Particle swarm optimization method in multiobjective problems
Proceedings of the 2002 ACM symposium on Applied computing
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
Using unconstrained elite archives for multiobjective optimization
IEEE Transactions on Evolutionary Computation
U-measure: a quality measure for multiobjective programming
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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In this paper, considering the multi-objective problems in batch processes, an improved multi-objective particle swarm optimization based on pareto-optimal solutions is proposed. In this method, a novel diversity preservation strategy that combines the information on distance and angle into similarity judgment is employed to select global best and thus guarantees the convergence and the diversity characteristics of the pareto front. As a result, enough pareto solutions are distributed evenly in the pareto front. Lastly, the algorithm is applied to a classical batch process. The results show that the quality at the end of each batch can approximate the desire value sufficiently and the input trajectory converges; thus verify the efficiency and practicability of the algorithm.