Dynamic Transfer Reference Point-Oriented MOEA/D Involving Local Objective-Space Knowledge

Xie, Yingbo; Yang, Shengxiang; Wang, Ding; Qiao, Junfei; Yin, Baocai

doi:10.1109/tevc.2022.3140265

Cited by 26 publications

(4 citation statements)

References 61 publications

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“…Cao et al proposed a new decomposition-based multi-objective evolutionary algorithm, MOEA/D-PBO [9], which uses the multi-population heuristic PBO as the search engine to improve the performance of the algorithm. Xie et al proposed an improved DTR-MOEA/D algorithm [10], which introduce a dynamic transfer criteria of reference points according to the population density relationship to guide the population evolutionary.…”

Section: Introductionmentioning

confidence: 99%

A reference vector-interpolation-based multi-objective evolutionary algorithm

Guo,

2024

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023)

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Decomposition-based multi-objective evolutionary algorithm can achieve good convergence and diversity performance in solving many multi-objective optimisation problems, but it is difficult to ensure good diversity of pareto optimal solutions in some multi-objective optimisation problems with complex pareto fronts, such as fronts with large curvature or discontinue fronts. If pareto solutions are not uniformly distributed on the front surface, and there are many invalid reference vectors. To solve such problems, a multi-objective evolutionary algorithm based on reference vector interpolation is proposed in this paper. In the proposed method, the MOEAD algorithm is firstly used to obtain the initial pareto frontier, and the obtained pareto solution is clustered to obtain multiple clustering subsets. Then, is a nonlinear fitting is performed in the objective space to construct a pseudo-pareto frontier to approximate the subsets, and a new weight vector is generated by using uniform interpolation on the pseudo-pareto frontier. Finally, the new weight vector is used to update the current reference vector to push the population to move to the optimal pareto frontier and promote the convergence of the evolutionary process. In order to verify the effectiveness and advantage, the proposed algorithm is compared with nine existing multi-objective evolutionary algorithms on 17 test problems. The results indicate that the proposed algorithm can achieve better diversity of pareto-optimal solutions on most test problems.

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Section: Introductionmentioning

confidence: 99%

A reference vector-interpolation-based multi-objective evolutionary algorithm

Guo,

2024

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023)

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“…This is achieved by converting a multi-objective problem into multiple single-objective sub-problems that can be solved individually, and utilizing a neighborhood search strategy and weight vectors to ensure global convergence and diversity. Compared to other multi-objective optimization algorithms, such as the dominance-based multi-objective optimization algorithm (NSGA-II), MOEA/D has several advantages: the MOEA/D algorithm uses a decomposition strategy for solving, so it can effectively deal with high-dimensional problems [9][10][11]; approximation of Pareto optimal solutions by collaborative solving among subproblems [12,13]; and using a single reference point to guide solution generation reduces the number of depth evaluations and improves the efficiency of the algorithm by only evaluating the solution in the vicinity of the reference point in each problem [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Parameter Tuning Strategy for Decomposition-Based Multi-Objective Evolutionary Algorithms

Zheng,

Ning,

et al. 2024

Applied Sciences

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The penalty-based boundary cross-aggregation (PBI) method is a common decomposition method of the MOEA/D algorithm, but the strategy of using a fixed penalty parameter in the boundary cross-aggregation function affects the convergence of the populations to a certain extent and is not conducive to the maintenance of the diversity of boundary solutions. To address the above problems, this paper proposes a penalty boundary crossing strategy (DPA) for MOEA/D to adaptively adjust the penalty parameter. The strategy adjusts the penalty parameter values according to the state of uniform distribution of solutions around the weight vectors in the current iteration period, thus helping the optimization process to balance convergence and diversity. In the experimental part, we tested the MOEA/D-DPA algorithm with several MOEA/D improved algorithms on the classical test set. The results show that the MOEA/D with the DPA has better performance than the MOEA/D with the other decomposition strategies.

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“…With the rapid development of current control fields, [1][2][3] the requirement of advanced control algorithms is also increasing. As proposed in Reference 4, adaptive dynamic programming (ADP) is an elective control approach with strong adaptive ability.…”

Section: Introductionmentioning

confidence: 99%

Adaptive optimal tracking control with novel event‐triggered formulation for a type of nonlinear systems

Wang

Qiao

2022

Adaptive Control & Signal

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In this article, an event-based intelligent critic algorithm is developed to address the optimal tracking control problem for a type of discrete-time nonlinear systems. The nonlinear optimal tracking control design is replaced by solving the optimal regulation problem of the error system. Then, the generalized value iteration algorithm is employed to obtain the admissible tracking control law with off-line learning. Next, a novel triggering condition is designed to reduce the update times of the controller and improve the resource utilization. It is emphasized that this triggering condition is established based on the iteration of the time-triggered mechanism. Moreover, in order to realize the cost guarantee of the error system, the real cost function is proved to possess a predetermined upper bound. By analysis, it is shown that the error system is asymptotically stable while the tracking error and the sampling signal are uniformly ultimately bounded during the process of training neural networks. Finally, two examples are conducted to demonstrate the effectiveness of the proposed algorithm.

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Dynamic Transfer Reference Point-Oriented MOEA/D Involving Local Objective-Space Knowledge

Cited by 26 publications

References 61 publications

A reference vector-interpolation-based multi-objective evolutionary algorithm

A reference vector-interpolation-based multi-objective evolutionary algorithm

A Dynamic Parameter Tuning Strategy for Decomposition-Based Multi-Objective Evolutionary Algorithms

Adaptive optimal tracking control with novel event‐triggered formulation for a type of nonlinear systems

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