A differential evolution algorithm with the guided movement for population and its application to interplanetary transfer trajectory design

Zuo, Mingcheng; Dai, Guangming; Peng, Lei; Tang, Zhe; Gong, Dunwei; Wang, Qinxia

doi:10.1016/j.engappai.2022.104727

Cited by 18 publications

(6 citation statements)

References 24 publications

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“…The efficient L-SHADE algorithms, include the three variants applied in this study, as well as SPS-L-SHADE-EIG that uses rotation invariant mechanism [107], L-SHADE-cnEpSin [108] that uses ensemble of sinusoidal approaches in parameter adaptation, L-SHADE-X [109] that introduces dimensionalitydependent spread parameter in distribution from which scaling factor is generated and an archive with offsprings that did not reach the main population despite having good performance, or OLSHADE-CS [110] that uses novel initialization and conservative selection mechanisms. In our opinion large successes of L-SHADE-based algorithms justify testing various their variants on different practical applications, as in [111]- [113].…”

Section: A Chosen Optimization Algorithmsmentioning

confidence: 96%

“…Various issues may affect this choice. Some of them are related to the flexibility of the specific algorithm, like balancing exploration and exploitation during search [46]- [49], fitness-landscape analysis [45], [50], [51], the approach to control parameter adaptation [52]- [57], or specification of the population size [58]- [61]. The other factors are related to the comparison settings that are set by the user, like the assumed maximum number of allowed function calls [62], [63], the specific benchmarks or criteria based on which the choice is being made [64]- [66], the statistical tests used in the evaluation [67], [68], or the nature of the decision space [69]- [70].…”

Section: Introductionmentioning

confidence: 99%

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Novel Air2water Model Variant for Lake Surface Temperature Modeling With Detailed Analysis of Calibration Methods

Piotrowski

Napiorkowski

Zhu

2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Air2water model is a simple and efficient tool for modelling surface water temperature in lakes based solely on the air temperature. In this paper we propose to modify the air2water model in such a way that different parameters would be associated with lake stratification of cold waters than with lake stratification of warm waters. The situation being a mix of both cold and warm waters is also considered. The model is tested on 22 lowland Polish lakes against two classical air2water variants. As the new air2water model variant is slightly more complicated than the basic versions, we focus on the importance of the choice of the calibration method. Each variant of the air2water model is calibrated with eight different optimization methods, which are also compared on various benchmark problems. We show that the proposed variant is superior to the classical air2water models on about 90% tested lakes, but only if the calibration approach is properly selected, which confirms the importance of the links between the model and appropriate optimization procedures. The proposed air2water variant performs well on various lowland lakes, with exception of large but shallow ones, probably due to the weak stratification of the shallow lakes.

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Section: A Chosen Optimization Algorithmsmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Novel Air2water Model Variant for Lake Surface Temperature Modeling With Detailed Analysis of Calibration Methods

Piotrowski

Napiorkowski

Zhu

2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Unlike the deep reinforcement learning generating regulatory strategies, evolutionary computation does not require known jig states, i.e., s t , but only a surrogate model evaluating candidate solutions and parameter boundaries is needed. Evolutionary algorithms, such as diferential evolution (DE) [16][17][18][19], are efective in solving the highdimensional optimization problems. However, to avoid large adjustment of equipment parameters, DE can only locally fne-tune the control parameters.…”

Section: Regulatory Strategies With Auto-diferential Evolutionmentioning

confidence: 99%

Deep Reinforcement Learning and Auto-Differential Evolution Co-Guided Coal Washing

Zuo

2024

Discrete Dynamics in Nature and Society

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Background. Coal washing is a complicated process and difficult to control, which has many controlling parameters with strong coupling relationship. It is still a challenge to realize the self-perception, self-adjustment, and self-evaluation of coal washing machine, improve the quality of coal washing, ensure production safety, and reduce labor cost. Methods. Through the intelligent transformation of jig, this paper proposes an intelligent washing method with cooperated deep reinforcement learning and evolutionary computation. First, it designs a fault warning method based on statistical analysis, helping to recover the normal running state of jig with manual maintenance. Then, it constructs a regulation strategy generation method with deep reinforcement learning supported by the fusion of artificial experience and historical data. Last, for the lack of monitoring data caused by poor communication quality and environment, the regulation strategy prediction method with evolutionary computation and surrogate model is proposed. Results. In practice, this method shows accurate fault warning accuracy and rapid cleaned coal ash adjustment response ability. Conclusions. This shows that the method proposed in this paper is of great significance for intelligent washing and can better cope with the special situation when the washing equipment sensing data are missing.

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“…To adjust the search direction, the multiple-operator method is a candidate. In this method, use of different reproduction operators leads to the generation of diverse types of solutions, aiding in adjusting the search direction of the population [3,[6][7][8]. However, the selection of reproduction operators also usually relies on subjective human experience, making it tough to determine the timing and scope of their application.…”

Section: Introductionmentioning

confidence: 99%

Population Feasibility State Guided Autonomous Constrained Multi-Objective Evolutionary Optimization

Zuo,

Xue

2024

Mathematics

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Many practical problems can be classified as constrained multi-objective optimization problems. Although various methods have been proposed for solving constrained multi-objective optimization problems, there is still a lack of research considering the integration of multiple constraint handling techniques. Given this, this paper combines the objective and constraint separation method with the multi-operator method, proposing a population feasibility state guided autonomous constrained evolutionary optimization method. This method first defines the feasibility state of the population based on both feasibility and ε feasibility of the solutions. Subsequently, a reinforcement learning model is employed to construct a mapping model between the population state and reproduction operators. Finally, based on the real-time population state, the mapping model is utilized to recommend the promising reproduction operator for the next generation. This approach demonstrates significant performance improvement for ε constrained mechanisms in constrained multi-objective optimization algorithms, and shows considerable advantages in comparison with state-of-the-art constrained multi-objective optimization algorithms.

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A differential evolution algorithm with the guided movement for population and its application to interplanetary transfer trajectory design

Cited by 18 publications

References 24 publications

Novel Air2water Model Variant for Lake Surface Temperature Modeling With Detailed Analysis of Calibration Methods

Novel Air2water Model Variant for Lake Surface Temperature Modeling With Detailed Analysis of Calibration Methods

Deep Reinforcement Learning and Auto-Differential Evolution Co-Guided Coal Washing

Population Feasibility State Guided Autonomous Constrained Multi-Objective Evolutionary Optimization

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