A Novel Cooperative Parallel Multi-Population Optimization Algorithm
Nimish Verma, Pooya Moradian Zadeh, Ziad Kobti · 2022
This work proposes a new parallel meta-heuristic optimization algorithm to deal with high dimensional optimization problems. We introduce a parallel and co-evolving multi population framework that mimics the hierarchical structure of grey wolves. We also propose using elite groups and a probabilistic mutation operator to improve the convergence speed and exploration ability. The algorithm is benchmarked on the twenty-eight functions of IEEE Congress of Evolutionary Computation (CEC) 2013 test suites and is compared with other meta-heuristic algorithms. Our proposed algorithm results show that our algorithm can find more optimal solutions at higher dimensions as compared to other meta-heuristic algorithms. Non-parametric statistical test also show the consistency in the obtained results.