Incorporation of a decision space diversity maintenance mechanism into MOEA/D for multi-modal multi-objective optimization

Hu, Chenxu; Ishibuchi, Hisao

doi:10.1145/3205651.3208224

Cited by 31 publications

(7 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to MOEA/D-AD [17], a variant of MOEA/D for MMOPs is proposed in [47]. The MOEA/D variant assigns K individuals to each subproblem.…”

Section: J Originality Of Adamentioning

confidence: 99%

See 1 more Smart Citation

A Framework to Handle Multimodal Multiobjective Optimization in Decomposition-Based Evolutionary Algorithms

Tanabe¹,

Ishibuchi

2020

IEEE Trans. Evol. Computat.

Self Cite

View full text Add to dashboard Cite

Multi-modal multi-objective optimization is to locate (almost) equivalent Pareto optimal solutions as many as possible. While decomposition-based evolutionary algorithms have good performance for multi-objective optimization, they are likely to perform poorly for multi-modal multi-objective optimization due to the lack of mechanisms to maintain the solution space diversity. To address this issue, this paper proposes a framework to improve the performance of decomposition-based evolutionary algorithms for multi-modal multi-objective optimization. Our framework is based on three operations: assignment, deletion, and addition operations. One or more individuals can be assigned to the same subproblem to handle multiple equivalent solutions. In each iteration, a child is assigned to a subproblem based on its objective vector, i.e., its location in the objective space. The child is compared with its neighbors in the solution space assigned to the same subproblem. The performance of improved versions of six decomposition-based evolutionary algorithms by our framework is evaluated on various test problems regarding the number of objectives, decision variables, and equivalent Pareto optimal solution sets. Results show that the improved versions perform clearly better than their original algorithms.

show abstract

“…In addition to MOEA/D-AD [17], a variant of MOEA/D for MMOPs is proposed in [47]. The MOEA/D variant assigns K individuals to each subproblem.…”

Section: J Originality Of Adamentioning

confidence: 99%

“…The fitness value is based on the PBI function value and two distance values in the solution space. The main disadvantage of the MOEA/D variant in [47] is the difficulty in finding a proper K value. Since the number of equivalent Pareto optimal solution subsets is unknown a priori, fine-tuning of K is necessary for a given problem.…”

Section: J Originality Of Adamentioning

confidence: 99%

A Framework to Handle Multimodal Multiobjective Optimization in Decomposition-Based Evolutionary Algorithms

Tanabe¹,

Ishibuchi

2020

IEEE Trans. Evol. Computat.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Two variants of MOEA/D [34] for MMOPs are proposed in [35], [36]. MOEA/D decomposes an M -objective problem into N single-objective subproblems using a set of weight vectors, assigning a single individual to each subproblem.…”

Section: Mmeasmentioning

confidence: 99%

“…The MOEA/D algorithm presented in [35] assigns K individuals to each subproblem. The selection is conducted based on a fitness value combining the PBI function value [34] and two distance values in the solution space.…”

Section: Mmeasmentioning

confidence: 99%

A Review of Evolutionary Multi-modal Multi-objective Optimization

Tanabe,

Ishibuchi

2020

Preprint

Self Cite

View full text Add to dashboard Cite

Multi-modal multi-objective optimization aims to find all Pareto optimal solutions including overlapping solutions in the objective space. Multi-modal multi-objective optimization has been investigated in the evolutionary computation community since 2005. However, it is difficult to survey existing studies in this field because they have been independently conducted and do not explicitly use the term "multi-modal multi-objective optimization". To address this issue, this paper reviews existing studies of evolutionary multi-modal multi-objective optimization, including studies published under names that are different from "multi-modal multi-objective optimization". Our review also clarifies open issues in this research area.

show abstract

“…To find multiple equivalent Pareto optimal solutions for each objective vector, a well-converged population with good diversity in both the objective and decision spaces is needed [5]. For this aim, some algorithms group the solutions in the decision space [6], [7], some measure the crowding distance of each solution in both the objective and decision spaces [4], [8], some preserve the diversity of one population in the objective space and preserve the diversity of another population in the decision space [5], [9], and yet others maintain multiple solutions with good diversity in the decision space for each single-objective subproblem in decomposition based MOEAs [10], [11].…”

Section: Introductionmentioning

confidence: 99%

A Multipopulation Evolutionary Algorithm for Solving Large-Scale Multimodal Multiobjective Optimization Problems

Tian

Liu

Zhang

et al. 2021

IEEE Trans. Evol. Computat.

View full text Add to dashboard Cite

Multi-modal multi-objective optimization problems (MMOPs) widely exist in real-world applications, which have multiple equivalent Pareto optimal solutions that are similar in the objective space but totally different in the decision space. While some evolutionary algorithms (EAs) have been developed to find the equivalent Pareto optimal solutions in recent years, they are ineffective to handle large-scale MMOPs having a large number of variables. This paper thus proposes an EA for solving large-scale MMOPs with sparse Pareto optimal solutions, i.e., most variables in the optimal solutions are zero. The proposed algorithm explores different regions of the decision space via multiple subpopulations, and guides the search behavior of the subpopulations via adaptively updated guiding vectors. The guiding vector for each subpopulation not only provides efficient convergence in the huge search space, but also differentiates its search direction from others to handle the multi-modality. While most existing evolutionary algorithms solve MMOPs with 2 to 7 decision variables, the proposed algorithm is showed to be effective for benchmark MMOPs with up to 500 decision variables. Moreover, the proposed algorithm also produces a better result than state-of-the-art methods for neural architecture search.

show abstract

Incorporation of a decision space diversity maintenance mechanism into MOEA/D for multi-modal multi-objective optimization

Cited by 31 publications

References 13 publications

A Framework to Handle Multimodal Multiobjective Optimization in Decomposition-Based Evolutionary Algorithms

A Framework to Handle Multimodal Multiobjective Optimization in Decomposition-Based Evolutionary Algorithms

A Review of Evolutionary Multi-modal Multi-objective Optimization

A Multipopulation Evolutionary Algorithm for Solving Large-Scale Multimodal Multiobjective Optimization Problems

Contact Info

Product

Resources

About