Many-Objective Evolutionary Algorithm Based On Decomposition With Random And Adaptive Weights

Farias, Lucas R. C.; Araújo, A.F.R.

doi:10.1109/smc.2019.8914005

Cited by 36 publications

(26 citation statements)

References 22 publications

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“…As a representative MOEA/D variant with an adaptive weight adjustment, MOEA/D-AWA periodically deletes weight vectors from the overly crowded regions and it adds new ones at the sparse areas guided by the external archive [42]. To improve the performance of MOEA/D-AWA for many-objective optimization problems, some variants have been proposed to use a uniformly random method to generate the initial weight vectors [43,44]; or emphasize more on promising yet under-exploited areas [45,75]; or enhance denser alternative weight vectors close to the valid weight vectors [46][47][48].…”

Section: Delete-and-add Methodsmentioning

confidence: 99%

“…Model-based adaptation methods T-MOEA/D [33], paλ-MOEA/D [34], apa-MOEA/D [35], DMOEA/D [36] Polynomial model MOEA/D-LTD [37,38] Parametric model MOEA/D-SOM [39], MOEA/D-GNG [40,41] Neural networks Delete-and-add MOEA/D-AWA [42], MOEA/D-URAW [43,44], AdaW [45] Less crowded or promising areas CLIA [46], MOEA/D-AM2M [47,48] RVEA* [49], [50], iRVEA [51], OD-RVEA [52], EARPEA [53], g-DBEA [54] Away from the promising areas A-NSGA-III [55], A 2 -NSGA-III [56], AMOEA/D [57], MOEA/D-TPN [58],…”

Section: Subcategory Algorithm Name Core Techniquementioning

confidence: 99%

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

2021

Preprint

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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.

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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

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“…Then, we start by analyzing the performance of the several algorithms for the trajectory optimization of a point-point motion, including the proposed INSGA-II, MO-INSGA-II [34], success history-based adaptive multi-objective differential evolution with whale optimization (SHAMODE-WO) [35], IMOPSO [36], many-objective evolutionary algorithm based on decomposition with random and adaptive weights (MOEA/ D-URAW) [37] and IMODE [38]. In a second phase, the composite polynomial with the quintic B-splines approach are compared to evaluate its effectiveness.…”

Section: Numerical Examplementioning

confidence: 99%

Multi-objective Trajectory Planning Method based on the Improved Elitist Non-dominated Sorting Genetic Algorithm

Wang

Shuai

et al. 2022

Chin. J. Mech. Eng.

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Robot manipulators perform a point-point task under kinematic and dynamic constraints. Due to multi-degree-of-freedom coupling characteristics, it is difficult to find a better desired trajectory. In this paper, a multi-objective trajectory planning approach based on an improved elitist non-dominated sorting genetic algorithm (INSGA-II) is proposed. Trajectory function is planned with a new composite polynomial that by combining of quintic polynomials with cubic Bezier curves. Then, an INSGA-II, by introducing three genetic operators: ranking group selection (RGS), direction-based crossover (DBX) and adaptive precision-controllable mutation (APCM), is developed to optimize travelling time and torque fluctuation. Inverted generational distance, hypervolume and optimizer overhead are selected to evaluate the convergence, diversity and computational effort of algorithms. The optimal solution is determined via fuzzy comprehensive evaluation to obtain the optimal trajectory. Taking a serial-parallel hybrid manipulator as instance, the velocity and acceleration profiles obtained using this composite polynomial are compared with those obtained using a quintic B-spline method. The effectiveness and practicability of the proposed method are verified by simulation results. This research proposes a trajectory optimization method which can offer a better solution with efficiency and stability for a point-to-point task of robot manipulators.

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“…The purpose of conducting simulation is to verify the search capability and convergence speed of the proposed INSGA-II as well as validity and competency of the composite polynomial approach for creating trajectory. In this section, taking a serial-parallel hybrid manipulator as instance [33], we start by analyzing the performance of the proposed INSGA-II, MO-INSGA-II [34], success historybased adaptive multi-objective differential evolution with whale optimization (SHAMODE-WO) [35], IMOPSO [36], many-objective evolutionary algorithm based on decomposition with random and adaptive weights (MOEA/D-URAW) [37] and IMODE [38] for the trajectory optimization of a point-point motion. In a second phase, we compared the composite polynomial with the quintic B-splines approach to evaluate its effectiveness.…”

Section: Numerical Examplementioning

confidence: 99%

A Multi-objective Trajectory Planning Method Based on the Improved Elitist Non-dominated Sorting Genetic Algorithm

Wang¹,

Li²,

Shuai³

et al. 2020

Preprint

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A multi-objective trajectory planning approach based on an improved elitist non-dominated sorting genetic algorithm (INSGA-II) is proposed. Trajectory function is planned with a new composite polynomial that by combining of quintic polynomials with cubic Bezier curves. Then, an INSGA-II, by introducing three genetic operators: ranking group selection (RGS), direction-based crossover (DBX) and adaptive precision-contrallable mutation (APCM), is developed to optimize travelling time and torque fluctuation. Inverted generational distance, hypervolume and optimizer overhead are selected to evaluate the convergence, diversity and computational effort of algorithms. The optimal solution is determined via fuzzy comprehensive evaluation to obtain the optimal trajectory. Taking a serial-parallel hybrid manipulator as instance, the velocity and acceleration profiles obtained using this composite polynomial are compared with those obtained using a quintic B-spline method. The effectiveness and practicability of the proposed method are verified by simulation results.

show abstract

Many-Objective Evolutionary Algorithm Based On Decomposition With Random And Adaptive Weights

Cited by 36 publications

References 22 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

Multi-objective Trajectory Planning Method based on the Improved Elitist Non-dominated Sorting Genetic Algorithm

A Multi-objective Trajectory Planning Method Based on the Improved Elitist Non-dominated Sorting Genetic Algorithm

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