This paper proposes four enhanced variants of standard multi-objective particle swarm optimization algorithm by individually or synergistically integrating three optimization strategies: logistic chaotic initialization, Levy flight, and angle-based archive update. Multiple comparative experiments were conducted on 22 test problems from the ZDT, UF, and DTLZ benchmark datasets, covering diverse problem characteristics such as non-convexity, discontinuity and multimodality. Algorithm performance was evaluated using inverted generational distance (IGD) and hypervolume (HV), where IGD measures convergence and distribution uniformity, while HV assesses diversity. The results demonstrate that the proposed variants lead to better performance in comparison to the standard multi-objective particle swarm optimization algorithm across different test problems.