Two-Archive Evolutionary Algorithm for Constrained Multiobjective Optimization
By Ke Li, Renzhi Chen, Guangtao Fu, Xin Yao. IEEE Transactions on Evolutionary Computation, 2017
657 citations recorded by the source corpus.
When solving constrained multiobjective optimization problems, an important issue is how to balance convergence, diversity, and feasibility simultaneously. To address this issue, this paper proposes a parameter-free constraint handling technique, a two-archive evolutionary algorithm, for constrained multiobjective optimization. It maintains two collaborative archives simultaneously: one, denoted as the convergence-oriented archive (CA), is the driving force to push the population toward the Pareto front; the other one, denoted as the diversity-oriented archive (DA), mainly tends to maintain the population diversity. In particular, to complement the behavior of the CA and provide as much diversified information as possible, the DA aims at exploring areas under-exploited by the CA including the infeasible regions. To leverage the complementary effects of both archives, we develop a restricted mating selection mechanism that adaptively chooses appropriate mating parents from them according to their evolution status. Comprehensive experiments on a series of benchmark problems and a real-world case study fully demonstrate the competitiveness of our proposed algorithm, in comparison to five state-of-the-art constrained evolutionary multiobjective optimizers.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/d5c6a30d44945fa2a8301c38d0b6feae7877b03d
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Computational Theory and Mathematics
In Control and Systems Engineering
Citations in the corpus
- A Survey on Evolutionary Constrained Multiobjective Optimization (2023, methodology): “different types of problems, five representative CMOEAs are selected, which are BiCo [90], C-TAEA [88], CCMO [87], MOEA/D-DAE [70], and PPS-MOEA/D [19].”
- A Coevolutionary Framework for Constrained Multiobjective Optimization Problems (2021, methodology): “ing the constrained dominance relation of NSGA-II [13], the two-archive collaborative framework of C-TAEA [14], and the biphasic search process of PPS [15].”
- Balancing Objective Optimization and Constraint Satisfaction in Constrained Evolutionary Multiobjective Optimization (2021, methodology): “Five popular CMOEAs (i.e., TiGE-2 [20], NSGA-II-CDP [9], C-TAEA [10], PPS [11], and ToP [14]) are selected as baselines.”