Constraint Handling in Multiobjective Evolutionary Optimization

By Yonas G. Woldesenbet, Gary G. Yen, Biruk G. Tessema. IEEE Transactions on Evolutionary Computation, 2009

383 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/48759bcd60776c0e05a93d830dd70b7bb8b8ffab

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Citations in the corpus

The jMetal framework for multi-objective optimization: Design and architecture (2010, methodology): “mechanisms can be added easily to our tool. A survey of them can be found in [ 47 ].”
Handling Constrained Multiobjective Optimization Problems With Constraints in Both the Decision and Objective Spaces (2019, methodology): “Woldesenbet et al. [24] proposed a CMOEA based on an adaptive penalty function and a distance measure, which can not only search for the Pareto optimal solutions in the feasible region, but also exploit the important information provided by the infeasible individuals with better objective function…”
A Multi-Facet Survey on Memetic Computation (2011, neutral): “function management schemes [66]–[68], multiobjective”
Evolutionary Constrained Multiobjective Optimization: Test Suite Construction and Performance Comparisons (2019, methodology): “Under the framework of NSGA-II, we found that: 1) the -constrained method [43] can well solve types I and II of CMOPs; 2) ATM [46] shows remarkable superiority on the type-I CMOPs with narrow feasible regions; 3) SP [39] can well approximate the unconstrained PF and the boundary of the feasible regi”
A Coevolutionary Framework for Constrained Multiobjective Optimization Problems (2021, background): “In [20], the objective values of each solution were modiﬁed by considering its constraint violation.”
Bio-inspired computation: Where we stand and what's next (2019, background): “When this is the case, many strategies to handle different types of constraints have been reported by the community [235, 410, 411, 412].”
Constraint-handling in nature-inspired numerical optimization: Past, present and future (2011, background): “For example, death-penalties [197], adaptive penalty functions [198, 199], use of constraints as objectives [200], stochastic ranking [201], feasibility rules [151, 202, 203] (with a modification in the first rule, in which two feasible solutions are compared, since in this case nondominance is chec”
Fuzzy-Based Pareto Optimality for Many-Objective Evolutionary Algorithms (2014, background): “Continuing research will be extended to other state-of-the-art MOEAs and to MOEAs designed specifically to handle constrained MaOPs [29], [30] and dynamic MaOPs [31], [32].”
Two-Archive Evolutionary Algorithm for Constrained Multiobjective Optimization (2017, methodology): “C-MOEA/D, C-NSGA-III, C-MOEA/DD [27], I-DBEA [21], and CMOEA [5], are chosen for peer comparisons.”
Push and Pull Search for Solving Constrained Multi-objective Optimization Problems (2017, methodology): “In the case of λ is changing according to the information collected from the search process, it is called an adaptive penalty approach [6]– [9].”
Balancing Objective Optimization and Constraint Satisfaction in Constrained Evolutionary Multiobjective Optimization (2021, background): “In [18], the objec-tive values of each solution are modiﬁed by considering its constraint violation.”
A Survey on Evolutionary Constrained Multiobjective Optimization (2023, methodology): “They are self-adaptive penalty method (SP) [37], CDP [45], ε constrained method [109], SR [71], and MO [77].”