A new mutation operator for differential evolution algorithm

Zuo, Mingcheng; Dai, Guangming; Peng, Lei

doi:10.1007/s00500-021-06077-6

Cited by 14 publications

(4 citation statements)

References 26 publications

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“…Among many paradigms of evolutionary algorithms, the differential evolution algorithm is very typical and widely used. In addition to the original guidance strategy, numerous enhanced guidance strategies have been proposed for the differential evolution algorithms, like the DE/current-to-pbest/1-X [55] . Existing differential guidance strategies can be divided into the following three categories: i) DE/best/1 and DE/best/2; ii) DE/rand/1 and DE/rand/2; and iii) DE/current-to-pbest/1.…”

Section: 𝜑 =mentioning

confidence: 99%

Process Knowledge-Guided Autonomous Evolutionary Optimization for Constrained Multiobjective Problems

Zuo

Gong

Wang

et al. 2024

IEEE Trans. Evol. Computat.

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Various real-world problems can be attributed to constrained multi-objective optimization problems. Although there are various solution methods, it is still very challenging to automatically select efficient solving strategies for constrained multi-objective optimization problems. Given this, a process knowledge-guided constrained multi-objective autonomous evolutionary optimization method is proposed. Firstly, the effects of different solving strategies on population states are evaluated in the early evolutionary stage. Then, the mapping model of population states and solving strategies is established. Finally, the model recommends subsequent solving strategies based on the current population state. This method can be embedded into existing evolutionary algorithms, which can improve their performances to different degrees. The proposed method is applied to 41 benchmarks and 30 dispatch optimization problems of the integrated coal mine energy system. Experimental results verify the effectiveness and superiority of the proposed method in solving constrained multi-objective optimization problems.

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Section: 𝜑 =mentioning

confidence: 99%

Process Knowledge-Guided Autonomous Evolutionary Optimization for Constrained Multiobjective Problems

Zuo

Gong

Wang

et al. 2024

IEEE Trans. Evol. Computat.

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“…We have used three algorithms from this family of methods, as several L-SHADEbased variants won various IEEE Competitions or achieved very good performances in comparison studies [94]. 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: 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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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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“…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

Self Cite

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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 new mutation operator for differential evolution algorithm

Cited by 14 publications

References 26 publications

Process Knowledge-Guided Autonomous Evolutionary Optimization for Constrained Multiobjective Problems

Process Knowledge-Guided Autonomous Evolutionary Optimization for Constrained Multiobjective Problems

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

Population Feasibility State Guided Autonomous Constrained Multi-Objective Evolutionary Optimization

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