Particle swarm optimization method in multiobjective problems
Proceedings of the 2002 ACM symposium on Applied computing
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A MOPSO algorithm based exclusively on pareto dominance concepts
EMO'05 Proceedings of the Third 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
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
The fully informed particle swarm: simpler, maybe better
IEEE Transactions on Evolutionary Computation
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
Finding Minimal Addition Chains with a Particle Swarm Optimization Algorithm
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
Expert Systems with Applications: An International Journal
Multi-objective workflow grid scheduling based on discrete particle swarm optimization
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
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This paper describes a PSO-Nelder Mead Simplex hybrid multi-objective optimization algorithm based on a numerical metric called µ -fuzzy dominance. Within each iteration of this approach, in addition to the position and velocity update of each particle using PSO, the k-means algorithm is applied to divide the population into smaller sized clusters. The Nelder-Mead simplex algorithm is used separately within each cluster for added local search. The proposed algorithm is shown to perform better than MOPSO on several test problems as well as for the optimization of a genetic model for flowering time control in Arabidopsis. Adding the local search achieves faster convergence, an important feature in computationally intensive optimization of gene networks.