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高尔夫优化算法:一种基于博弈的新型元启发式算法及其在考虑弹性的能源分配问题中的应用

Golf Optimization Algorithm: A New Game-Based Metaheuristic Algorithm and Its Application to Energy Commitment Problem Considering Resilience.

作者信息

Montazeri Zeinab, Niknam Taher, Aghaei Jamshid, Malik Om Parkash, Dehghani Mohammad, Dhiman Gaurav

机构信息

Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz 7155713876, Iran.

School of Engineering & Technology, Central Queensland University, Rockhampton 4701, Australia.

出版信息

Biomimetics (Basel). 2023 Aug 24;8(5):386. doi: 10.3390/biomimetics8050386.

Abstract

In this research article, we uphold the principles of the No Free Lunch theorem and employ it as a driving force to introduce an innovative game-based metaheuristic technique named Golf Optimization Algorithm (GOA). The GOA is meticulously structured with two distinctive phases, namely, exploration and exploitation, drawing inspiration from the strategic dynamics and player conduct observed in the sport of golf. Through comprehensive assessments encompassing fifty-two objective functions and four real-world engineering applications, the efficacy of the GOA is rigorously examined. The results of the optimization process reveal GOA's exceptional proficiency in both exploration and exploitation strategies, effectively striking a harmonious equilibrium between the two. Comparative analyses against ten competing algorithms demonstrate a clear and statistically significant superiority of the GOA across a spectrum of performance metrics. Furthermore, the successful application of the GOA to the intricate energy commitment problem, considering network resilience, underscores its prowess in addressing complex engineering challenges. For the convenience of the research community, we provide the MATLAB implementation codes for the proposed GOA methodology, ensuring accessibility and facilitating further exploration.

摘要

在这篇研究文章中,我们秉持无免费午餐定理的原则,并将其作为驱动力引入一种名为高尔夫优化算法(GOA)的创新型基于游戏的元启发式技术。GOA经过精心构建,具有探索和利用两个独特阶段,其灵感来源于在高尔夫运动中观察到的战略动态和球员行为。通过涵盖52个目标函数和4个实际工程应用的综合评估,对GOA的有效性进行了严格检验。优化过程的结果表明,GOA在探索和利用策略方面都具有卓越的能力,能够有效地在两者之间达成和谐的平衡。与十种竞争算法的比较分析表明,在一系列性能指标上,GOA具有明显且在统计上显著的优势。此外,GOA成功应用于考虑网络弹性的复杂能源分配问题,突出了其在解决复杂工程挑战方面的实力。为方便研究界,我们提供了所提出的GOA方法的MATLAB实现代码,确保其可访问性并便于进一步探索。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22a5/10526449/f92a6569adcc/biomimetics-08-00386-g001.jpg

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