Cluster Computing, Год журнала: 2025, Номер 28(5)
Опубликована: Апрель 28, 2025
Язык: Английский
Scientific Reports, Год журнала: 2024, Номер 14(1)
Опубликована: Окт. 7, 2024
Addressing the imbalance between exploration and exploitation, slow convergence, local optima Traps, low convergence precision in Northern Goshawk Optimizer (NGO): Introducing a Multi-Strategy Integrated (MINGO). In response to challenges faced by (NGO), including issues like susceptibility optima, precision, this paper introduces an enhanced variant known as The algorithm tackles balance exploitation improving strategies development approaches. It incorporates Levy flight preserve population diversity enhance precision. Additionally, avoid getting trapped Cauchy mutation strategies, its capability escape during search process. Finally, individuals with poor fitness are eliminated using crossover strategy of Differential Evolution overall quality. To assess performance MINGO, conducts analysis from four perspectives: diversity, behavior, various variants. Furthermore, MINGO undergoes testing on CEC-2017 CEC-2022 benchmark problems. test results, along Wilcoxon rank-sum demonstrate that outperforms NGO other advanced optimization algorithms terms performance. applicability superiority further validated six real-world engineering problems 3D Trajectory planning for UAVs.
Язык: Английский
Процитировано
6
Journal of Analytical and Applied Pyrolysis, Год журнала: 2024, Номер 180, С. 106512 - 106512
Опубликована: Апрель 25, 2024
Язык: Английский
Процитировано
5
IEEE Access, Год журнала: 2024, Номер 12, С. 103271 - 103298
Опубликована: Янв. 1, 2024
A recently created optimization algorithm named the Dynamic Hunting Leadership (DHL) was inspired by leadership tactics used in hunting operations. The foundation of DHL is idea that successful can significantly increase endeavors. Although has shown to be relatively simple and tackling a variety practical issues, it been discovered suffers with efficiently balancing global exploration local search phase, particularly high-dimensional numerical problems engineering applications. Furthermore, due drawbacks, vulnerable becoming stuck optimal. present study aims tackle aforementioned challenges introducing modified variant DHL, referred as mDHL, utilizes Levy Flight technique localized development strategy augment each hunter's capacity track their prey attain superior optimal outcomes. Moreover, escape operator quasi-opposition learning are synergistically incorporated foster hunters' techniques. These knowledge sharing between leaders hunters, resulting harmonious blend capabilities. mDHL outperform existing optimizers across 20 function test suites varying dimensions from 30 200 CEC 2019 functions. In addition, successfully applied solve four design cases, demonstrating its practicality. experimental findings indicate substantial improvement over conventional emphasizing potential competitive efficient for addressing challenges.
Язык: Английский
Процитировано
5
Engineering Computations, Год журнала: 2024, Номер unknown
Опубликована: Сен. 18, 2024
Purpose Most of the existing time-cost-quality-environmental impact trade-off (TCQET) analysis models have focused on solving a simple project representation without taking typical activity and characteristics into account. This study aims to present novel approach called “hybrid opposition learning-based Aquila Optimizer” (HOLAO) for optimizing TCQET decisions in generalized construction projects. Design/methodology/approach In this paper, HOLAO algorithm is designed, incorporating quasi-opposition-based learning (QOBL) quasi-reflection-based (QRBL) strategies initial population generation jumping phases, respectively. The crowded distance rank (CDR) mechanism utilized optimal Pareto-front solutions assist decision-makers (DMs) achieving single compromise solution. Findings efficacy proposed methodology evaluated by examining problems, involving 69 290 activities, Results indicate that provides competitive problems It observed surpasses multiple objective social group optimization (MOSGO), plain Optimization (AO), QRBL QOBL algorithms terms both number function evaluations (NFE) hypervolume (HV) indicator. Originality/value paper introduces concept hybrid opposition-based (HOL), which incorporates two strategies: as an explorative exploitative opposition. Achieving effective balance between exploration exploitation crucial success any algorithm. To end, are developed ensure proper equilibrium phases basic AO third contribution provide resource utilizations (construction plans) evaluate these resources performance.
Язык: Английский
Процитировано
5
Expert Systems with Applications, Год журнала: 2024, Номер 257, С. 124955 - 124955
Опубликована: Авг. 8, 2024
Язык: Английский
Процитировано
4
Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, Год журнала: 2024, Номер 14(6)
Опубликована: Авг. 18, 2024
Abstract This paper reviews the integration of Q‐learning with meta‐heuristic algorithms (QLMA) over last 20 years, highlighting its success in solving complex optimization problems. We focus on key aspects QLMA, including parameter adaptation, operator selection, and balancing global exploration local exploitation. QLMA has become a leading solution industries like energy, power systems, engineering, addressing range mathematical challenges. Looking forward, we suggest further integration, transfer learning strategies, techniques to reduce state space. article is categorized under: Technologies > Computational Intelligence Artificial
Язык: Английский
Процитировано
4
Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 126403 - 126403
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0
Applied Soft Computing, Год журнала: 2025, Номер unknown, С. 112740 - 112740
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0
The Journal of Supercomputing, Год журнала: 2025, Номер 81(4)
Опубликована: Фев. 18, 2025
Язык: Английский
Процитировано
0
Knowledge-Based Systems, Год журнала: 2025, Номер unknown, С. 113323 - 113323
Опубликована: Март 1, 2025
Язык: Английский
Процитировано
0