An Improved Reptile Search Algorithm with Ghost Opposition-based Learning for Global Optimization Problems

Abstract

In 2021, a meta-heuristic algorithm, Reptile Search Algorithm (RSA), was proposed. RSA mainly simulates the cooperative predatory behavior oFcrocodiles. Although RSA has a fast convergence speed, due to the influence oFthe crocodile predation mechanism, iFthe algorithm falls into the local optimum in the early stage, RSA will probably be unable to jump out oFthe local optimum, resulting in a poor comprehensive performance. Because oFthe shortcomings oFRSA, introducing the local escape operator can effectively improve crocodiles' ability to explore space and generate new crocodiles to replace poor crocodiles. Benefiting from adding a restart strategy, when the optimal solution oFRSA is no longer updated, the algorithm's ability to jump out oFthe local optimum is effectively improved by randomly initializing the crocodile. Then joining Ghost opposition-based learning to balance the IRSA's exploitation and exploration, the Improved RSA with Ghost Opposition-based Learning for the Global Optimization Problem (IRSA) is proposed. To verify the performance oFIRSA, we used nine famous optimization algorithms to compare with IRSA in 23 standard benchmark functions and CEC2020 test functions. The experiments show that IRSA has good optimization performance and robustness, and can effectively solve six classical engineering problems, thus proving its effectiveness in solving practical problems.