Difficulty Adjustable and Scalable Constrained Multiobjective Test Problem Toolkit

Abstract

Multiobjective evolutionary algorithms (MOEAs) have progressed significantly in recent decades, but most of them are designed to solve unconstrained multiobjective optimization problems. In fact, many real-world multiobjective problems contain a number of constraints. To promote research on constrained multiobjective optimization, we first propose a problem classification scheme with three primary types of difficulty, which reflect various types of challenges presented by real-world optimization problems, in order to characterize the constraint functions in constrained multiobjective optimization problems (CMOPs). These are feasibility-hardness, convergence-hardness, and diversity-hardness. We then develop a general toolkit to construct difficulty adjustable and scalable CMOPs (DAS-CMOPs, or DAS-CMaOPs when the number of objectives is greater than three) with three types of parameterized constraint functions developed to capture the three proposed types of difficulty. In fact, the combination of the three primary constraint functions with different parameters allows the construction of a large variety of CMOPs, with difficulty that can be defined by a triplet, with each of its parameters specifying the level of one of the types of primary difficulty. Furthermore, the number of objectives in this toolkit can be scaled beyond three. Based on this toolkit, we suggest nine difficulty adjustable and scalable CMOPs and nine CMaOPs, to be called DAS-CMOP1-9 and DAS-CMaOP1-9, respectively. To evaluate the proposed test problems, two popular CMOEAs—MOEA/D-CDP (MOEA/D with constraint dominance principle) and NSGA-II-CDP (NSGA-II with constraint dominance principle) and two popular constrained many-objective evolutionary algorithms (CMaOEAs)—C-MOEA/DD and C-NSGA-III—are used to compare performance on DAS-CMOP1-9 and DAS-CMaOP1-9 with a variety of difficulty triplets, respectively. The experimental results reveal that mechanisms in MOEA/D-CDP may be more effective in solving convergence-hard DAS-CMOPs, while mechanisms of NSGA-II-CDP may be more effective in solving DAS-CMOPs with simultaneous diversity-, feasibility-, and convergence-hardness. Mechanisms in C-NSGA-III may be more effective in solving feasibility-hard CMaOPs, while mechanisms of C-MOEA/DD may be more effective in solving CMaOPs with convergence-hardness. In addition, none of them can solve these problems efficiently, which stimulates us to continue to develop new CMOEAs and CMaOEAs to solve the suggested DAS-CMOPs and DAS-CMaOPs.

Push and pull search embedded in an M2M framework for solving constrained multi-objective optimization problems

“…In order to further discuss the advantages of the proposed PPS-M2M in solving CMOPs, we plot non-dominated solutions achieved by each algorithm on LIR-CMOP2, LIR-CMOP7 and LIR-CMOP11 with the median HV values. The feasible and infeasible regions of LIR-CMOP2, LIR-CMOP7 and LIR-CMOP11, corresponding to three different types of difficulties [42], are plotted in Fig. 3.…”

MOEA/D with angle-based constrained dominance principle for constrained multi-objective optimization problems

“…To evaluate the performance of the proposed MOEA/D-ACDP, 14 constrained multi-objective test problems with large infeasible regions in the objective space are used [27,28].…”

Handling Constrained Multi-objective Optimization By Ignoring Constraints and Using Two Evolutionary Frameworks

“…In Formula (1), g i (x) ≤ 0 defines the i-th of the p inequality constraints, and h j (x) = 0 defines the j-th of the q equality constraints. According to [4], CMOPs with more than three objectives are known as constrained many-objective optimization problems (CMaOPs). From the perspective of decision makers, feasible solutions are naturally emphasized over infeasible ones.…”

“…Moreover, as shown in Fig. 2, each type of CMOPs may have infeasible regions blocking the way of converging towards the CPF [4], [5]. To emphasize this feature, CMOPs with infeasible barriers are called Type-I ′ , Type-II ′ and Type-III ′ problems in this paper.…”

“…• We demonstrate the effectiveness and efficiency of the proposed algorithm by conducting a comprehensively comparative experimental study, in which CIC-MOEA/D is compared with several state-of-the-art CMOEAs on a number of both artificial and practical CMOPs, including MW test problems [38], DC-DTLZ test problems [5], DAS-CMOPs/DAS-CMaOPs [4] and the real-world constrained optimal software product selection (OSPS) problem [39]. All these problems have been proposed recently and can be classified based on our taxonomy.…”