Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Handbook of Parametric and Nonparametric Statistical Procedures
Handbook of Parametric and Nonparametric Statistical Procedures
Study of preference relations in many-objective optimization
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
Empirical comparison of MOPSO methods: guide selection and diversity preservation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Controlling dominance area of solutions and its impact on the performance of MOEAs
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
On sequential online archiving of objective vectors
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
Many-Objective optimization: an engineering design perspective
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
IEEE Transactions on Evolutionary Computation
Diversity Management in Evolutionary Many-Objective Optimization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
I-MOPSO: A Suitable PSO Algorithm for Many-Objective Optimization
SBRN '12 Proceedings of the 2012 Brazilian Symposium on Neural Networks
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Many-Objective Optimization Problems are problems that have more than three objective functions. In general, Multi-Objective Evolutionary Algorithms scale poorly when the number of objectives increases. To overcome this limitation, in a previous study, a new MOPSO algorithm called I-MOPSO was proposed. In this study, this work is extended, and we seek to achieve two goals. The first goal is to perform an in-depth evaluation of the I-MOPSO algorithm in different many-objective scenarios. Two versions of this algorithm are studied: I-MOPSO and I-SIGMA. The second goal is to generalize the I-MOPSO algorithm; the new version is called REF-I-MOPSO, and it uses a new archiving method that guides the search in the algorithm to different regions of the Pareto Front using reference points. Two variants of this algorithm are presented: REF_M and REF_Ex. All these algorithms are evaluated with several Many-Objective Problems in terms of their convergence and diversity to the Pareto front. Additionally, we present an empirical analysis that aims to analyze the distribution of the solutions that are generated by the REF-I-MOPSO algorithm. The results showed that the solutions generated by this algorithm were close to the selected reference point. Furthermore, the results of REF-I-MOPSO were notably similar to I-MOPSO.