Particle swarm optimization method in multiobjective problems
Proceedings of the 2002 ACM symposium on Applied computing
An effective use of crowding distance in multiobjective particle swarm optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Multiobjective optimization using dynamic neighborhood particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
MOPSO: a proposal for multiple objective particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
A novel hybrid immune algorithm for global optimization in design and manufacturing
Robotics and Computer-Integrated Manufacturing
A non-dominated sorting particle swarm optimizer for multiobjective optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Optimization using particle swarms with near neighbor interactions
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
International Journal of Applied Metaheuristic Computing
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Particle Swarm Optimization (PSO) is one of the most effective metaheuristics algorithms, with many successful real-world applications. The reason for the success of PSO is the movement behavior, which allows the swarm to effectively explore the search space. Unfortunately, the original PSO algorithm is only suitable for single objective optimization problems. In this paper, three movement strategies are discussed for multi-objective PSO (MOPSO) and popular test problems are used to confirm their effectiveness. In addition, these algorithms are also applied to solve the engineering design and portfolio optimization problems. Results show that the algorithms are effective with both direct and indirect encoding schemes.