Simplified Multiplayer Battle Game-inspired Optimizer with Diverse Search Strategies

We propose two modifications to the standard multiplayer battle game-inspired optimizer (MBGO) to simplify its search framework and enhance its performance. Specifically, the first modification changes the original serial two-stage search to a parallel approach, where individuals probabilistically choose to execute one of the stages instead of both. The second modification introduces additional new search strategies for generating diversified offspring individuals in the two stages, while retaining the original search strategies. To evaluate the performance of our proposal, we compared the MBGO combined with two proposed modifications against several classic algorithms using the function collections of CEC2017 and CEC2020. The experimental results demonstrate that the improved MBGO is highly competitive, particularly with increasing dimensionality, where the performance improvement becomes more pronounced.