Reinforcement Learning-Based Bonobo Optimizer for Efficient Load Balancing in Cloud Computing

Abstract

The field of optimization has undergone a paradigm shift with the advent of reinforcement learning techniques, which are widely used for solving complex problems in various domains. In this paper, we propose a novel optimization algorithm, called the Bonobo Optimization. The algorithm is inspired by the social behaviour of Bonobo apes, which are known for their collaborative and communicative behaviour. This algorithm is designed to learn from its interactions with the environment and adapt to new situations, making it a robust and adaptive optimization tool. We demonstrate the effectiveness on several benchmark optimization problems, where it outperforms them.