EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Improving PSO-Based multi-objective optimization using crowding, mutation and ∈-dominance
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Evolutionary multi-objective optimization: a historical view of the field
IEEE Computational Intelligence Magazine
Multiobjective programming using uniform design and genetic algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An orthogonal genetic algorithm with quantization for globalnumerical optimization
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
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To solve the multi-objective problems, a novel hybrid particle swarm optimization algorithm is proposed(called HPSODE). The new algorithm includes three major improvement: (I)Population initialization is constructed by statistical method Uniform Design , (II)Regeneration method has two phases: the first phase is particles updated by adaptive PSO model with constriction factor *** , the second phase is Differential Evolution operator with archive, (III)A new accept rule called Distance/volume fitness is designed to update archive. Experiment on ZDTx and DTLZx problems by jMetal 2.1, the results show that the new hybrid algorithm significant outperforms OMOPSO, SMPSO in terms of additive Epsilon, HyperVolume, Genetic Distance, Inverted Genetic Distance.