Swarm intelligence
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II
IEEE Transactions on Evolutionary Computation
Metaheuristics: From Design to Implementation
Metaheuristics: From Design to Implementation
A novel smart multi-objective particle swarm optimisation using decomposition
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
A multi-objective particle swarm optimizer based on decomposition
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Improving PSO-Based multi-objective optimization using crowding, mutation and ∈-dominance
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
IEEE Transactions on Evolutionary Computation
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D2MOPSO is a multi-objective particle swarm optimizer that incorporates the dominance concept with the decomposition approach. Whilst decomposition simplifies the multi-objective problem (MOP) by rewriting it as a set of aggregation problems, solving these problems simultaneously, within the PSO framework, might lead to premature convergence because of the leader selection process which uses the aggregation value as a criterion. Dominance plays a major role in building the leader's archive allowing the selected leaders to cover less dense regions avoiding local optima and resulting in a more diverse approximated Pareto front. Results from 10 standard MOPs show D2MOPSO outperforms two state-of-the-art decomposition based evolutionary methods.