Particle swarm optimization method in multiobjective problems
Proceedings of the 2002 ACM symposium on Applied computing
Maintenance Optimization Of Equipment By Linear Programming
Probability in the Engineering and Informational Sciences
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
Particle swarm optimization for integer programming
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
A non-dominated sorting particle swarm optimizer for multiobjective optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
A new mechanism for maintaining diversity of Pareto archive in multi-objective optimization
Advances in Engineering Software
Intelligent particle swarm optimization in multi-objective problems
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A Multiobjective Memetic Algorithm Based on Particle Swarm Optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Maintenance planning plays a key role in equipment operational management, and strategic equipment maintenance planning (SEML) is an integrated and complicated optimization problem consisting of more than one objectives and constraints. In this paper we present a new multi-objective particle swarm optimization (PSO) algorithm for effectively solving the SEML problem model whose objectives include minimizing maintenance cost and maximizing expected mission capability of military equipment systems. Our algorithm employs an objective leverage function for global best selection, and preserves the diversity of non-dominated solutions based on the measurement of minimum pairwise distance. Experimental results show that our approach can achieve good solution quality with low computational costs to support effective decision-making.