用于降低有功功率损耗和增强电压稳定性的新型皇后萨拉尼优化算法

Novel empress SARANI optimization algorithm for active power loss reduction and voltage stability enhancement.

作者信息

Kanagasabai Lenin

机构信息

Prasad V.Potluri Siddhartha Institute of Technology, Kanuru, Vijayawada, Andhra Pradesh, 520007, India.

出版信息

Heliyon. 2024 Oct 9;10(22):e38984. doi: 10.1016/j.heliyon.2024.e38984. eCollection 2024 Nov 30.

DOI:10.1016/j.heliyon.2024.e38984

PMID:39748959

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11693907/

Abstract

This paper proposes Pomarine jaeger Optimization (PJO) algorithm, Tiger hunting Optimization (THO) Algorithm, Desert Reynard and Vixen Inspired Optimization (DRVIO) Algorithm, Lonchodidae optimization (LO) algorithm, Caracal optimization (CO) algorithm, Barasingha optimization (BO) algorithm, Amur leopard optimization (AO) algorithm and Empress SARANI Optimization Algorithm to solve the active power loss reduction problem. Regular actions of Pomarine jaeger have been emulated to model the PJO procedure. In THO algorithm, how the Tiger moves to capture the prey is imitated and formulated. In DRVIO algorithm, Desert Reynard and Vixen burrowing capability and spurt tactic from desolate slayers are imitated to formulate the algorithm. LO algorithm emulates the physiognomies of convergent progression, track reliance, populace development and rivalry in the growth of the Lonchodidae populace in environment. In CO approach, Caracal assaults the designated quarry and then quests the quarry in a dashing procedure. BO algorithm stimulated by the Barasingha existence capability in the slayer subjugated atmosphere. AO algorithm imitates the Amur leopard behaviour. Movement paths, stalking, breeding and death are the some phases in the Amur leopard life cycle. Empress SARANI Optimization Algorithm is designed by integrating Parastylotermes Empress inspired optimization (PEIO) algorithm, Dryocopus martius optimization (DMO) algorithm, Ostrya Carpinifolia Search Optimization (OCSO) Algorithm, Hermitage Activities Inspired optimization (HAIO) algorithm with SARANI algorithm. Validity of Empress SARANI Optimization Algorithm is verified in 24 benchmark functions, IEEE and Practical systems. Real power loss (MW) obtained by projected algorithms for is PJO-21.99, THO-22.79, DRVIO-21.79, LO-23.16, CO-23.92, BO-22.81, AO- 24.89 and For is PJO-395.153, THO-397.398, DRVIO-394.208, LO-398.192, CO-398.397, BO-395.209, AO-399.884 and . For is PJO-336.108, THO-339.563, DRVIO-339.099, LO-340.164, CO-340.592, BO 338.906, AO-342.184 and . For is PJO-29. 008, THO-30. 929, DRVIO-28. 519, LO-31.265, CO-31. 893, BO-29.872, AO-32.899, .

摘要

本文提出了矛隼优化（PJO）算法、猎虎优化（THO）算法、沙漠狐和雌狐启发优化（DRVIO）算法、戴胜优化（LO）算法、狞猫优化（CO）算法、沼鹿优化（BO）算法、东北豹优化（AO）算法和皇后萨拉尼优化算法来解决降低有功功率损耗问题。已模拟矛隼的常规行为来建立PJO过程模型。在THO算法中，模仿并制定了老虎捕捉猎物的移动方式。在DRVIO算法中，模仿沙漠狐和雌狐的挖掘能力以及荒漠杀手的冲刺策略来制定该算法。LO算法模拟戴胜种群在环境中的趋同进化、轨迹依赖、种群发展和竞争的特征。在CO方法中，狞猫攻击指定猎物，然后以快速的过程追捕猎物。BO算法受到沼鹿在杀手征服环境中的生存能力的启发。AO算法模仿东北豹的行为。移动路径、跟踪、繁殖和死亡是东北豹生命周期中的一些阶段。皇后萨拉尼优化算法是通过将受白蚁皇后启发的优化（PEIO）算法、大斑啄木鸟优化（DMO）算法、铁木搜索优化（OCSO）算法、隐居活动启发优化（HAIO）算法与萨拉尼算法相结合而设计的。在24个基准函数、IEEE和实际系统中验证了皇后萨拉尼优化算法的有效性。所提出算法获得的实际功率损耗（MW）对于……为PJO - 21.99，THO - 22.79，DRVIO - 21.79，LO - 23.16，CO - 23.92，BO - 22.81，AO - 24.89以及对于……为PJO - 395.153，THO - 397.398，DRVIO - 394.208，LO - 398.192，CO - 398.397，BO - 395.209，AO - 399.884以及对于……为PJO - 336.108，THO - 339.563，DRVIO - 339.099，LO - 340.164，CO - 340.592，BO 338.906，AO - 342.184以及对于……为PJO - 29.008，THO - 30.929，DRVIO - 28.519，LO - 31.265，CO - 31.893，BO - 29.872，AO - 32.899，……