Dynamic spiral updating whale optimization algorithm for solving optimal power flow problem

Wang, Fengxian; Feng, Shaozhi; Pan, Youmei; Zhang, Huanlong; Bi, Senlin; Zhang, Jiaxiang

doi:10.1007/s11227-023-05427-5

Cited by 4 publications

(2 citation statements)

References 66 publications

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“…Additionally, the algorithm's effectiveness may be impacted by the variability and unpredictability inherent in real-world supply chain environments. (13) Proposed a Dynamic Spiral Updating Whale Optimization Algorithm specifically designed for solving the optimal power flow problem, contributing to the optimization of power systems. Despite its potential benefits in optimizing power flow, this algorithm may face challenges in accurately modeling and adapting to dynamic changes in power grid conditions.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhanced Whale Optimization Algorithm for Multi-Objective Node Disjoint Routing

Hussain,

Sugumar

2024

IJST

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Objectives: The research goal is to identify node-disjoint pathways maximizing path lifetime, delivery ratio, etc. while establishing several node-disjoint paths to optimize routing and enhance interference reduction and PDR efficiency. Methods: In the MANET, the Enhanced Whale Optimization Algorithm (EWOA) is utilized to discover the most secure routing path. The dataset used in this research comprises simulated scenarios representing various network conditions and configurations, allowing for a comprehensive evaluation of the proposed work. The software employed for simulation and analysis includes an NS-3 network simulation tool, allowing the model to evaluate the performance of the proposed routing protocol in diverse MANET scenarios. The parameters considered during the evaluation include throughput, end-to-end delay, packet delivery ratio (PDR), and routing overhead. The modifications involve enhancing the routing algorithm to prioritize node-disjoint pathways, thus improving interference reduction, energy efficiency, and overall network performance. Findings: The EWOA for Multi-Objective Node Disjoint Routing demonstrates superior performance compared to existing routing protocols such as SRABC and OLSR across various key metrics. Specifically, in terms of end-to-end delay, EWOA consistently outperforms SRABC and OLSR by reducing delay by 1.3% and 4.7%, respectively. Additionally, EWOA achieves a higher packet delivery ratio, surpassing SRABC and OLSR by 4.3% and 0.4%, respectively. Moreover, EWOA exhibits higher throughput, with a throughput increase of 11.3% compared to SRABC and 6.2% compared to OLSR. Furthermore, EWOA demonstrates lower routing overhead, reducing overhead by 9.6% compared to SRABC and 3.1% compared to OLSR. These findings highlight the efficacy of EWOA in optimizing multi-objective routing in MANETs, offering improved network performance and efficiency compared to existing protocols. Novelty: The Multi-Objective Node Disjoint Routing Protocol with Enhanced Whale Optimization is utilized in MANET to choose the best route, reducing latency resulting from link failure and distributing traffic loads across multiple paths. Keywords: Optimization, Whale Optimization Algorithm (WOA), Optimized Link State Routing (OLSR), Secure Routing Algorithm Blockchain Technology (SRABC)), MANET

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Section: Literature Reviewmentioning

confidence: 99%

Enhanced Whale Optimization Algorithm for Multi-Objective Node Disjoint Routing

Hussain,

Sugumar

2024

IJST

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“…The Entire Network Losses (ENL) and the Emissions (EE) have not been taken into consideration krill herd algorithm (KHA) [31] 26-bus and IEEE 57-bus MDOPF FC, ENL, and voltage deviation functions EE has not taken into consideration Opposition-based learning whale optimization algorithm [32] IEEE 30 MDOPF FC function…”

Section: Algorithm Application Advantage Disadvantagementioning

confidence: 99%

Enhanced Kepler Optimization Method for Nonlinear Multi-Dimensional Optimal Power Flow

Alqahtani,

Almutairi,

Shaheen

et al. 2024

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Multi-Dimensional Optimal Power Flow (MDOPF) is a fundamental task in power systems engineering aimed at optimizing the operation of electrical networks while considering various constraints such as power generation, transmission, and distribution. The mathematical model of MDOPF involves formulating it as a non-linear, non-convex optimization problem aimed at minimizing specific objective functions while adhering to equality and inequality constraints. The objective function typically includes terms representing the Fuel Cost (FC), Entire Network Losses (ENL), and Entire Emissions (EE), while the constraints encompass power balance equations, generator operating limits, and network constraints, such as line flow limits and voltage limits. This paper presents an innovative Improved Kepler Optimization Technique (IKOT) for solving MDOPF problems. The IKOT builds upon the traditional KOT and incorporates enhanced local escaping mechanisms to overcome local optima traps and improve convergence speed. The mathematical model of the IKOT algorithm involves defining a population of candidate solutions (individuals) represented as vectors in a high-dimensional search space. Each individual corresponds to a potential solution to the MDOPF problem, and the algorithm iteratively refines these solutions to converge towards the optimal solution. The key innovation of the IKOT lies in its enhanced local escaping mechanisms, which enable it to explore the search space more effectively and avoid premature convergence to suboptimal solutions. Experimental results on standard IEEE test systems demonstrate the effectiveness of the proposed IKOT in solving MDOPF problems. The proposed IKOT obtained the FC, EE, and ENL of USD 41,666.963/h, 1.039 Ton/h, and 9.087 MW, respectively, in comparison with the KOT, which achieved USD 41,677.349/h, 1.048 Ton/h, 11.277 MW, respectively. In comparison to the base scenario, the IKOT achieved a reduction percentage of 18.85%, 58.89%, and 64.13%, respectively, for the three scenarios. The IKOT consistently outperformed the original KOT and other state-of-the-art metaheuristic optimization algorithms in terms of solution quality, convergence speed, and robustness.

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