18 papers · ranked by Valyu relevance
Na Li, Zi Miao, Sha Zhou, Haoxiang Zhou + 3 more
The Educational Competition Optimizer (ECO) formulates search as a three-stage didactic process-primary, secondary and tertiary learning-but the original framework suffers from scarce information exchange, sluggish late-stage convergence and an unstable exploration-exploitation ratio. We present EECO, which introduces…
Wei Lv, Yueshun He, Yuankun Yang, Xiaohui Ma + 3 more
Although the Dung Beetle Optimizer (DBO) is a promising new metaheuristic for global optimization, it often struggles with premature convergence and lacks the necessary precision when applied to complex optimization challenges. Therefore, we developed the Multi-Strategy Improved Dung Beetle Optimizer (MIDBO), an…
Oguz Emrah Turgut, Hadi Genceli, Mustafa Asker, Ehsan Baniasadi + 1 more
This research proposes a novel hybrid metaheuristic optimization framework that combines the Aquila Optimization algorithm with the Sine-Cosine Optimizer to find equilibrium points of reacting components under specified operational reaction conditions. The method aims to address the exploitative limitations of the…
Chenglong Wang, Chengqian Tan, Youyou Cheng, Yinggao Yue
Optimization algorithms play a crucial role in solving complex problems in reservoir geology and engineering, particularly those involving highly non-linear, multi-parameter, and high-dimensional systems. In the context of reservoir development, accurate optimization is essential for enhancing hydrocarbon recovery…
Hanjie Xu, Jian Xiong, Jinyu Wu, Xianlai Zhou + 2 more
The Ivy Algorithm (IVYA), a swarm intelligence algorithm inspired by plant growth, presents a novel framework for optimization. To unlock its full potential in complex, high-dimensional problems, it is crucial to address the fundamental challenge of balancing exploration and exploitation, which can impact overall…
Ali Asghari, Mohammadhossein Mohammadi, Heming Jia
Metaheuristic algorithms are widely used to find optimal or near-optimal solutions for complex problems by taking inspiration from natural behaviors and processes. Although many different methods have been developed, a common problem in many of them is maintaining a good balance between exploration and exploitation and…
M. Paknahad, P. Hosseini, C. Paknahad, S. J. S. Hakim + 1 more
Engineering optimization problems are increasingly complex, requiring more sophisticated approaches to locate global optima. This paper presents the Adaptive Hybrid Optimization (AHO) algorithm, which addresses the limitations of single-equation update mechanisms and conventional linear decay schedules through three…
Shengnan Li, Taiju Yin, Heming Jia
As engineering systems grow in complexity, reliable metaheuristic optimizers are increasingly essential. While swarm intelligence algorithms are widely applied, recent approaches like the Cuckoo Catfish Optimizer (CCO) can experience premature convergence due to limited local exploitation and simplistic boundary…
Yuwei Fan, Zhilin Cheng, Youyou Cheng, Chaoqun Li
Metaheuristic algorithms can fail to balance global exploration and local exploitation, occasionally becoming trapped in suboptimal regions on highly multimodal problems. The Groupers and Moray Eels (GME) algorithm, inspired by the associative hunting strategies of marine predators, provides a cooperative optimization…
Assem F. Alabu-Husain, Mostafa A. ElBahloul, Mahmoud M. Saafan, Eman M. El-Gendy
This study introduces the Pray Optimization Algorithm (POA), a novel metaheuristic inspired by the procedural rituals of Islamic pray, designed to solve complex engineering and robotic manipulator problems. The mathematical model is structured into three distinct phases: Phase I simulates searching for a suitable…
Taybe Alabed, Sema Servi, Bolin Liao, Shuai (Steven) Li + 1 more
Clustering is a fundamental unsupervised learning technique used to uncover hidden patterns in unlabeled data. Although metaheuristic algorithms have demonstrated effectiveness in clustering, many suffer from premature convergence and limited population diversity. This study employs the Black-Winged Kite Algorithm…
Reham Kamal, Eman Amin, Diaa Salama AbdElminaam, Rasha Ismail
Feature selection is a key step in machine learning-based decision systems, especially in medical and biomedical applications, where datasets often contain a large number of features that can negatively affect both accuracy and interpretability. In this study, we introduce the binary secretary bird optimization…
Zhiwei Ye, Yawen Yan, Yujun Ma, Fan Ma + 2 more
Feature selection is essential for improving classification performance in high-dimensional biomedical data, yet conventional metaheuristic algorithms often suffer from premature convergence and loss of population diversity. To address these issues, this paper proposes a Feature Grouping and Improved Hybrid Breeding…
Hongmei Bai, Taosuo Wu, Jianfu Luo, Na Ta + 1 more
This paper proposes a multi-strategy improved pied kingfisher optimizer (MSIPKO), a novel metaheuristic algorithm designed to address constrained optimization problems (COPs). COPs are widely encountered in engineering and industrial applications and are characterized by complex constraints that restrict the feasible…
Aryan Kumar, Punit Gupta, Rohit Verma, Asaad Ahmed Gad Elrab Ahmed
Fog computing minimizes latency and bandwidth consumption by processing data near the source, but it has the challenge of workload balancing across the dynamic and resource limited fog nodes. Uneven task assignment can result in bottlenecks, idle resources, and lowered Quality of Service (QoS) standards. In this work…
Kecheng Su, Yaoyang Wang, Yikang Kong, Wenan Liu + 1 more
Multi-objective optimization problems have extensive application value in the fields of engineering and science, among which UAV path planning, as a typical application scenario, has attracted considerable attention. This study innovatively proposes a multi-objective extension of the Gold Rush Optimization algorithm…
Paulo Figueroa-Torrez, Broderick Crawford, Orlando Durán, Martín Jurado-Camacho + 4 more
The Cell Formation Problem plays a fundamental role in cellular manufacturing due to its impact on efficiency, flexibility, and reliability. Its complexity increases under real-world conditions involving alternative process routes and machine reliability constraints, leading to the Generalized Cell Formation Problem…
Alia A. Othman, Dina A. Elmanakhly
The deployment of Unmanned Aerial Vehicles (UAVs) in conjunction with Mobile Edge Computing (MEC) has come to be a viable approach to solve some challenges that face the internet of things systems, including energy consumption, latency, and data processing efficiency. However, trajectory planning optimization for UAVs…