A novel reward-based golden jackal optimization algorithm uses mix-weighted dynamic and random opposition learning to solve optimization problems DOI

Sarada Mohapatra,

Priteesha Sarangi,

Prabhujit Mohapatra

et al.

Cluster Computing, Journal Year: 2025, Volume and Issue: 28(5)

Published: April 28, 2025

Language: Английский

Multi-strategy fusion improved Northern Goshawk optimizer is used for engineering problems and UAV path planning DOI Creative Commons
Fan Yang,

Hong Jiang,

Lixin Lyu

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 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.

Language: Английский

Citations

6
Exploring machine learning applications in chemical production through valorization of biomass, plastics, and petroleum resources: A comprehensive review DOI
Iradat Hussain Mafat,

Dadi Venkata Surya,

Sumeet K. Sharma

et al.

Journal of Analytical and Applied Pyrolysis, Journal Year: 2024, Volume and Issue: 180, P. 106512 - 106512

Published: April 25, 2024

Language: Английский

Citations

5
Refinement of Dynamic Hunting Leadership Algorithm for Enhanced Numerical Optimization DOI Creative Commons

Oluwatayomi Rereloluwa Adegboye,

Afi Kekeli Feda, Opeoluwa Seun Ojekemi

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 103271 - 103298

Published: Jan. 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.

Language: Английский

Citations

5
A dual opposition learning-based multi-objective Aquila Optimizer for trading-off time-cost-quality-CO2 emissions of generalized construction projects DOI
Mohammad Azım Eırgash, Vedat Toğan

Engineering Computations, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 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.

Language: Английский

Citations

5
MMPA: A modified marine predator algorithm for 3D UAV path planning in complex environments with multiple threats DOI
Lixin Lyu, Fan Yang

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 257, P. 124955 - 124955

Published: Aug. 8, 2024

Language: Английский

Citations

4
Advancements in Q‐learning meta‐heuristic optimization algorithms: A survey DOI
Yang Yang, Yuchao Gao, Zhe Ding

et al.

Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, Journal Year: 2024, Volume and Issue: 14(6)

Published: Aug. 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

Language: Английский

Citations

4
An improved multi-operator differential evolution via a knowledge-guided information sharing strategy for global optimization DOI
Zhuoming Yuan, Lei Peng, Guangming Dai

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 126403 - 126403

Published: Jan. 1, 2025

Language: Английский

Citations

0
Dynamic Q&A multi-label classification based on adaptive multi-scale feature extraction DOI
Ying Li, Ming Li, Xiaoyi Zhang

et al.

Applied Soft Computing, Journal Year: 2025, Volume and Issue: unknown, P. 112740 - 112740

Published: Jan. 1, 2025

Language: Английский

Citations

0
A novel marine predator algorithm for path planning of UAVs DOI
Rong Gong,

Huaming Gong,

Lila Hong

et al.

The Journal of Supercomputing, Journal Year: 2025, Volume and Issue: 81(4)

Published: Feb. 18, 2025

Language: Английский

Citations

0
Twin Q-learning-driven forest ecosystem optimization for feature selection DOI
Hongbo Zhang, Jinlong Li, Xiaofeng Yue

et al.

Knowledge-Based Systems, Journal Year: 2025, Volume and Issue: unknown, P. 113323 - 113323

Published: March 1, 2025

Language: Английский

Citations

0