Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multiobjective Evolutionary Algorithms and Applications (Advanced Information and Knowledge Processing)
Towards estimating nadir objective vector using evolutionary approaches
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Reliability-based multi-objective optimization using evolutionary algorithms
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
An algorithm for projecting a reference direction onto the nondominated set of given points
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Reference point based multi-objective evolutionary algorithms for group decisions
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Integrating user preferences with particle swarms for multi-objective optimization
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Introduction to Evolutionary Multiobjective Optimization
Multiobjective Optimization
Consideration of Partial User Preferences in Evolutionary Multiobjective Optimization
Multiobjective Optimization
Multiobjective Optimization
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
A tool for multiobjective evolutionary algorithms
Advances in Engineering Software
Investigating and exploiting the bias of the weighted hypervolume to articulate user preferences
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Using a distance metric to guide PSO algorithms for many-objective optimization
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A territory defining multiobjective evolutionary algorithms and preference incorporation
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation - Special issue on preference-based multiobjective evolutionary algorithms
An interactive territory defining evolutionary algorithm: iTDEA
IEEE Transactions on Evolutionary Computation - Special issue on preference-based multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation - Special issue on preference-based multiobjective evolutionary algorithms
The r-dominance: a new dominance relation for interactive evolutionary multicriteria decision making
IEEE Transactions on Evolutionary Computation - Special issue on preference-based multiobjective evolutionary algorithms
An interactive evolutionary multi-objective optimization method based on polyhedral cones
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
Information and Software Technology
Interactive MOEA/D for multi-objective decision making
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Advances in evolutionary multi-objective optimization
SSBSE'12 Proceedings of the 4th international conference on Search Based Software Engineering
A preference multi-objective optimization based on adaptive rank clone and differential evolution
Natural Computing: an international journal
Many-hard-objective optimization using differential evolution based on two-stage constraint-handling
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Advances in Engineering Software
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In this paper, we borrow the concept of reference direction approach from the multi-criterion decision-making literature and combine it with an EMOprocedure to develop an algorithm for finding a single preferred solution in a multi-objective optimization scenario efficiently. EMO methodologies are adequately used to find a set of representative efficient solutions over the past decade. This study is timely in addressing the issue of optimizing and choosing a single solution using certain preference information. In this approach, the user supplies one or more reference directions in the objective space. The population approach of EMO methodologies is exploited to find a set of efficient solutions corresponding to a number of representative points along the reference direction. By using a utility function, a single solution is chosen for further analysis. This procedure is continued till no further improvement is possible. The working of the procedure is demonstrated on a set of test problems having two to ten objectives and on an engineering design problem. Results are verified with theoretically exact solutions on two-objective test problems.