An Indicator-Based Multiobjective Evolutionary Algorithm With Reference Point Adaptation for Better Versatility
Anhui University · University of Birmingham · +1 more institution
Abstract
During the past two decades, a variety of multiobjective evolutionary algorithms (MOEAs) have been proposed in the literature. As pointed out in some recent studies, however, the performance of an MOEA can strongly depend on the Pareto front shape of the problem to be solved, whereas most existing MOEAs show poor versatility on problems with different shapes of Pareto fronts. To address this issue, we propose an MOEA based on an enhanced inverted generational distance indicator, in which an adaptation method is suggested to adjust a set of reference points based on the indicator contributions of candidate solutions in an external archive. Our experimental results demonstrate that the proposed algorithm is…
Citation impact
- FWCI
- 44.25
- Percentile
- 100%
- References
- 74
Authors
5Topics & keywords
- Evolutionary algorithm
- Multi-objective optimization
- Mathematical optimization
- Computer science
- Pareto principle
- Set (abstract data type)
- Point (geometry)
- Evolutionary computation
- No poverty