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

Electronic Research Archive, Journal Year: 2024, Volume and Issue: 32(7), P. 4659 - 4683

Published: Jan. 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>

Language: Английский

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

Communications in computer and information science, Journal Year: 2024, Volume and Issue: unknown, P. 255 - 269

Published: Sept. 21, 2024

Language: Английский

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

Mathematical and Computational Applications, Journal Year: 2024, Volume and Issue: 29(6), P. 103 - 103

Published: Nov. 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.

Language: Английский

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

Swarm and Evolutionary Computation, Journal Year: 2024, Volume and Issue: 92, P. 101829 - 101829

Published: Dec. 30, 2024

Language: Английский

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