Self-adaptive weight vector adjustment strategy for decomposition-based multi-objective differential evolution algorithm

Fan, Rui; Wei, Lixin; Li, Xin; Zhang, Jinlu; Fan, Zheng

doi:10.1007/s00500-020-04732-y

Cited by 13 publications

(4 citation statements)

References 34 publications

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“…At the same time, two mutation operators are used to enhance the search ability of the algorithm, and a chaotic strategy is introduced to update the parameters of DE. Fan et al [ 40 ] used alternative probability models in sampling to improve population diversity. And then combining the algorithm with DE and adopting an adaptive strategy to improve the convergence speed of the algorithm.…”

Section: Related Workmentioning

confidence: 99%

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A novel differential evolution algorithm with multi-population and elites regeneration

Cao,

Luan

2024

PLoS ONE

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Differential Evolution (DE) is widely recognized as a highly effective evolutionary algorithm for global optimization. It has proven its efficacy in tackling diverse problems across various fields and real-world applications. DE boasts several advantages, such as ease of implementation, reliability, speed, and adaptability. However, DE does have certain limitations, such as suboptimal solution exploitation and challenging parameter tuning. To address these challenges, this research paper introduces a novel algorithm called Enhanced Binary JADE (EBJADE), which combines differential evolution with multi-population and elites regeneration. The primary innovation of this paper lies in the introduction of strategy with enhanced exploitation capabilities. This strategy is based on utilizing the sorting of three vectors from the current generation to perturb the target vector. By introducing directional differences, guiding the search towards improved solutions. Additionally, this study adopts a multi-population method with a rewarding subpopulation to dynamically adjust the allocation of two different mutation strategies. Finally, the paper incorporates the sampling concept of elite individuals from the Estimation of Distribution Algorithm (EDA) to regenerate new solutions through the selection process in DE. Experimental results, using the CEC2014 benchmark tests, demonstrate the strong competitiveness and superior performance of the proposed algorithm.

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Section: Related Workmentioning

confidence: 99%

“…The second test is the Wilcoxon signed-rank test for multiple comparisons, which examines the differences among all algorithms across all functions. The Wilcoxon signed-rank test is employed with a significance level of 0.05 [ 40 ]. By utilizing the Wilcoxon signed-rank test, we analyze the variable R + .…”

Section: Numerical Experiments and Comparisonsmentioning

confidence: 99%

A novel differential evolution algorithm with multi-population and elites regeneration

Cao,

Luan

2024

PLoS ONE

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“…Furthermore, since the values of L and d determine the dimensions of w and b, the MOM in ( 10) is an uncertainty optimization problem. Hence, most common multiobjective optimization algorithms, such as strength pareto evolutionary algorithm, nondominated sorting genetic algorithm-II, multiobjective evolutionary algorithm, and multiple objective particle swarm optimization [39], [40], [41], are also not suitable for…”

Section: Multiobjective Optimization For Model Trainingmentioning

confidence: 99%

A Deep Learning Feature Fusion Based Health Index Construction Method for Prognostics Using Multiobjective Optimization

et al. 2023

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Degradation modeling and prognostics serve as the basis for system health management. Recently, various sensors provide plentiful monitoring data that can reflect the system status. A multitude of feature fusion techniques based on multisensor data have been proposed to generate a composite health index (HI) for prognostics, which can represent the underlying degradation mechanism. Most existing methods have used linear fusion models and neglected the practical requirements for HI construction, which are insufficient to reveal the nonlinear relations among features and difficult to obtain accurate HIs for complicated systems. This study proposes a novel feature fusion-based HI construction method with deep learning and multiobjective optimization. Multiple degradation features are fused by a deep neutral network (DNN). Several desired properties that the HIs should have for prognostics are adopted to formulate the objective functions of DNN training. To balance the spatial complexity and performance of the fusion model, a multiobjective optimization model is generated for training the DNN. Then, a generalized nonlinear Wiener process model is used to predict the remaining useful life with the resulted HIs. Finally, two cases are analyzed to illustrate the effectiveness and robustness of the proposed method.

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“…Considering economic objectives and robustness objectives, this paper uses the MOMDE (Multi-Objective Molecular Differential Evolution) algorithm to solve the model. By using the evolutionary variation mechanism based on inter-molecular forces, it can overcome the precocious convergence phenomenon effectively and achieve efficient depth optimization [30,31]. The flow chart of the solution process is shown in…”

Section: Solution Algorithmmentioning

confidence: 99%

Robust Optimal Scheduling of Integrated Energy Systems Considering the Uncertainty of Power Supply and Load in the Power Market

Zhao

Zeng

Wang³

et al. 2023

Energies

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The integrated energy system is a complex energy system that involves multi-stakeholder and multi-energy coordinated operations. The key to improving its scale and sustainable development is to construct a better-integrated energy system dispatching method which is suitable for the power market. However, the randomness of the supply side and load side of the integrated energy system brings further challenges to system planning and scheduling. Therefore, the optimal scheduling method of an integrated energy system considering the uncertainty of supply and demand in the market environment is studied in this paper. Firstly, the uncertainty models of the supply side and load side of the integrated energy system are established. Then, the optimal scheduling model based on robust chance constraint is established. The reserve capacity constraint is set as a chance constraint with a certain confidence level to maximize the system profit in the power market. Finally, simulations show that the proposed method not only guarantees the robustness of the system but also improves the economy of the system. The method provides ideas for exploring the development mechanism and strategy of integrated energy systems in the electricity market environment.

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Self-adaptive weight vector adjustment strategy for decomposition-based multi-objective differential evolution algorithm

Cited by 13 publications

References 34 publications

A novel differential evolution algorithm with multi-population and elites regeneration

A novel differential evolution algorithm with multi-population and elites regeneration

A Deep Learning Feature Fusion Based Health Index Construction Method for Prognostics Using Multiobjective Optimization

Robust Optimal Scheduling of Integrated Energy Systems Considering the Uncertainty of Power Supply and Load in the Power Market

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