MOEA/D with Adaptive IWO for Synthesizing Phase-Only Reconfigurable
Linear Arrays

Tan, Yanyan; Li, Shengtao; Fang, Xiaonan

doi:10.2174/1874123101509010125

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…The IGD metric value can reflect the convergence and diversity of the results obtained by algorithms. Let P * be a group of uniformly-distributed Pareto-optimal points in PF, as well as P be a group of points for PF approximation; then, the IGD metric can be defined as follows [10,37]:…”

Section: Comparison Algorithms and Performance Metricsmentioning

confidence: 99%

An Improved MOEA/D with Optimal DE Schemes for Many-Objective Optimization Problems

et al. 2017

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MOEA/D is a promising multi-objective evolutionary algorithm based on decomposition, and it has been used to solve many multi-objective optimization problems very well. However, there is a class of multi-objective problems, called many-objective optimization problems, but the original MOEA/D cannot solve them well. In this paper, an improved MOEA/D with optimal differential evolution (oDE) schemes is proposed, called MOEA/D-oDE, aiming to solve many-objective optimization problems. Compared with MOEA/D, MOEA/D-oDE has two distinguishing points. On the one hand, MOEA/D-oDE adopts a newly-introduced decomposition approach to decompose the many-objective optimization problems, which combines the advantages of the weighted sum approach and the Tchebycheff approach. On the other hand, a kind of combination mechanism for DE operators is designed for finding the best child solution so as to do the a posteriori computing. In our experimental study, six continuous test instances with 4-6 objectives comparing NSGA-II (nondominated sorting genetic algorithm II) and MOEA/D as accompanying experiments are applied. Additionally, the final results indicate that MOEA/D-oDE outperforms NSGA-II and MOEA/D in almost all cases, particularly in those problems that have complicated Pareto shapes and higher dimensional objectives, where its advantages are more obvious.

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Section: Comparison Algorithms and Performance Metricsmentioning

confidence: 99%

An Improved MOEA/D with Optimal DE Schemes for Many-Objective Optimization Problems

et al. 2017

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A Modification of MOEA/D for Solving Multi-Objective Optimization Problems

Zheng¹,

Tan²,

Gao³

et al. 2018

JACIII

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In this paper, a novel modified algorithm based on MOEA/D, abbreviated as mMOEA/D, is proposed for well solving the multi-objective optimization problems. Our proposed mMOEA/D inherits from MOEA/D. In mMOEA/D, a novel elastic weight vectors design method is introduced and adopted to make those weight vectors spread more widely. On the other hand, a flexible and efficient trail DE operator is designed and used in mMOEA/D for further enhancing the performance of MOEA/D. Three groups of experimental studies are carried out. Proposed mMOEA/D is compared with the four state-the-art multi-objective optimization evolutionary algorithms on solving the multi-objective optimization problems with many objectives, and the other is that mMOEA/D is compared with MOEA/D-DE, an improved version of MOEA/D, on solving the multi-objective optimization problems with complicated PS shapes. The versions of mMOEA/D with the improvement of weight vector and DE operator are compared with MOEA/D-DE to solve multi-objective optimization problems at last. The experimental results show that mMOEA/D performs the best on almost all test instances. In other words, our proposed modification of MOEA/D is effective.

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Retraction Notice: MOEA/D with Adaptive IWO for Synthesizing Phase-Only Reconfigurable Linear Arrays

Tan¹,

Li²,

Fang³

2016

TOCENGJ

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MOEA/D with Adaptive IWO for Synthesizing Phase-Only Reconfigurable Linear Arrays

Cited by 3 publications

References 21 publications

An Improved MOEA/D with Optimal DE Schemes for Many-Objective Optimization Problems

An Improved MOEA/D with Optimal DE Schemes for Many-Objective Optimization Problems

A Modification of MOEA/D for Solving Multi-Objective Optimization Problems

Retraction Notice: MOEA/D with Adaptive IWO for Synthesizing Phase-Only Reconfigurable Linear Arrays

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