Optimization over the efficient set: four special cases
Journal of Optimization Theory and Applications
Dual approach to minimization on the set of Pareto-optimal solutions
Journal of Optimization Theory and Applications
Journal of Optimization Theory and Applications
Dimension reduction by local principal component analysis
Neural Computation
LEARNABLE EVOLUTION MODEL: Evolutionary Processes Guided by Machine Learning
Machine Learning - Special issue on multistrategy learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
The Cross Entropy Method: A Unified Approach To Combinatorial Optimization, Monte-carlo Simulation (Information Science and Statistics)
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms (Studies in Fuzziness and Soft Computing)
Covariance Matrix Adaptation for Multi-objective Optimization
Evolutionary Computation
A hybrid method for solving multi-objective global optimization problems
Journal of Global Optimization
Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II
IEEE Transactions on Evolutionary Computation
Capabilities of EMOA to detect and preserve equivalent pareto subsets
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Prediction-based population re-initialization for evolutionary dynamic multi-objective optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Pareto set and EMOA behavior for simple multimodal multiobjective functions
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Omni-optimizer: a procedure for single and multi-objective optimization
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Properties of an adaptive archiving algorithm for storing nondominated vectors
IEEE Transactions on Evolutionary Computation
Dynamic multiobjective evolutionary algorithm: adaptive cell-based rank and density estimation
IEEE Transactions on Evolutionary Computation
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
A review of multiobjective test problems and a scalable test problem toolkit
IEEE Transactions on Evolutionary Computation
A distributed Cooperative coevolutionary algorithm for multiobjective optimization
IEEE Transactions on Evolutionary Computation
An Investigation on Noisy Environments in Evolutionary Multiobjective Optimization
IEEE Transactions on Evolutionary Computation
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
IEEE Transactions on Evolutionary Computation
On the Evolutionary Optimization of Many Conflicting Objectives
IEEE Transactions on Evolutionary Computation
RM-MEDA: A Regularity Model-Based Multiobjective Estimation of Distribution Algorithm
IEEE Transactions on Evolutionary Computation
A Simulated Annealing-Based Multiobjective Optimization Algorithm: AMOSA
IEEE Transactions on Evolutionary Computation
On the Scalability of Real-Coded Bayesian Optimization Algorithm
IEEE Transactions on Evolutionary Computation
Dominance-Based Multiobjective Simulated Annealing
IEEE Transactions on Evolutionary Computation
Integrating decision space diversity into hypervolume-based multiobjective search
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Defining and optimizing indicator-based diversity measures in multiobjective search
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Parameter evolution for a particle swarm optimization algorithm
ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
A concentration-based artificial immune network for multi-objective optimization
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
Multi-objective immune algorithm with Baldwinian learning
Applied Soft Computing
A hybrid multiobjective evolutionary algorithm: Striking a balance with local search
Mathematical and Computer Modelling: An International Journal
WCCI'12 Proceedings of the 2012 World Congress conference on Advances in Computational Intelligence
On the performance metrics of multiobjective optimization
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
A co-evolutionary multi-objective optimization algorithm based on direction vectors
Information Sciences: an International Journal
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Most existing multiobjective evolutionary algorithms aim at approximating the Pareto front (PF), which is the distribution of the Pareto-optimal solutions in the objective space. In many real-life applications, however, a good approximation to the Pareto set (PS), which is the distribution of the Pareto-optimal solutions in the decision space, is also required by a decision maker. This paper considers a class of multiobjective optimization problems (MOPs), in which the dimensionalities of the PS and the PF manifolds are different so that a good approximation to the PF might not approximate the PS very well. It proposes a probabilistic model-based multiobjective evolutionary algorithm, called MMEA, for approximating the PS and the PF simultaneously for an MOP in this class. In the modeling phase of MMEA, the population is clustered into a number of subpopulations based on their distribution in the objective space, the principal component analysis technique is used to estimate the dimensionality of the PS manifold in each subpopulation, and then a probabilistic model is built for modeling the distribution of the Pareto-optimal solutions in the decision space. Such a modeling procedure could promote the population diversity in both the decision and objective spaces. MMEA is compared with three other methods, KP1, Omni-Optimizer and RM-MEDA, on a set of test instances, five of which are proposed in this paper. The experimental results clearly suggest that, overall, MMEA performs significantly better than the three compared algorithms in approximating both the PS and the PF.