On the use of random weights in MOEA/D

Li, Hui; Ding, Min; Deng, Jingda; Zhang, Qingfu

doi:10.1109/cec.2015.7256996

Cited by 26 publications

(16 citation statements)

References 10 publications

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“…The fixed weight vectors in MOEA/D may not work well when the PF of an MOP is irregular or complex. An MOEA/D with both fixed and random weight vectors (MOEA/D-RW) was suggested in [31]. The random search direction (weight vector) is considered to optimize the subproblems only if the solutions to all subproblems with fixed weight vectors have no improvement over several iterations.…”

Section: ) Other Weight Vector Generation Methodsmentioning

confidence: 99%

See 1 more Smart Citation

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

2020

IEEE Access

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Section: ) Other Weight Vector Generation Methodsmentioning

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%

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

2020

IEEE Access

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“…In MOEA/D-M2M [5], it uses K unit vectors to partition the objective space into K subregions, and generate K subpopulations to search each subregion in order to enhance the population diversity. However, the uniformly distributed weight vectors cannot produce uniformly distributed P-O solutions when the PF is complex [25] or irregular [26]. Therefore, several works have adopted alternate ways of decomposition, such as, in RVEA [6], a reference vector adaptation method is proposed, which can generate a uniformly weight vector according to the ranges of the objective values.…”

Section: B Objective Space Partition Methodsmentioning

confidence: 99%

Preference-Inspired Coevolutionary Algorithm Based on Differentiated Resource Allocation Strategy

Qiu

Wang

et al. 2020

IEEE Access

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Preference-inspired co-evolutionary algorithms (PICEAs) consider the target vectors as the preferences, and then use the domination relationship between the candidate solutions and target vectors to increase their selection pressure. However, the size of dominating objective space varies with the different positions of candidate solutions and it leads to the imbalance of the evolutionary ability of whole population. To solve this problem, this paper proposes a preference-inspired coevolutionary algorithm based on a differentiated allocation strategy (PICEAg-DS). First, it sets up an external archive to save the nondominated solutions and then extracts the convergence and diversity information from it. Second, it divides the objective space into several subspaces and designs a space distance operator to evaluate their optimization difficulty. Finally, it dynamically assigns the target vectors and guides more computational resource to the difficult to optimize subspaces, and thus drives the whole population evolution. To prove the advantages of differentiated resource allocation strategy, the PICEAg-DS is compared with two classic coevolutionary algorithms (PICEAg, CMOPSO) and two classic MOEAs based on resource allocation strategy (EAG-MOEAD, MOEAD-DRA). The experimental results show that PICEAg-DS performs better than the other algorithms on many WFG test problems. To further analysis the effectiveness of PICEAg-DS, compare it with two MOEAs based on domination relationship (NSGAII, SPEA2) and two MOEAs based on decomposition (RVEA, MOEA/D-M2M) on MOP and UF test suite. The experimental results show the PICEAg-DS has a better convergence than the other comparison algorithms, especially on 3-objective MOP6-7 and UF8-9.

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“…MOEA/D has attracted significant attention from researchers in the research area of Evolutionary Computation (EC) [2] since it was proposed. The performance of MOEA/D on MOPs has been improved by proposing novel weight vector generation methods [3], decomposition approaches [4], reproduction operator [5], [6], [7], mating selection [8] and replacement mechanism [9], etc. Furthermore, the decomposition-based framework has been extended to the constrained multiobjective optimization [10]- [12],…”

Section: Introductionmentioning

confidence: 99%

A Shift Vector Guided Multiobjective Evolutionary Algorithm Based on Decomposition for Dynamic Optimization

et al. 2020

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This paper presents a novel algorithm to deal with dynamic multiobjective optimization problems, in which the objective functions change over time. The algorithm adopts the decomposition framework to decompose the multiobjective optimization problems into a number of scalar optimization subproblems. For each subproblem, its respective solutions obtained in several former consecutive environments can form a moving trajectory over time. A shift vector guided prediction model is proposed, which samples three intermediately previous solutions of each subproblem to construct two shift vectors. The shift vectors use the weighted summation to generate a new shift vector as the forthcoming motion of the target solution. Then a new location in the later environment is estimated based on the current location and the newly generated shift vector. When detecting an environmental change, the multiobjective evolutionary algorithm based on decomposition will update the population using the predicted solutions by the proposed model. Empirical results demonstrate that our proposed algorithm is effective in tracking dynamic optimal solutions and shows great superiority comparing with state-of-the-art methods.

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On the use of random weights in MOEA/D

Cited by 26 publications

References 10 publications

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

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

Preference-Inspired Coevolutionary Algorithm Based on Differentiated Resource Allocation Strategy

A Shift Vector Guided Multiobjective Evolutionary Algorithm Based on Decomposition for Dynamic Optimization

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