A switching competitive swarm optimizer for multi-objective optimization with irregular Pareto fronts

In the past two decades, multi-objective particle swarm optimization, as a powerful swarm intelligence paradigm, has been widely used to solve multi-objective optimization problems. By further exploring the competition mechanism among particles in the swarm, some particle update strategies have been proposed, which are effective in improving convergence performance and search speed. However, these strategies encounter difficulties in properly approximating irregular Pareto fronts (PFs), as their geometric structures are complex, leading to get trapped in local optima. To address this issue, a switching competitive swarm optimizer for multi-objective optimization with irregular PFs is proposed. In comparison with existing approaches that utilize reference vectors to guide the search or selection process, the proposed approach introduces a competition-based learning strategy with a novel winner determination mechanism. Within this strategy, independent neighborhoods are constructed for each winner to learn the structure of irregular PFs. To take advantage of the potential knowledge of winners, the switching competitive swarm optimizer is designed to provide more diverse search directions. The experimental results demonstrate the superiority of the proposed algorithm over several state-of-the-art evolutionary algorithms in tackling multi-objective optimization problems with irregular PFs.