Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Genetic diversity as an objective in multi-objective evolutionary algorithms
Evolutionary Computation
Performance scaling of multi-objective evolutionary algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
The balance between proximity and diversity in multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
On the Evolutionary Optimization of Many Conflicting Objectives
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Solving multiobjective problems using cat swarm optimization
Expert Systems with Applications: An International Journal
Hi-index | 0.00 |
The proximity of an approximation set to the Pareto-optimal front of a multiobjective optimisation problem and the diversity of the solutions within the approximation set are two essential requirements in evolutionary multiobjective optimisation. These two requirements may be found to be in conflict with each other in many -objective optimisation scenarios deploying Pareto-dominance selection alongside active diversity promotion mechanisms. This conflict is hindering the optimisation process of some of the most established MOEAs and introducing problems such as the problem of dominance resistance and speciation. In this study, a diversity management operator (DMO) for controlling and promoting the diversity requirement in many -objective optimisation scenarios is introduced and tested on a set of test functions with increasing numbers (6 to 12) of objectives. The results achieved by the proposed strategy outperform results achieved by a reputed and representative MOEA in terms of both criteria: convergence and diversity.