Adaptive weight vector assignment method for MOEA/D

Harada, Kei; Hiwa, Satoru; Hiroyasu, Tomoyuki

doi:10.1109/ssci.2017.8285195

Cited by 7 publications

(7 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then new weight vectors are reset with respect to each subset. To improve the performance of MOEA-ABD for many-objective optimization, MOEA/D-AWVAM resets weight vectors proportional to the search difficulty of each subset, which is roughly estimated as the number of associated non-dominated solutions [71].…”

Section: Delete-and-add Methodsmentioning

confidence: 99%

“…Regulated neighborhood TPEA-PBA [59], MaOEA/D-2ADV [60], EMOSA [61,62], AWA [63] AWA-SSCWA [64], MOEA/D-HD [65] Local niches of non-dominated solutions AWD-MOEA/D [66], FV-MOEA/D [67], AR-MOEA [68], MOEA-ABD [69] E-IM-MOEA [70], MOEA/D-AWVAM [71] A weight vector plays as the carrier of a subproblem in MOEA/D while the setting of weight vectors is essential to achieve a well balance between convergence and diversity [72]. However, it is arguable that using a set of evenly distributed weight vectors can be detrimental if the PF is not a regular simplex, such as disconnected, badly scaled and strong convex shapes shown in Fig.…”

Section: Subcategory Algorithm Name Core Techniquementioning

confidence: 99%

See 1 more Smart Citation

Decomposition Multi-Objective Evolutionary Optimization: From State-of-the-Art to Future Opportunities

2021

Preprint

View full text Add to dashboard Cite

Decomposition has been the mainstream approach in the classic mathematical programming for multi-objective optimization and multi-criterion decision-making. However, it was not properly studied in the context of evolutionary multi-objective optimization until the development of multi-objective evolutionary algorithm based on decomposition (MOEA/D). In this article, we present a comprehensive survey of the development of MOEA/D from its origin to the current state-of-the-art approaches. In order to be self-contained, we start with a step-by-step tutorial that aims to help a novice quickly get onto the working mechanism of MOEA/D. Then, selected major developments of MOEA/D are reviewed according to its core design components including weight vector settings, subproblem formulations, selection mechanisms and reproduction operators. Besides, we also overviews some further developments for constraint handling, computationally expensive objective functions, preference incorporation, and real-world applications. In the final part, we shed some lights on emerging directions for future developments.

show abstract

Section: Delete-and-add Methodsmentioning

confidence: 99%

Section: Subcategory Algorithm Name Core Techniquementioning

confidence: 99%

Decomposition Multi-Objective Evolutionary Optimization: From State-of-the-Art to Future Opportunities

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Harada et al [27] improved MOEA/D by adding a method of adaptively assigning weight vectors to subproblems. The method identifies the subproblems that are hard to search by using the EP and weight vectors, and then divides each of these subproblems into several subproblems to assign multiple weight vectors, implying that more computational resources are allocated in the direction of the subproblem with search difficulty.…”

Section: ) Methods Based On Adaptive Adjustmentmentioning

confidence: 99%

“…. , z m ) T , where apply a problem-specific repair algorithm on y to produce y ; required by each subproblem [27] or PF geometry shape [28], [29], user preferences-based [30], and hybrid [31]. Evolution operators consist of mating selection, reproduction and replacement.…”

Section: Research Directions Of Moea/dmentioning

confidence: 99%

“…Therefore, it is significant to allocate computational resources reasonably. Many algorithms have been proposed to assign weight vectors that are used to allocate different amounts of computational resources for subproblems according to some criteria, such as computational difficulties of solving subproblems [27], geometrical characteristics of a PF [29], and user preferences [30]. To realize the adaptive decomposition for a region [25] and overcome the defect of wasting resources due to fixed and uniform weight vectors, another potential method is to assign subpopulations with different sizes to their corresponding subproblems.…”

Section: F Computational Resources Allocationmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition: Variants, Challenges and Future Directions

2020

IEEE Access

View full text Add to dashboard Cite

Epigenetic opportunities for evolutionary computation

2023

View full text Add to dashboard Cite

Evolutionary computation is a group of biologically inspired algorithms used to solve complex optimization problems. It can be split into evolutionary algorithms, which take inspiration from genetic inheritance, and swarm intelligence algorithms, that take inspiration from cultural inheritance. However, much of the modern evolutionary literature remains relatively unexplored. To understand which evolutionary mechanisms have been considered, and which have been overlooked, this paper breaks down successful bioinspired algorithms under a contemporary biological framework based on the extended evolutionary synthesis, an extension of the classical, genetics focused, modern synthesis. Although the idea of the extended evolutionary synthesis has not been fully accepted in evolutionary theory, it presents many interesting concepts that could provide benefits to evolutionary computation. The analysis shows that Darwinism and the modern synthesis have been incorporated into evolutionary computation but the extended evolutionary synthesis has been broadly ignored beyond: cultural inheritance, incorporated in the sub-set of swarm intelligence algorithms, evolvability, through covariance matrix adaptation evolution strategy (CMA-ES), and multilevel selection, through multilevel selection genetic algorithm (MLSGA). The framework shows a gap in epigenetic inheritance for evolutionary computation, despite being a key building block in modern interpretations of evolution. This leaves a diverse range of biologically inspired mechanisms as low hanging fruit that should be explored further within evolutionary computation and illustrates the potential of epigenetic based approaches through the recent benchmarks in the literature.

show abstract

Adaptive weight vector assignment method for MOEA/D

Cited by 7 publications

References 21 publications

Decomposition Multi-Objective Evolutionary Optimization: From State-of-the-Art to Future Opportunities

Decomposition Multi-Objective Evolutionary Optimization: From State-of-the-Art to Future Opportunities

A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition: Variants, Challenges and Future Directions

Epigenetic opportunities for evolutionary computation

Contact Info

Product

Resources

About