Decomposition Multi-Objective Evolutionary Algorithm Based on Adaptive Neighborhood Adjustment Strategy

Wang, Liping; Xu, Mengna; Yu, Wei; Qiu, Qicang; Wu, Feng

doi:10.1109/access.2020.2990193

Cited by 10 publications

(6 citation statements)

References 25 publications

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“…MOEA/D uses the fixed neighborhood size T in the whole process which is difficult to balance the performance and efficiency of the algorithm. MOEA/D-ANA [13] proposes a dynamical adjustment strategy of the neighborhood size to solve the above difficulty. This strategy adjusts the neighborhood size in two different parts.…”

Section: Moea/d Performance With Different Neighborhood Sizesmentioning

confidence: 99%

“…We compare our algorithm MOEA/D-DNSA with MOEA/ D-D [8], MOEA/D-DE [9], MOEA/D-AWA [10], MOEA/ D-DRA [11], ENS-MOEA/D [12] and MOEA/D-ANA [13]. MOEA/D-DE uses differential evolution (DE) in MOEA/D, and it is good at handling complicated Pareto set shapes.…”

Section: Algorithms In Comparison and Parameter Settingsmentioning

confidence: 99%

“…However, ENS-MOEA/D can't balance the performance and efficiency because the algorithm just changes the neighborhood size by choosing between several values of neighborhood sizes. Wang et al proposed MOEA/D based on adaptive neighborhood adjustment strategy (MOEA/D-ANA) [13] to improve algorithm performance while being efficient. MOEA/D-ANA adjusts the neighborhood size in two parts.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the neighborhood size of different sub-problems in each generation should also be adjusted adaptively by using the crowded distance. Furthermore, this paper compares MOEA/D-DNSA with some state-of-the-art MOEA/D [8], [9], [10], [11], [12], [13], the results show that MOEA/D-DNSA runs faster in the premise of the optimal convergence and diversity.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Neighborhood Adjustment Strategy for Multi-Objective Evolutionary Algorithm Based on Decomposition

Cheng

Li²,

You³

2023

IEEE Access

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Multi-objective evolutionary algorithm based on decomposition (MOEA/D) has achieved great success in the field of evolutionary multi-objective optimization. It decomposes a multi-objective optimization problem into a number of scalar optimization sub-problems. Each sub-problem is optimized by using information from its neighboring sub-problems. Therefore, the neighborhood size of each sub-problem plays an important role in MOEA/D. Different neighborhood sizes are tested in this paper. Experimental results demonstrate that larger neighborhood size helps achieve better convergence and diversity with more CPU time and vice versa. MOEA/D uses constant neighborhood size during the whole process, and it is difficult to balance the convergence, diversity and running time. Therefore, this paper propose an algorithm based on MOEA/D. The algorithm adjusts the neighborhood size dynamically in different generations and different sub-problems to reduce the running time while the convergence and diversity of this algorithm are similar or better than other state-of-the-art algorithms. Compared to the original MOEA/D, experimental results show that adjusting the neighborhood size dynamically is a good way to reduce the running time significantly while maintaining the convergence and diversity. Furthermore, the algorithm proposed in this paper is compared with five state-of-the-art algorithms based on MOEA/D. The experimental results show that the proposed algorithm outperforms the others in efficiency while performs similarly in convergence and diversity.INDEX TERMS MOEA/D, dynamic neighborhood size, diversity and convergence, running time.

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Section: Moea/d Performance With Different Neighborhood Sizesmentioning

confidence: 99%

Section: Algorithms In Comparison and Parameter Settingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dynamic Neighborhood Adjustment Strategy for Multi-Objective Evolutionary Algorithm Based on Decomposition

Cheng

Li²,

You³

2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…MOEA/D is widely concerned and studied by many scholars at present for its high efficiency and inclusiveness, and numerous improved algorithms are proposed. For instance, Wang et al 16 propose an adaptive neighborhood adjustment strategy based on conventional MOEA/D, in which the neighborhood size of different subproblems can be changed adaptively with the increasing of iterations. Moreover, this adaptive adjustment strategy can also balance the convergence and diversity of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

An improved multiobjective evolutionary algorithm based on decomposition approach and its application in antenna array beam pattern synthesis

Liang

Fang

et al. 2021

Int J Numerical Modelling

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Antenna arrays can enhance the performance and reduce the overhead of the wireless communication systems. However, the beam pattern synthesis of antenna arrays are difficult problems since the optimization properties are usually trade‐offs that affect each other. In this paper, we formulate a multiobjective beam pattern optimization problem (MBPOP) to simultaneously reduce the maximum sidelobe level (SLL) and achieve the nulls of the antenna array beam pattern. The multiobjective evolutionary algorithm based on decomposition (MOEA/D) is a general and effective algorithm to solve the MOPs. However, it may be easy to lose population diversity and converge to local optimum. To overcome the issues above, we propose an improved MOEA/D (IMOEA/D) to deal with the formulated MBPOP. IMOEA/D introduces the normal distribution crossover operator (NDX), Lévy flight strategy and Euclidean distance‐based solution selection mechanism to enhance the performance of conventional MOEA/D to make it more suitable to solve the formulated MBPOP. Experiments are conducted and the results indicate that the proposed IMOEA/D has a better performance in terms of the convergence rate and population diversity compared to other algorithms for solving the formulated MBPOP.

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An enhanced decomposition-based multiobjective evolutionary algorithm with adaptive neighborhood operator and extended distance-based environmental selection

Yuan

Wang

2023

J Supercomput

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Decomposition Multi-Objective Evolutionary Algorithm Based on Adaptive Neighborhood Adjustment Strategy

Cited by 10 publications

References 25 publications

Dynamic Neighborhood Adjustment Strategy for Multi-Objective Evolutionary Algorithm Based on Decomposition

Dynamic Neighborhood Adjustment Strategy for Multi-Objective Evolutionary Algorithm Based on Decomposition

An improved multiobjective evolutionary algorithm based on decomposition approach and its application in antenna array beam pattern synthesis

An enhanced decomposition-based multiobjective evolutionary algorithm with adaptive neighborhood operator and extended distance-based environmental selection

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