This paper presents a comprehensive survey of Multi-Objective Particle Swarm Optimizers (MOPSOs). The authors review various MOPSO approaches, classify them, and identify their main features. They also discuss promising research directions in this field. Particle Swarm Optimization (PSO) is a heuristic search technique inspired by bird flocking behavior. It has been extended to multi-objective optimization, where the goal is to find a set of solutions that represent the best trade-offs among conflicting objectives. The paper discusses the challenges of applying PSO to multi-objective problems, including the selection of leaders, retention of nondominated solutions, and maintaining diversity in the swarm. It also covers different approaches to MOPSO, such as aggregating functions, lexicographic ordering, sub-population methods, and Pareto-based approaches. The paper highlights the importance of diversity in PSO and discusses techniques to promote it, such as the use of mutation operators and different neighborhood topologies. The authors also discuss the use of external archives to store nondominated solutions and the impact of different selection criteria on the performance of MOPSO. The paper concludes with a discussion of future research directions in multi-objective optimization.This paper presents a comprehensive survey of Multi-Objective Particle Swarm Optimizers (MOPSOs). The authors review various MOPSO approaches, classify them, and identify their main features. They also discuss promising research directions in this field. Particle Swarm Optimization (PSO) is a heuristic search technique inspired by bird flocking behavior. It has been extended to multi-objective optimization, where the goal is to find a set of solutions that represent the best trade-offs among conflicting objectives. The paper discusses the challenges of applying PSO to multi-objective problems, including the selection of leaders, retention of nondominated solutions, and maintaining diversity in the swarm. It also covers different approaches to MOPSO, such as aggregating functions, lexicographic ordering, sub-population methods, and Pareto-based approaches. The paper highlights the importance of diversity in PSO and discusses techniques to promote it, such as the use of mutation operators and different neighborhood topologies. The authors also discuss the use of external archives to store nondominated solutions and the impact of different selection criteria on the performance of MOPSO. The paper concludes with a discussion of future research directions in multi-objective optimization.