Adaptive gaining-sharing knowledge-based variant algorithm with historical probability expansion and its application in escape maneuver decision making

Artificial Intelligence Review, Год журнала: 2025, Номер 58(6)

Опубликована: Март 15, 2025

Язык: Английский

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Enhanced Gaining-Sharing knowledge-based algorithm

Results in Control and Optimization, Год журнала: 2025, Номер unknown, С. 100542 - 100542

Опубликована: Март 1, 2025

Язык: Английский

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Forecasting of Türkiye's net electricity consumption with metaheuristic algorithms

Utilities Policy, Год журнала: 2025, Номер 95, С. 101929 - 101929

Опубликована: Март 28, 2025

Язык: Английский

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Laplacian biogeography-based algorithm using a gaining–sharing knowledge-based strategy for global optimization problems and the Lennard-Jones problem

Engineering Optimization, Год журнала: 2025, Номер unknown, С. 1 - 32

Опубликована: Май 21, 2025

Язык: Английский

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Enhanced skill optimization algorithm: Solution to the stochastic reactive power dispatch framework with optimal inclusion of renewable resources using large‐scale network

IET Renewable Power Generation, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 23, 2024

Abstract Optimal reactive power dispatch (ORPD) is taken as a vital problem related to electric networks for economic and control operations. Nowadays, thermal generators are no longer utilized renewable resources (RERs) have been integrated owing their marvellous benefits. The integration of RERs into considered strenuous imposition due uncertainties. objective determine the placement four wind PV units large‐scale 118‐bus network reduce expected losses. normal, lognormal, Weibull distributions model system uncertainties, while Monte‐Carlo simulation reduction‐based approaches generate novel set optimal scenarios. To avoid stagnation problems in skilled optimization algorithm (SOA), three strategies such fitness‐distance balance selection, mutation, gorilla troops‐based improve overall strength SOA. Effectiveness ESOA proved via statistical non‐parametric analysis using benchmark functions, results further compared with other techniques. proposed also used resolve deterministic stochastic ORPD frameworks losses By incorporation framework can saved around 24.01%.

Язык: Английский

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Robust parameter identification based on nature‐inspired optimization for accurate photovoltaic modelling under different operating conditions

IET Renewable Power Generation, Год журнала: 2024, Номер 18(12), С. 1893 - 1925

Опубликована: Авг. 2, 2024

Abstract Accurate parameter identification plays a crucial role in realizing precise modelling, design optimization, condition monitoring, and fault diagnosis of photovoltaic systems. However, due to the nonlinear, multivariate, multistate characteristics PV models, it is difficult identify perfect model parameters using traditional analytical numerical methods. Besides, some existing methods may stick local optimum have slow convergence speed. To address these challenges, this paper proposes an enhanced nature‐inspired OLARO algorithm for under different conditions. improved from ARO incorporating opposition‐based learning, Lévy flight roulette fitness‐distance balance improve global search capability avoid optima. Firstly, novel data smoothing measure taken reduce noises I – V curves. Then, compared with several common algorithms on solar cells modules robustness analysis statistical tests. The results indicate that has better ability than others extract models such as single diode, double module models. Moreover, performance more excellent other algorithms. Additionally, curves two irradiance temperature conditions are applied verify proposed extraction algorithm. successfully real operating modules, recent well‐known by FDB. Finally, sensitivity analysis, stability discussion practical challenges provided.

Язык: Английский

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An enhanced flower pollination algorithm with superiority of feasible solution for optimal power flow problem

Electrical Engineering, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 13, 2024

Язык: Английский

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A Novel Hybrid Algorithm for Solving Economic Load Dispatch in Power Systems

International Journal of Energy Research, Год журнала: 2024, Номер 2024(1)

Опубликована: Янв. 1, 2024

Various algorithms have been created in the past to take economic load dispatch (ELD) into account. These algorithms, however, concentrate on multiple tuning parameters, necessitating hyperparameter adjustment. A unique parameterless hybrid is presented explicitly evaluate ELD for test systems and real‐world power plant matching operational limitations. In addition, earlier could only offer estimates of final cost fuel based choices. This may prevent global minimum values from being met. To find comprehensive solutions problem systems, this paper suggests a new method called Jaya optimization algorithm, which uses merits teaching–learning‐based (TLBO) algorithms. enhancement proposed improve population variety, balance between local search, early convergence original method. metaheuristic technique TLBO simulates teaching–learning process classroom optimize problems. The algorithm an exploration phase possible are generated at random discover best solution. then exploitation refine search space‐based parameter adjustments enhance quality solution identified. On other hand, motivated by idea social behavior nature. Candidate improved repeatedly through cooperation competition using population‐based approach, each adjusts its position worst answers population. By combining advantages both (Jaya–TLBO) outperforms alone minimizes generation, improving quality. efficacy, Jaya–TLBO tested four different cases, such as Institute Electrical Electronics Engineers (IEEE) 6‐unit, 13‐unit, 20‐unit, 40‐unit system Indonesian 10‐unit one. Simulation results show that superior minimization well‐known used recently. As result, planners can utilize most economical dispatch.

Язык: Английский

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Performance Assessment of Natural Survivor Method-Based Metaheuristic Optimizers in Global Optimization and Engineering Design Problems

Sakarya University Journal of Computer and Information Sciences, Год журнала: 2024, Номер 7(2), С. 227 - 243

Опубликована: Авг. 26, 2024

This study presents the comparative performance analysis of Natural Survivor Method (NSM)-based algorithms in solving IEEE CEC 2022 test suite benchmark problems and four real-world engineering design problems. Three different variants (Case1, Case2, Case3) NSM-TLABC, NSM-SFS NSM-LSHADE-SPACMA were used study. The data obtained from experimental studies statistically analyzed using Friedman Wilcoxon signed-rank tests. Based on results, NSM-LSHADE-SPACMA_Case2 showed best with an average score 3.96. that outperformed its competitors 13 out 16 experiments, achieving a success rate 81.25%. NSM-LSHADE-SPACMA_Case2, which was found to be most powerful NSM-based algorithms, is solve cantilever beam design, tension/compression spring pressure vessel gear train optimization results are also compared eight state-of-the-art metaheuristics, including Rime Optimization Algorithm (RIME), Nonlinear Marine Predator (NMPA), Northern Goshawk (NGO), Kepler (KOA), Honey Badger (HBA), Artificial Gorilla Troops Optimizer (GTO), Exponential Distribution (EDO) Hunger Games Search (HGS). Given all together, it seen algorithm consistently produced for global studied.

Язык: Английский

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Anomaly Detection in Sports Training Data: An Improved Adaptive Algorithm

Опубликована: Дек. 13, 2024

Язык: Английский

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