Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
Evolutionary Computation
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
A multi-objective evolutionary algorithm with weighted-sum niching for convergence on knee regions
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
A Fuzzy Particle Swarm Approach to Multiobjective Quadratic Assignment Problems
AMS '08 Proceedings of the 2008 Second Asia International Conference on Modelling & Simulation (AMS)
A preference-based evolutionary algorithm for multi-objective optimization
Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
The smart normal constraint method for directly generating a smart Pareto set
Structural and Multidisciplinary Optimization
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Evolutionary algorithms have amply demonstrated their effectiveness and efficiency in approximating the Pareto front of different multi-objective optimization problems. Fewer attentions have been paid to search for the preferred parts of the Pareto front according to the decision maker preferences. Knee regions are special portions of the Pareto front containing solutions having the maximum marginal rates of return, i.e., solutions for which an improvement in one objective implies a severe degradation in at least another one. Such characteristic makes knee regions of particular interest in practical applications from the decision maker perspective. In this paper, we propose a new updating strategy for a reference points based multi-objective evolutionary algorithm which forces this latter to focus on knee regions. The proposed idea uses a set of mobile reference points guiding the search towards knee regions. The extent of the obtained regions could be controlled by the means of a user-defined parameter. The verification of the proposed approach is assessed on two- and three-objective knee-based test problems a priori and interactively. The obtained results are promising.