An Indicator-Based Multiobjective Evolutionary Algorithm With Reference Point Adaptation for Better Versatility
By Ye Tian, Ran Cheng, Xing-yi Zhang, Fan Cheng, Yaochu Jin. IEEE Transactions on Evolutionary Computation, 2018
597 citations recorded by the source corpus.
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 versatile for solving problems with various types of Pareto fronts, outperforming several state-of-the-art evolutionary algorithms for multiobjective and many-objective optimization.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/3045665705ae12b8e96cda9493094ffc107754a9
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Citations in the corpus
- Balancing Objective Optimization and Constraint Satisfaction in Constrained Evolutionary Multiobjective Optimization (2021, background): “The indicator-based evolutionary algorithms select solutions according to their contributions to performance indicators, such as hypervolume (HV) [7] and inverted generational distance (IGD)-NS [8], where performance indicators can 2168-2267 c (cid:6) 2021 IEEE.”
- A Strengthened Dominance Relation Considering Convergence and Diversity for Evolutionary Many-Objective Optimization (2018, background): “The second idea employs performance indicators to distinguish the quality of incomparable candidate solutions by selecting those with large contributions, such as HypE [16] and ARMOEA [17].”
- Efficient Large-Scale Multiobjective Optimization Based on a Competitive Swarm Optimizer (2020, background): “, the strategy to select solutions from the current population for the next generation) [13]–[15]; however, their search efficiency is not high enough to find optimal solutions in a large search space, hence they are incapable of solving large-scale MOPs no matter how effective the environmental sel”
- A Survey on Evolutionary Constrained Multiobjective Optimization (2023, methodology): “Up to now, a large number of MOEAs have been proposed, and they can be roughly divided into the methods based on dominance [7]–[9], the methods based on decomposition [10]–[12], and the methods based on indicator [13]–[15].”
- A competitive mechanism based multi-objective particle swarm optimizer with fast convergence (2018, background): “Such optimization problems are usually called multi-objective optimization problems (MOPs), which are generally more challenging to be solved than single-objective optimization problems (SOPs), since there usually exist a set of solutions to be obtained as trade-offs between different objectives for”