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Tsonyo M. Pavkov, Valentin G. Kabadzhov, Ivan K. Ivanov and Stoil I. Ivanov
In this paper, we provide a detailed local convergence analysis of a one-parameter family of iteration methods for the simultaneous approximation of polynomial zeros due to Ivanov (Numer. Algor. 75(4): 1193?1204, 2017). Thus, we obtain two local converge...
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Ioannis K. Argyros, Debasis Sharma, Christopher I. Argyros, Sanjaya Kumar Parhi, Shanta Kumari Sunanda and Michael I. Argyros
A variety of strategies are used to construct algorithms for solving equations. However, higher order derivatives are usually assumed to calculate the convergence order. More importantly, bounds on error and uniqueness regions for the solution are also n...
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Davide Bilò, Luciano Gualà, Stefano Leucci and Guido Proietti
Network creation games have been extensively used as mathematical models to capture the key aspects of the decentralized process that leads to the formation of interconnected communication networks by selfish agents. In these games, each user of the netw...
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Samundra Regmi, Ioannis K. Argyros and Santhosh George
A local convergence comparison is presented between two ninth order algorithms for solving nonlinear equations. In earlier studies derivatives not appearing on the algorithms up to the 10th order were utilized to show convergence. Moreover, no error esti...
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Erna Budhiarti Nababan, Opim Salim Sitompul, Yuni Cancer
Pág. 87 - 100
Population size of classical genetic algorithm is determined constantly. Its size remains constant over the run. For more complex problems, larger population sizes need to be avoided from early convergence to produce local optimum. Objective of this rese...
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Ioannis K. Argyros, Stepan Shakhno, Samundra Regmi and Halyna Yarmola
A plethora of methods are used for solving equations in the finite-dimensional Euclidean space. Higher-order derivatives, on the other hand, are utilized in the calculation of the local convergence order. However, these derivatives are not on the methods...
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Janak Raj Sharma, Sunil Kumar and Ioannis K. Argyros
We discuss the local convergence of a derivative-free eighth order method in a Banach space setting. The present study provides the radius of convergence and bounds on errors under the hypothesis based on the first Fréchet-derivative only. The approaches...
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Zhenyu Song, Xuemei Yan, Lvxing Zhao, Luyi Fan, Cheng Tang and Junkai Ji
Brain-storm optimization (BSO), which is a population-based optimization algorithm, exhibits a poor search performance, premature convergence, and a high probability of falling into local optima. To address these problems, we developed the adaptive mecha...
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Xi Liu and Dan Zhang
Enterprise investment decision-making should not only consider investment profits, but also investment risks, which is a complex nonlinear multi-objective optimization problem. However, traditional investment decisions often only consider profit as a goa...
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Yanchun Zhao, Senlin Bi, Huanlong Zhang and Zhiwu Chen
The salp swarm algorithm imitates the swarm behavior of salps during navigation and hunting that has been proven the superiority of search for best solution. However, although it has sufficient global search ability, it is still worth paying attention to...
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Manli Dai and Zhongyi Jiang
An improved slime mold algorithm (IMSMA) is presented in this paper for a multiprocessor multitask fair scheduling problem, which aims to reduce the average processing time. An initial population strategy based on Bernoulli mapping reverse learning is pr...
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Chunhe Hu, Yu Xia and Junguo Zhang
Path planning of unmanned aerial vehicles (UAVs) in threatening and adversarial areas is a constrained nonlinear optimal problem which takes a great amount of static and dynamic constraints into account. Quantum-behaved pigeon-inspired optimization (QPIO...
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Chunhe Hu, Yu Xia and Junguo Zhang
Path planning of unmanned aerial vehicles (UAVs) in threatening and adversarial areas is a constrained nonlinear optimal problem which takes a great amount of static and dynamic constraints into account. Quantum-behaved pigeon-inspired optimization (QPIO...
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Khiem Van Truong
To fulfill the objective of a predictive tool for rotorcraft, comprehensive analysis (CA) needs to be capable of providing both accurate and time-efficient predictions of rotor air loads and structural loads. The more recent methodology based on comprehe...
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Khiem Van Truong
To fulfill the objective of a predictive tool for rotorcraft, comprehensive analysis (CA) needs to be capable of providing both accurate and time-efficient predictions of rotor air loads and structural loads. The more recent methodology based on comprehe...
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Ioannis K. Argyros, Santhosh George, Samundra Regmi and Christopher I. Argyros
Iterative algorithms requiring the computationally expensive in general inversion of linear operators are difficult to implement. This is the reason why hybrid Newton-like algorithms without inverses are developed in this paper to solve Banach space-valu...
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Wenbo Zhang, Yuchen Zhao, Fangjing Li and Hongbo Zhu
Federated learning is currently a popular distributed machine learning solution that often experiences cumbersome communication processes and challenging model convergence in practical edge deployments due to the training nature of its model information ...
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Yunyang Zhang, Shiyu Du and Quan Zhang
The slime mold algorithm (SMA) is a swarm-based metaheuristic algorithm inspired by the natural oscillatory patterns of slime molds. Compared with other algorithms, the SMA is competitive but still suffers from unbalanced development and exploration and ...
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Quan Zhang, Shiyu Du, Yiming Zhang, Hongzhuo Wu, Kai Duan and Yanru Lin
The Chimp Optimization Algorithm (ChOA) is a heuristic algorithm proposed in recent years. It models the cooperative hunting behaviour of chimpanzee populations in nature and can be used to solve numerical as well as practical engineering optimization pr...
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