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This paper studies an evolutionary multiobjective optimization algorithm, called EVOLT, which heuristically optimizes QoS (Quality of Service) in communication networks for electric power utilities. EVOLT uses a population of individuals, each of which represents a set of QoS parameters, and evolves them via genetic operators such as crossover and mutation for satisfying given QoS requirements. Simulation results show that EVOLT outperforms a well-known existing evolutionary algorithm for multiobjective optimization and efficiently obtains quality QoS parameters with acceptable computational costs.
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