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 of crocodiles. Although RSA has a fast convergence speed, due to the influence of the crocodile predation mechanism, if the algorithm falls into the local optimum in the early stage, RSA will probably be unable to jump out of the local optimum, resulting in a poor comprehensive performance. Because of the shortcomings of RSA, 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 Benefiting from adding a restart strategy, when the optimal solution of RSA is no longer updated, the algorithm's ability to jump out of the local optimum is effectively improved by randomly initializing the crocodile.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 Reptile Search Algorithm with Ghost Opposition-based Learning for the Global Optimization Problem (IRSA) is proposed. To verify the performance of IRSA, we used nine famous optimization algorithms to compare with IRSA in twenty-three 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.

MLBRSA: Multi-Learning-Based Reptile Search Algorithm for Global Optimization and Software Requirement Prioritization Problems

“…When evaluating the proposed MLBRSA, it is benchmarked against several other algorithms. These include the RSA, improved RSA (IRSA) [65], reinforcement learning-based GWO (RLBGWO) [82], improved dwarf mongoose optimization algorithm (IDMOA) [102], RL-based hybrid Aquila optimizer and AOA (RLAOA) [80], adaptive gaining-sharing knowledge (AGSK) algorithm [103], and ensemble sinusoidal differential covariance matrix adaptation with Euclidean neighborhood (LSHADE-cnEpSin) algorithm [104]. The population size and the maximum number of iterations for the 23 test functions are 30 and 500, respectively, and for the real-world problems are 30 and 1000, respectively.…”

“…This results in improved algorithm performance compared to other recently developed algorithms in particular problem domains. The authors of [65] introduced a modified version of the RSA specifically designed for numerical optimization problems. The utilization of the adaptive chaotic oppositionbased learning strategy, shifting distribution estimation method, and elite alternative pooling technique effectively enhance the variety of the population, thereby achieving a balanced approach to exploration and exploitation.…”

AOBLMOA: A Hybrid Biomimetic Optimization Algorithm for Numerical Optimization and Engineering Design Problems

“…Muhammad et al [ 17 ] combined OBL with teaching–learning-based optimization. Jia et al [ 18 ] hybridized OBL with the Reptile Search Algorithm. Sarada et al [ 19 ] mixed OBL with the Golden Jackal Optimization algorithm to optimize it.…”