A power generation accumulation-based adaptive chaotic differential evolution algorithm for wind turbine placement problems

Electronic Research Archive, Год журнала: 2024, Номер 32(7), С. 4659 - 4683

Опубликована: Янв. 1, 2024

<p>The focus on clean energy has significantly increased in recent years, emphasizing eco-friendly sources like solar, wind, hydropower, geothermal, and biomass energy. Among these, wind energy, utilizing the kinetic from is distinguished by its economic competitiveness environmental benefits, offering scalability minimal operational emissions. It requires strategic turbine placement within farms to maximize conversion efficiency, a complex task involving analysis of patterns, spacing, technology. This traditionally been tackled meta-heuristic algorithms, which face challenges balancing local exploitation with global exploration integrating problem-specific knowledge into search mechanism. To address these challenges, an innovative power generation accumulation-based adaptive chaotic differential evolution algorithm (ACDE) proposed, enhancing conventional approach adjustment strategy based tournament selection. aimed prioritize energy-efficient positions improve population diversity, thereby overcoming limitations existing algorithms. Comprehensive experiments varying rose configurations demonstrated ACDE's superior performance showcasing potential optimizing for enhanced production. The farm layout optimization competition hosted Genetic Evolutionary Computation Conference provided comprehensive set layouts. dataset was utilized further validate results unequivocally demonstrate superiority ACDE when tackling problems.</p>

Язык: Английский

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Differential Evolution for Classification: A Novel Classifier Technique in Data Mining

Опубликована: Фев. 28, 2024

In the realm of Data Mining, pursuit innovative classification methodologies remains crucial for advancing robust techniques in handling complex and diverse datasets. This paper explores application Differential Evolution (DE), a powerful optimization algorithm, as unique effective optimization-based standalone classifier. Our exploration focused on harnessing DE's intrinsic capabilities, adapting it into classifier while preserving its distinctiveness. The fundamental principle DE involves iteratively optimizing center point each class using operators, which are specialized mechanisms exploring refining solutions, utilizing these optimized points making predictions. effectiveness our model was evaluated ten datasets from UCI Machine Learning Repository compared against three other methods: KNN, ZMP, BPNN. Experimental results underscore competitive performance proposed model, emphasizing potential effectively addressing challenges.

Язык: Английский

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An Interior Illuminance Prediction Model Based on Differential Evolution-Gaussian Fitting

Communications in computer and information science, Год журнала: 2024, Номер unknown, С. 255 - 269

Опубликована: Сен. 21, 2024

Язык: Английский

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An Experimental Comparison of Self-Adaptive Differential Evolution Algorithms to Induce Oblique Decision Trees

Mathematical and Computational Applications, Год журнала: 2024, Номер 29(6), С. 103 - 103

Опубликована: Ноя. 9, 2024

This study addresses the challenge of generating accurate and compact oblique decision trees using self-adaptive differential evolution algorithms. Although traditional tree induction methods create explainable models, they often fail to achieve optimal classification accuracy. To overcome these limitations, other strategies, such as those based on evolutionary computation, have been proposed in literature. In particular, we evaluate use variants evolve a population encoded real-valued vectors. Our proposal includes (1) an alternative initialization strategy that reduces redundant nodes (2) fitness function penalizes excessive leaf nodes, promoting smaller more trees. We perform comparative performance analysis variants, showing while exhibit similar statistical behavior, Single-Objective real-parameter optimization (jSO) method produces most is second best compactness. The findings highlight potential algorithms improve effectiveness machine learning applications.

Язык: Английский

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Differential Evolution with multi-stage parameter adaptation and diversity enhancement mechanism for numerical optimization

Swarm and Evolutionary Computation, Год журнала: 2024, Номер 92, С. 101829 - 101829

Опубликована: Дек. 30, 2024

Язык: Английский

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