeed M. Political Optimizer: A novel socio-inspired meta-heuristic for global optimization. Knowledge-Based Systems. 2020 Mar 5" https://doi.org/10.1016/j.knosys.2020.105709.
My Homepage: http://qamaraskari.com/
The Latex sources and MATLAB implementation of my algorithms and benchmark functions are also available at my homepage. I'm open to collaborate if you are interested in to work on my algorithms and enhance them or hybridize them with existing techniques or apply them to solve real-world applications. My research interests and current projects are also available at my homepage.
Qamar Askari (2020). Political Optimizer (PO) (https://www.mathworks.com/matlabcentral/fileexchange/74577-political-optimizer-po), MATLAB Central File Exchange. Retrieved .
Askari, Qamar, et al. “Political Optimizer: A Novel Socio-Inspired Meta-Heuristic for Global Optimization.” Knowledge-Based Systems, Elsevier BV, Mar. 2020, p. 105709, doi:10.1016/j.knosys.2020.105709.
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