partial reinforcement optimizer (PRO). Matlab Source Code

版本 1.0.0 (3.7 MB) 作者: Ahmad Taheri

This source code is an implementation of the PRO algorithm to solve CEC2017 benchmark functions.

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更新时间 2023/11/2

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Partial Reinforcement Optimizer (PRO), is a novel evolutionary optimization algorithm. The major idea behind the PRO comes from a psychological theory in evolutionary learning and training called the partial reinforcement effect (PRE) theory. According to the PRE theory, a learner is intermittently reinforced to learn or strengthen a specific behavior during the learning and training process. The reinforcement patterns significantly impact the response rate and strength of the learner during a reinforcement schedule, achieved by appropriately selecting a reinforcement behavior and the time of applying reinforcement process. In the PRO algorithm, the PRE theory is mathematically modeled to an evolutionary optimization algorithm for solving global optimization problems.

引用格式

Taheri, Ahmad, et al. “Partial Reinforcement Optimizer: An Evolutionary Optimization Algorithm.” Expert Systems with Applications, Elsevier BV, Oct. 2023, p. 122070, doi:10.1016/j.eswa.2023.122070.

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PRO Matlab Code CEC2017

版本	已发布	发行说明
1.0.0	2023/11/2		下载

MLA	Taheri, Ahmad, et al. “Partial Reinforcement Optimizer: An Evolutionary Optimization Algorithm.” Expert Systems with Applications, Elsevier BV, Oct. 2023, p. 122070, doi:10.1016/j.eswa.2023.122070.
APA	Taheri, A., RahimiZadeh, K., Beheshti, A., Baumbach, J., Rao, R. V., Mirjalili, S., & Gandomi, A. H. (2023). Partial reinforcement optimizer: An evolutionary optimization algorithm. Expert Systems with Applications, 122070. Elsevier BV. Retrieved from https://doi.org/10.1016%2Fj.eswa.2023.122070
BibTeX	@article{Taheri_2023, doi = {10.1016/j.eswa.2023.122070}, url = {https://doi.org/10.1016%2Fj.eswa.2023.122070}, year = 2023, month = {oct}, publisher = {Elsevier {BV}}, pages = {122070}, author = {Ahmad Taheri and Keyvan RahimiZadeh and Amin Beheshti and Jan Baumbach and Ravipudi Venkata Rao and Seyedali Mirjalili and Amir H. Gandomi}, title = {Partial reinforcement optimizer: An evolutionary optimization algorithm}, journal = {Expert Systems with Applications} }