Expert Systems with Applications, Год журнала: 2025, Номер 270, С. 126503 - 126503
Опубликована: Янв. 16, 2025
Язык: Английский
Advances in Engineering Software, Год журнала: 2024, Номер 195, С. 103694 - 103694
Опубликована: Июнь 15, 2024
Язык: Английский
Процитировано
49
Scientific Reports, Год журнала: 2024, Номер 14(1)
Опубликована: Фев. 11, 2024
Abstract The parameter identification problem of photovoltaic (PV) models is classified as a complex nonlinear optimization that cannot be accurately solved by traditional techniques. Therefore, metaheuristic algorithms have been recently used to solve this due their potential approximate the optimal solution for several complicated problems. Despite that, existing still suffer from sluggish convergence rates and stagnation in local optima when applied tackle problem. study presents new estimation technique, namely HKOA, based on integrating published Kepler algorithm (KOA) with ranking-based update exploitation improvement mechanisms estimate unknown parameters third-, single-, double-diode models. former mechanism aims at promoting KOA’s exploration operator diminish getting stuck optima, while latter strengthen its faster converge solution. Both KOA HKOA are validated using RTC France solar cell five PV modules, including Photowatt-PWP201, Ultra 85-P, STP6-120/36, STM6-40/36, show efficiency stability. In addition, they extensively compared techniques effectiveness. According experimental findings, strong alternative method estimating because it can yield substantially different superior findings
Язык: Английский
Процитировано
24
Sustainable Computing Informatics and Systems, Год журнала: 2024, Номер 43, С. 100992 - 100992
Опубликована: Май 1, 2024
Язык: Английский
Процитировано
21
Heliyon, Год журнала: 2024, Номер 10(5), С. e26665 - e26665
Опубликована: Март 1, 2024
This research introduces the Multi-Objective Liver Cancer Algorithm (MOLCA), a novel approach inspired by growth and proliferation patterns of liver tumors. MOLCA emulates evolutionary tendencies tumors, leveraging their expansion dynamics as model for solving multi-objective optimization problems in engineering design. The algorithm uniquely combines genetic operators with Random Opposition-Based Learning (ROBL) strategy, optimizing both local global search capabilities. Further enhancement is achieved through integration elitist non-dominated sorting (NDS), information feedback mechanism (IFM) Crowding Distance (CD) selection method, which collectively aim to efficiently identify Pareto optimal front. performance rigorously assessed using comprehensive set standard test benchmarks, including ZDT, DTLZ various Constraint (CONSTR, TNK, SRN, BNH, OSY KITA) real-world design like Brushless DC wheel motor, Safety isolating transformer, Helical spring, Two-bar truss Welded beam. Its efficacy benchmarked against prominent algorithms such grey wolf optimizer (NSGWO), multiobjective multi-verse (MOMVO), (NSGA-II), decomposition-based (MOEA/D) marine predator (MOMPA). Quantitative analysis conducted GD, IGD, SP, SD, HV RT metrics represent convergence distribution, while qualitative aspects are presented graphical representations fronts. source code available at: https://github.com/kanak02/MOLCA.
Язык: Английский
Процитировано
20
Energy Strategy Reviews, Год журнала: 2024, Номер 53, С. 101409 - 101409
Опубликована: Май 1, 2024
The synergy between deep learning and meta-heuristic algorithms presents a promising avenue for tackling the complexities of energy-related modeling forecasting tasks. While excels in capturing intricate patterns data, it may falter achieving optimality due to nonlinear nature energy data. Conversely, offer optimization capabilities but suffer from computational burdens, especially with high-dimensional This paper provides comprehensive review spanning 2018 2023, examining integration within frameworks applications. We analyze state-of-the-art techniques, innovations, recent advancements, identifying open research challenges. Additionally, we propose novel framework that seamlessly merges into paradigms, aiming enhance performance efficiency addressing problems. contributions include: 1. Overview advancements MHs, DL, integration. 2. Coverage trends 2023. 3. Introduction Alpha metric evaluation. 4. Innovative harmonizing MHs DL
Язык: Английский
Процитировано
19
Artificial Intelligence Review, Год журнала: 2025, Номер 58(4)
Опубликована: Янв. 30, 2025
Язык: Английский
Процитировано
7
Wireless Personal Communications, Год журнала: 2023, Номер 133(3), С. 2019 - 2064
Опубликована: Дек. 1, 2023
Язык: Английский
Процитировано
25
Knowledge-Based Systems, Год журнала: 2024, Номер 295, С. 111725 - 111725
Опубликована: Апрель 16, 2024
Язык: Английский
Процитировано
17
Alexandria Engineering Journal, Год журнала: 2024, Номер 91, С. 348 - 367
Опубликована: Фев. 19, 2024
Honey badger algorithm (HBA) is a recent swarm-based metaheuristic that excels in simplicity and high exploitation capability. However, it suffers from some limitations including weak exploration capacity an imbalance between exploitation. In this paper, improved honey called ODEHBA proposed to improve the performance of basic HBA. Firstly, orthogonal opposition-based learning technique employed assist population escaping local optimum. Secondly, differential evolution utilized ensure enrichment diversity enhance convergence speed. Finally, capability boosted by equilibrium pool strategy. To validate efficacy ODEHBA, compared with 13 well-known algorithms on CEC2022 benchmark test sets. Friedman Wilcoxon rank-sum are assess ODEHBA. Furthermore, three engineering design problems Internet Vehicles (IoV) routing problem applied The simulation results demonstrate solving complex numerical problems, design, IoV problems. This holds significant practical implications for cost reduction resource utilization.
Язык: Английский
Процитировано
14
Advanced Engineering Informatics, Год журнала: 2024, Номер 62, С. 102685 - 102685
Опубликована: Июль 12, 2024
Язык: Английский
Процитировано
14