ビーバー行動最適化器:太陽光発電のパラメータ識別とエンジニアリングの問題のための新しいメタヒューリスティックアルゴリズム
Kaichen OuYang1, Dedai Wei2, Xinye Sha3
1Department of Mathematics, University of Science and Technology of China, Hefei 230026, China.
Journal of advanced research
|September 6, 2025
まとめ
新しいビーバー・ビハビオール・オプティマイザー (BBO) は,複雑なエンジニアリングと太陽光発電 (PV) システムの最適化に優れています. このバイオインスピレーションによるアルゴリズムは 効率的に最適な解決策を見つけ 伝統的な方法を上回ります
科学分野:
- コンピューター・インテリジェンス
- スワームインテリジェンス アルゴリズム
- バイオインスピレーションによるコンピューティング
背景:
- デジタル最適化は太陽光発電 (PV) システムとエンジニアリングに不可欠ですが,従来の方法は複雑で高次元の問題に直面しています.
- 既存の最適化技術は 非線形的な環境で効率的な解決策を見つけるのに苦労し 太陽エネルギーのような分野での進歩を妨げています
研究 の 目的:
- ビーバーのダム建設行動にインスパイアされた新しいビーバー行動最適化 (BBO) アルゴリズムを紹介します.
- 太陽光発電のパラメータの最適化に焦点を当てて,ベンチマークテスト機能と現実世界のエンジニアリング上のBBOの有効性を検証します.
主な方法:
- ビーバーの行動に基づいてモデル化されたBBOで,探検と搾取の段階が異なります.
- CEC 2017とCEC 2022のベンチマーク機能を様々な次元 (10〜100) でテストしました.
- BBOを3つの太陽光発電パラメータ識別問題と4つのエンジニアリング設計問題に適用し,他の11のアルゴリズムと比較した.
主要な成果:
- BBOはすべてのベンチマーク機能で優れたパフォーマンスを示し,太陽光発電とエンジニアリング最適化タスクで1位となりました.
- アルゴリズムは,ほとんどのシナリオで最先端の方法を上回り,堅固な収束と最小限の結果の分散を示しました.
- 統計的なテストは,BBOの業績改善の重要性を確認しました.
結論:
- ビーバー行動最適化器 (BBO) は,特に太陽光発電とエンジニアリングの設計において,複雑な最適化のための強力なツールとして検証されています.
- BBOのバイオインスピレーションによるアプローチは 探査と採掘のバランスをとって 競争上の優位性をもたらします
- この研究は,BBOが要求の高い最適化課題に効率的かつ正確な解決策を提供できる可能性を強調しています.
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