Swarm intelligence
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Metaheuristics: From Design to Implementation
Metaheuristics: From Design to Implementation
The measure of Pareto optima applications to multi-objective metaheuristics
EMO'03 Proceedings of the 2nd 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
IEEE Transactions on Evolutionary Computation
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
IEEE Transactions on Evolutionary Computation
A multi-objective particle swarm optimizer based on decomposition
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Swarm intelligence guided by multi-objective mathematical programming techniques
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Clustering-based leaders' selection in multi-objective particle swarm optimisation
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
D2MOPSO: multi-objective particle swarm optimizer based on decomposition and dominance
EvoCOP'12 Proceedings of the 12th European conference on Evolutionary Computation in Combinatorial Optimization
Comprehensive Survey of the Hybrid Evolutionary Algorithms
International Journal of Applied Evolutionary Computation
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A novel Smart Multi-Objective Particle Swarm Optimisation method - SDMOPSO - is presented in the paper. The method uses the decomposition approach proposed in MOEA/D, whereby a multiobjective problem (MOP) is represented as several scalar aggregation problems. The scalar aggregation problems are viewed as particles in a swarm; each particle assigns weights to every optimisation objective. The problem is solved then as a Multi-Objective Particle Swarm Optimisation (MOPSO), in which every particle uses information from a set of defined neighbours. The paper also introduces a novel smart approach for sharing information between particles, whereby each particle calculates a new position in advance using its neighbourhood information and shares this new information with the swarm. The results of applying SDMOPSO on five standard MOPs show that SDMOPSO is highly competitive comparing with two state-of-the-art algorithms.