Constrained Test Problems for Multi-objective Evolutionary Optimization

By Kalyanmoy Deb, Amrit Pratap, T. Meyarivan. International Conference on Evolutionary Multi-Criterion Optimization, 2001

385 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/88ce329c931b9f98d987a18947f1eb60183875ba

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Citations in the corpus

Multiobjective Multifactorial Optimization in Evolutionary Multitasking (2017, background): “As prescribed in [51], the constraints are imposed as follows:”
Controlled Elitist Non-dominated Sorting Genetic Algorithms for Better Convergence (2001, neutral): “1 Multi-obje tive Rastrigin's Problem This problem is identi al to the ZDT4 fun tion, introdu ed elsewhere [12℄: ZDT4 :8>><>>>:Minimize f1(x) = x1 Minimize f2(x) = g(x) h1 px1=g(x)i g(x) = 91 +P10 i=2 x2i 10 os(4 xi) x1 2 [0; 1℄; xi 2 [ 5; 5℄; i = 2; : : : ; 10: (3)”
Scalable Test Problems for Evolutionary Multiobjective Optimization (2005, background): “An increase in dimensionality of the objective space also causes a large proportion of a random initial population to be non-dominated to each other (Deb, 2001), thereby reducing the e(cid:11)ect of selection operator in an MOEA.”
An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point Based Nondominated Sorting Approach, Part II: Handling Constraints and Extending to an Adaptive Approach (2014, methodology): “But, in the presence of constraints, we follow the constraintdomination principle adopted in NSGA-II [13] using the ideas from [18], [19].”
Evolutionary Constrained Multiobjective Optimization: Test Suite Construction and Performance Comparisons (2019, background): “Most of them have disconnected or discrete constrained PFs. Note that, CTPs have large feasibility ratios.”
GDE3: the third evolution step of generalized differential evolution (2005, methodology): “9, Pm = 1/D, ?IC = 20, and ?lm = 20 used in [10].”
Towards a New Evolutionary Computation - Advances in the Estimation of Distribution Algorithms (2006, methodology): “For example, suppose some prior marginal distributions are specified, and among prior distributions with these marginals the most non-informative distribution is sought [12, 13].”
Handling Constrained Multiobjective Optimization Problems With Constraints in Both the Decision and Objective Spaces (2019, methodology): “The construction of problems 1–3 and 8–10 is inspired by the method introduced in [8], in which only the objective constraints are considered.”
Evolutionary Algorithms for Solving Multi-Objective Problems (2002, background): “24 MOP-GX disconnected P Ftrue regions (Deb et al., 2001a; Deb and Meyarivan, 2000) 12S”
An efficient multiobjective differential evolution algorithm for engineering design (2009, methodology): “The details of these problems are described in Table 1, where OSY, SRN, and TNK are chosen from [6], Kita and Tamaki are chosen from [24], DTLZ8 and DTLZ9 are selected from [7].”
A Survey on Evolutionary Constrained Multiobjective Optimization (2023, background): “…many benchmark test functions have been proposed, including the SRN test function [133], TNK test [134], OSY test function [135], CTP test set [136], CF test set [137], NCTP test set [138], C-DTLZ test set [139], CMOP test set[140], LIR-CMOP test set [22], MW test set [18], DOC test set [96],…”