Adaptive Multi-strategy Rabbit Optimizer for Large-scale Optimization

Abstract

As optimization problems continue to grow in complexity, the need for effective metaheuristic algorithms becomes increasingly evident. However, the challenge lies in identifying the right parameters and strategies for these algorithms. In this paper, we introduce the adaptive multi-strategy Rabbit Algorithm (RA). RA is inspired by the social interactions of rabbits, incorporating elements such as exploration, exploitation, and adaptation to address optimization challenges. It employs three distinct subgroups, comprising male, female, and child rabbits, to execute a multi-strategy search. Key parameters, including distance factor, balance factor, and learning factor, strike a balance between precision and computational efficiency. We offer practical recommendations for fine-tuning five essential RA parameters, making them versatile and independent. RA is capable of autonomously selecting adaptive parameter settings and mutation strategies, enabling it to successfully tackle a range of 17 CEC05 benchmark functions with dimensions scaling up to 5000. The results underscore RA’s superior performance in large-scale optimization tasks, surpassing other state-of-the-art metaheuristics in convergence speed, computational precision, and scalability. Finally, RA has demonstrated its proficiency in solving complicated optimization problems in real-world engineering by completing 10 problems in CEC2020.