Artificial evolution for computer graphics
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
An updated survey of GA-based multiobjective optimization techniques
ACM Computing Surveys (CSUR)
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
MOCS: Multi-objective Clustering Selection Evolutionary Algorithm
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
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
Techniques for highly multiobjective optimisation: some nondominated points are better than others
Proceedings of the 9th annual conference on Genetic and evolutionary computation
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Substitute distance assignments in NSGA-II for handling many-objective optimization problems
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
DEMO: differential evolution for multiobjective optimization
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Discovering driving strategies with a multiobjective optimization algorithm
Applied Soft Computing
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Abstract: Traditional multi-objective optimization algorithms typically return several hundred non-dominated solutions. From a practical point of view, a small set of 5-10 distinct candidates is often preferred because post-processing many solutions may be too costly, too time-consuming, or it may be too difficult to compare design differences. In this paper, we introduce Multi-objective Distinct Candidates Optimization (MODCO) as an approach to find a user-defined low number of clearly different solutions wrt. performance and design. To demonstrate the potential of the MODCO approach, we suggest the General Cluster-Forming Differential Evolution (GCFDE) algorithm and test it on five well-known mechanical engineering problems and a new five-objective constrained problem from electrical engineering - the circuit component sizing problem of the Alpha Pro pump. The experiments showed that GCFDE significantly outperformed all competing MOEAs on the many-objective circuit problem and had slightly better performance on the mechanical problems. Furthermore, our algorithm was able to return result sets in accordance with the user's settings for result set cardinality as well as performance and design distinctiveness.