A Social Group Optimization Algorithm Using the Laplace Operator for the Economic Dispatch Problem

Processes, Год журнала: 2025, Номер 13(2), С. 405 - 405

Опубликована: Фев. 4, 2025

The economic dispatch (ED) problem focuses on the optimal scheduling of thermal generating units in a power system to minimize fuel costs while satisfying operational constraints. This article proposes modified version social group optimization (SGO) algorithm address ED with various practical characteristics (such as valve-point effects, transmission losses, prohibited operating zones, and multi-fuel sources). SGO is population-based metaheuristic strong exploration capabilities, but for certain types problems, it may stagnate local optimum due potential imbalance between exploitation. new version, named SGO-L, retains structure incorporates Laplace operator derived from distribution into all iterative solution update equations. adjustment generates more effective search steps space, improving exploration–exploitation balance overall performance terms stability quality. SGO-L validated four systems small (six-unit), medium (10-unit), large (40-unit 110-unit) sizes diverse characteristics. efficiency compared other algorithms. experimental results demonstrate that proposed robust than well-known algorithms particle swarm optimization, genetic algorithms, differential evolution, cuckoo algorithms) competitor mentioned study. Moreover, non-parametric Wilcoxon statistical test indicates promising original For example, standard deviation obtained by shows significantly lower values (6.02 × 10−9 USD/h six-unit system, 7.56 10−5 10-unit 75.89 40-unit 4.80 10−3 110-unit system) (0.44 50.80 274.91 1.04 system).

Язык: Английский

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Dynamic economic dispatch with uncertain wind power generation using an enhanced artificial hummingbird algorithm

Neural Computing and Applications, Год журнала: 2025, Номер unknown

Опубликована: Фев. 5, 2025

Язык: Английский

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A swank raccoon yin yang pair optimization (RYi-YaP) model for solving economic load dispatch (ELD) and combined emission dispatch (CED) problems

Computers & Electrical Engineering, Год журнала: 2025, Номер 123, С. 110149 - 110149

Опубликована: Фев. 21, 2025

Язык: Английский

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A novel prediction of the PV system output current based on integration of optimized hyperparameters of multi-layer neural networks and polynomial regression models

Next Energy, Год журнала: 2025, Номер 8, С. 100256 - 100256

Опубликована: Фев. 26, 2025

Язык: Английский

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Self-Adaptive Multi-Population Quadratic Approximation Guided Jaya Optimization Applied to Economic Load Dispatch Problems With or Without Valve-point Effects

Results in Control and Optimization, Год журнала: 2025, Номер unknown, С. 100543 - 100543

Опубликована: Март 1, 2025

Язык: Английский

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Dual-layer scheduling coordination algorithm for power supply guarantee using multi-objective optimization in spot market environment

Energy Informatics, Год журнала: 2025, Номер 8(1)

Опубликована: Март 18, 2025

Язык: Английский

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Arithmetic Optimization Algorithm with Cosine Composite Chaotic Mapping in Polar Coordinate System for Economic Load Dispatching Problems in Power Systems

Applied Soft Computing, Год журнала: 2025, Номер unknown, С. 113039 - 113039

Опубликована: Март 1, 2025

Язык: Английский

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Solving the Economic Load Dispatch Problem by Attaining and Refining Knowledge-Based Optimization

Mathematics, Год журнала: 2025, Номер 13(7), С. 1042 - 1042

Опубликована: Март 23, 2025

The Static Economic Load Dispatch (SELD) problem is a paramount optimization challenge in power engineering that seeks to optimize the allocation of between generating units meet imposed constraints while minimizing energy requirements. Recently, researchers have employed numerous meta-heuristic approaches tackle this challenging, non-convex problem. This work introduces an innovative algorithm, named “Attaining and Refining Knowledge-based Optimization (ARKO)”, which uses ability humans learn from their surroundings by leveraging collective knowledge population. ARKO algorithm consists two distinct phases: attaining refining. In phase, gathers population’s top candidates, refining phase enhances performance other selected candidates. way learning improving with help candidates provides robust exploration exploitation capability for algorithm. To validate efficacy ARKO, we conduct comprehensive evaluation against eleven established algorithms using diverse set 41 test functions CEC-2017 CEC-2022 suites, then, three real-life applications also verify its practical ability. Subsequently, implement SELD considering several instances. examination numerical statistical results confirms remarkable efficiency potential complex tasks.

Язык: Английский

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Comparative Performance Evaluation of Economic Load Dispatch Using Metaheuristic Techniques: A Practical Case Study for Bangladesh

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104720 - 104720

Опубликована: Март 1, 2025

Язык: Английский

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Cost-effective economic dispatch in large-scale power systems using enhanced manta ray foraging optimization

Neural Computing and Applications, Год журнала: 2025, Номер unknown

Опубликована: Апрель 3, 2025

Язык: Английский

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