Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
An introduction to genetic algorithms
An introduction to genetic algorithms
Swarm intelligence
Particle swarm optimization method in multiobjective problems
Proceedings of the 2002 ACM symposium on Applied computing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Fundamentals of Computational Swarm Intelligence
Fundamentals of Computational Swarm Intelligence
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
A MOPSO algorithm based exclusively on pareto dominance concepts
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
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In service systems, service cost is the cost associated with the employment of service-providing personnel, while utilization rate is the proportion of the system's resources which are used by the incoming traffic for service. Service cost and server utilization rate are conflicting objectives in service systems. In this paper, we cast the problem as a multi-objective optimization problem and use the Multi-Objective Particle Swarm Optimization MOPSO algorithm to minimize the two conflicting objectives simultaneously. MOPSO is a fairly recent swarm intelligence meta-heuristic algorithm known for its simplicity in programming and its rapid convergence. The multi-objective optimization procedure is illustrated with the example of a practical service system. MOPSO produces a well-spread Pareto front for the two conflicting objective functions in the practical service system.