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Hybrid Modified Chimp Optimization Algorithm and Reinforcement Learning for Global Numeric Optimization
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DOI：10.1007/s42235-023-00394-2.png)
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Abstract
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Chimp Optimization Algorithm (ChOA) is one of the most efficient recent optimization algorithms, which proved its ability to deal with different problems in various do- mains. However, ChOA suffers from the weakness of the local search technique which leads to a loss of diversity, getting stuck in a local minimum, and procuring premature convergence. In response to these defects, this paper proposes an improved ChOA algorithm based on using Opposition-based learning (OBL) to enhance the choice of better solutions, written as OChOA. Then, utilizing Reinforcement Learning (RL) to improve the local research technique of OChOA, called RLOChOA. This way effectively avoids the algorithm falling into local optimum. The performance of the proposed RLOChOA algorithm is evaluated using the Friedman rank test on a set of CEC 2015 and CEC 2017 benchmark functions problems and a set of CEC 2011 real-world problems. Numerical results and statistical experiments show that RLOChOA provides better solution quality, convergence accuracy and stability compared with other state-of-the-art algorithms.
Keywords:
Chimp optimization algorithm
Reinforcement learning
Disruption operator
Opposition-based learning
CEC 2011 real-world problems
CEC 2015 and CEC 2017 benchmark functions problems
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