检索结果-内蒙古大学图书馆

ABFIA: A hybrid algorithm based on artificial bee colony and Fibonacci indicator algorithm

JOURNAL OF COMPUTATIONAL SCIENCE 2022年第0期61卷

作者： Etminaniesfahani, Alireza Gu, Hanyu Salehipour, Amir Univ Technol Sydney Sch Math & Phys Sci 15 Broadway Ultimo NSW 2007 Australia

The artificial bee colony (ABC) is a metaheuristic optimization algorithm known for its simplicity, flexibility, and efficiency. The algorithm, however, suffers from slow convergence due to a lack of a powerful local search capability. The Fibonacci indicator algorithm (FIA), on the other hand, is a recently proposed derivative-free metaheuristic that incorporates a powerful local search mechanism based on the line search method. This paper proposes hybridizing the artificial bee colony with the Fibonacci indicator algorithm to achieve strong exploration and highly efficient exploitation capabilities. We show that the hybrid algorithm is better than ABC and FIA and delivers superior outcomes for various optimization functions widely used in the literature, including 20 scalable basic and ten complex CEC2019 test functions.

关键词： Artificial bee colony algorithm Fibonacci indicator algorithm hybrid algorithms Metaheuristics

来源：评论

学校读者我要写书评

暂无评论

Particle Swarm Optimization: A Comprehensive Survey

引用

IEEE ACCESS 2022年 10卷 10031-10061页

作者： Shami, Tareq M. El-Saleh, Ayman A. Alswaitti, Mohammed Al-Tashi, Qasem Summakieh, Mhd Amen Mirjalili, Seyedali Univ York Dept Elect Engn York YO10 5DD N Yorkshire England ASharqiyah Univ Coll Engn Dept Elect & Commun Engn Ibra 400 Oman Xiamen Univ Malaysia Sch Elect & Comp Engn Dept Informat & Commun Technol Sepang 43900 Malaysia Univ Texas MD Anderson Canc Ctr Dept Imaging Phys Houston TX 77030 USA Univ Albaydha Fac Adm & Comp Sci Albaydha Yemen Multimedia Univ Fac Engn Cyberjaya 63100 Selangor Malaysia Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Fortitude Valley Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab Seoul 03722 South Korea

Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms in the literature. Although the original PSO has shown good optimization performance, it still severely suffers from premature convergence. As a result, many researchers have been modifying it resulting in a large number of PSO variants with either slightly or significantly better performance. Mainly, the standard PSO has been modified by four main strategies: modification of the PSO controlling parameters, hybridizing PSO with other well-known meta-heuristic algorithms such as genetic algorithm (GA) and differential evolution (DE), cooperation and multi-swarm techniques. This paper attempts to provide a comprehensive review of PSO, including the basic concepts of PSO, binary PSO, neighborhood topologies in PSO, recent and historical PSO variants, remarkable engineering applications of PSO, and its drawbacks. Moreover, this paper reviews recent studies that utilize PSO to solve feature selection problems. Finally, eight potential research directions that can help researchers further enhance the performance of PSO are provided.

关键词： Signal processing algorithms Optimization Particle swarm optimization Feature extraction Topology Birds Statistics Applications of PSO binary PSO evolutionary computation feature selection hybrid algorithms meta-heuristic algorithms particle swarm optimization PSO variants

来源：评论

学校读者我要写书评

暂无评论

Hierarchical tri-level optimization model for effective use of by-products in a sugarcane supply chain network

引用

APPLIED SOFT COMPUTING 2022年第0期128卷

作者： Chouhan, Vivek Kumar Khan, Shahul Hamid Hajiaghaei-Keshteli, Mostafa Indian Inst Informat Technol Dept Mech Engn Design & Mfg Chennai India Tecnol Monterrey Escuela Ingn & Ciencias Puebla Campus Monterrey Mexico

Developing a solution algorithm for tri-level facility location-allocation problem is always challenging due to the inherent complexity of these problems. The proposed sugarcane supply chain network is a tri-level modeling approach formulated based on the static Stackelberg game between producer, distribution center, sugarcane industry, compost unit, biorefinery unit and market in the framework. The network emphasizes the use of massive amount of the by-products generated in the sugarcane industry. These by-products are excellent raw materials for compost unit and biorefinery units. In such cases decisions are made in the hierarchy. By reviewing the proposed algorithms in the past, it has been realized that the shortcomings of the algorithm could be improved by introducing some efficient search mechanism in the algorithms. In this context, a strong local search mechanism based on social engineering optimizer is developed to intensify search space more carefully. Two-hybrid algorithms, GASEO based on Genetic algorithm and social engineering optimizer, and KASEO based on Keshtel algorithm and social engineering optimizer is proposed. To appraise the performance of the proposed algorithm, we pervasively discussed the parameter tuning using Taguchi approach. The ANOVA and Tukey post-hoc test ensures that the performance of the proposed GASEO is appreciable over other algorithms. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Multi-level programming Facility location-allocation Agricultural by-products Metaheuristics hybrid algorithms

来源：评论

学校读者我要写书评

暂无评论

hybrid Grey Wolf Algorithmic Approach for Optimizing Workflow Scheduling with Budget and Deadline Constraints in IaaS Clouds 15

Hybrid Grey Wolf Algorithmic Approach for Optimizing Workflo...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Kolla, Sai Sampath Ramesh, Dharavath Santosh, Thakur Narvaneni, Ramarao Department of Computer Science and Engineering Dhanbad India Mahindra University Centre for Life Sciences Ecole Centrale School of Engineering Hyderabad India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350370249

Efficiently allocating virtual machines (VMs) to tasks within cloud computing environments is crucial for optimizing resource usage and cutting operational expenses. Traditional methods often struggle to balance cost efficiency and timeliness, especially in intricate cloud workflow setups. This paper introduces a novel hybridization approach merging Simulated Annealing (SA) and Grey Wolf Optimizer (GWO) to tackle these challenges. HGWO utilizes SA's exploratory prowess to escape local optima and leverages GWO's exploitative abilities to refine solutions towards global optimality. Additionally, the algorithm includes a penalty function to rigorously eliminate solutions that violate predefined budget and deadline constraints, ensuring adherence to Quality of Service (QoS) requirements. Performance evaluation via extensive simulations, modeling realistic cloud workflow scenarios, demonstrates that the hybrid algorithm consistently surpasses existing cost reduction and deadline adherence methods, making it a potent tool for cloud service providers seeking to boost resource allocation efficiency. This study's implications indicate that integrating diverse optimization techniques can yield more robust solutions in cloud workflow management, fostering adaptive and scalable cloud computing architectures. © 2024 IEEE.

关键词： Cloud computing hybrid algorithms Makespan Optimization Penalty function Workflow scheduling

来源：评论

学校读者我要写书评

暂无评论

hybrid In-Vehicle Background Noise Reduction for Robust Speech Recognition:The Possibilities of Next Generation 5G Data Networks

引用

Computers, Materials & Continua 2022年第6期71卷 4659-4676页

作者： Radek Martinek Jan Baros Rene Jaros Lukas Danys Jan Nedoma VSB-Technical University of Ostrava Faculty of Electrical Engineering and Computer ScienceDepartment of Cybernetics and Biomedical Engineering70800Ostrava-PorubaCzechia VSB Technical University of Ostrava Faculty of Electrical Engineering and Computer ScienceDepartment of Telecommunications70800Ostrava-PorubaCzechia

This pilot study focuses on employment of hybrid LMS-ICA system for in-vehicle background noise *** vehicles are nowadays increasingly supporting voice commands,which are one of the pillars of autonomous and SMART *** speaker recognition for context-aware in-vehicle applications is limited to a certain extent by in-vehicle back-ground *** article presents the new concept of a hybrid system which is implemented as a virtual *** highly modular concept of the virtual car used in combination with real recordings of various driving scenarios enables effective testing of the investigated methods of in-vehicle background noise *** study also presents a unique concept of an adaptive system using intelligent clusters of distributed next generation 5G data networks,which allows the exchange of interference information and/or optimal hybrid algorithm settings between individual *** average,the unfiltered voice commands were successfully recognized in 29.34%of all scenarios,while the LMS reached up to 71.81%,and LMS-ICA hybrid improved the performance further to 73.03%.

关键词： 5G noise reduction hybrid algorithms speech recognition 5G data networks in-vehicle background noise

来源：评论

学校读者我要写书评

暂无评论

Comparing the Soil Conservation Service model with new machine learning algorithms for predicting cumulative infiltration in semi-arid regions

引用

Pedosphere 2022年第5期32卷 718-732页

作者： Khabat KHOSRAVI Phuong T.T.NGO Rahim BARZEGAR John QUILTY Mohammad T.AALAMI Dieu T.BUI Department of Watershed Management Engineering Ferdowsi University of MashhadMashhad 93(Iran) Department of Earth and Environment Florida International UniversityMiami 33199(USA) Institute of Research and Development Duy Tan UniversityDa Nang 550000(Vietnam) Department of Bioresource Engineering McGill UniversitySte Anne de Bellevue QC H9X(Canada) Faculty of Civil Engineering University of TabrizTabriz 51(Iran) Department of Civil and Environmental Engineering University of WaterlooWaterloo N2L 3G1(Canada) Department of Business and IT University of South-Eastern NorwayNotodden 3603(Norway)

Water infiltration into soil is an important process in hydrologic cycle;however,its measurement is difficult,time-consuming and *** and physical models have been developed to predict cumulative infiltration(CI),but are often *** this study,several novel standalone machine learning algorithms(M5Prime(M5P),decision stump(DS),and sequential minimal optimization(SMO))and hybrid algorithms based on additive regression(AR)(i.e.,AR-M5P,AR-DS,and AR-SMO)and weighted instance handler wrapper(WIHW)(i.e.,WIHW-M5P,WIHW-DS,and WIHW-SMO)were developed for CI *** Soil Conservation Service(SCS)model developed by the United States Department of Agriculture(USDA),one of the most popular empirical models to predict CI,was considered as a ***,154 measurements of CI(explanatory/input variables)were taken from 16 sites in a semi-arid region of Iran(Illam and Lorestan provinces).Six input variable combinations were considered based on Pearson correlations between candidate model inputs(time of measuring and soil bulk density,moisture content,and sand,clay,and silt percentages)and *** dataset was divided into two subgroups at random:70%of the data were used for model building(training dataset)and the remaining 30%were used for model validation(testing dataset).The various models were evaluated using different graphical approaches(bar charts,scatter plots,violin plots,and Taylor diagrams)and quantitative measures(root mean square error(RMSE),mean absolute error(MAE),Nash-Sutcliffe efficiency(NSE),and percent bias(PBIAS)).Time of measuring had the highest correlation with CI in the study *** best input combinations were different for different *** results showed that all hybrid algorithms enhanced the CI prediction accuracy compared to the standalone *** AR-M5P model provided the most accurate CI predictions(RMSE=0.75 cm,MAE=0.59 cm,NSE=0.98),while the SCS model had the lowest performance(RMSE=4.77 cm,MAE=2.64 cm,NSE=

关键词： additive regression hybrid algorithms empirical model soil water infiltration weighted instances handler wrapper

来源：评论

学校读者我要写书评

暂无评论

Cardinality constrained portfolio optimization with a hybrid scheme combining a Genetic Algorithm and Sonar Inspired Optimization

引用

OPERATIONAL RESEARCH 2022年第3期22卷 2465-2487页

作者： Konstantinou, Christos Tzanetos, Alexandros Dounias, Georgios Univ Aegean Management & Decis Engn Lab Dept Financial & Management Engn 41 Kountouriotou Str Chios 82132 Greece

The constraints and the vast solution space of operational research optimization problems make them hard to cope with. However, Computational Intelligence, and especially Nature-Inspired algorithms, has been a useful tool to tackle hard and large space optimization problems. In this paper, a very consistent and effective hybrid optimization scheme to tackle cardinality constrained portfolio optimization problems is presented. This scheme consists of two nature-inspired algorithms, i.e. Sonar Inspired Optimization algorithm and Genetic Algorithm. Also, the incorporation of heuristic information, i.e. an expert's knowledge, etc., to the overall performance of the hybrid scheme is tested and compared to previous studies. More specifically, under the framework of a financial portfolio optimization problem, the heuristic information-enhanced hybrid scheme manages to reach a new optimal solution. Additionally, a comparison of the proposed hybrid scheme with other hybrid schemes applied to the same problem with the same data is performed.

关键词： Portfolio optimization Sonar inspired optimization Genetic algorithm hybrid algorithms Nature-inspired algorithms

来源：评论

学校读者我要写书评

暂无评论

Parallel hybrid Island Metaheuristic Algorithm

引用

IEEE ACCESS 2022年 10卷 42254-42272页

作者： Li, Jiawei Gonsalves, Tad Sophia Univ Fac Sci & Technol Dept Informat & Commun Sci Tokyo 1028554 Japan

This study introduces a novel Parallel hybrid Island architecture which shows a parallel way to combine different meta-heuristic algorithms by using the island model as the base. The corresponding hybrid algorithm is called Parallel hybrid Island Metaheuristic algorithms (PHIMA). The hybrid parallel structure exploits the characteristics of the individual metaheuristic algorithms to boost robustness and diversity. Island Genetic Algorithm has been combined with Particle Swarm Optimization and Fireworks Algorithm to build three different PHIMA algorithms: PSO-GA (PHIMA-PGA), FWA-GA (PHIMA-FGA) and FWA-PSO-GA (PHIMA-FPGA). Further, another implementational variation known as "co-evolution" is applied to the sub-GA islands of PHIMA-FPGA to improve the performance on multi-modal high-dimensional problems. This variation is referred to as PHIMA-FPGA-Co. Each PHIMA Algorithm exhibits different advantages and characteristics, and the parallel hybridization using the island model is found to improve robustness and population diversity. The performances of the four new algorithms are compared with each other and that of the traditional Island GAs and all four proposed PHIMA algorithms show better result quality.

关键词： Sparks Genetic algorithms Metaheuristics Statistics Sociology Explosions Heuristic algorithms Meta-heuristic algorithms hybrid algorithms optimization genetic algorithm particle swarm algorithm fireworks algorithm co-evolution island model

来源：评论

学校读者我要写书评

暂无评论

Computational methods and diffusion theory in triangulation sensing to model neuronal navigation

引用

REPORTS ON PROGRESS IN PHYSICS 2022年第10期85卷 104601-104601页

作者： Dobramysl, Ulrich Holcman, David Univ Oxford Peter Medawar Bldg Pathogen Res Oxford England Univ Cambridge Dept Appl Math & Theoret Phys Cambridge England IBENS PSL Ecole Normale Super Grp Data Modeling & Computat Biol Paris France

Computational methods are now recognized as powerful and complementary approaches in various applied sciences such as biology. These computing methods are used to explore the gap between scales such as the one between molecular and cellular. Here we present recent progress in the development of computational approaches involving diffusion modeling, asymptotic analysis of the model partial differential equations, hybrid methods and simulations in the generic context of cell sensing and guidance via external gradients. Specifically, we highlight the reconstruction of the location of a point source in two and three dimensions from the steady-state diffusion fluxes arriving to narrow windows located on the cell. We discuss cases in which these windows are located on the boundary of a two-dimensional plane or three-dimensional half-space, on a disk in free space or inside a two-dimensional corridor, or a ball in three dimensions. The basis of this computational approach is explicit solutions of the Neumann-Green's function for the mentioned geometry. This analysis can be used to design hybrid simulations where Brownian paths are generated only in small regions in which the local spatial organization is relevant. Particle trajectories outside of this region are only implicitly treated by generating exit points at the boundary of this domain of interest. This greatly accelerates the simulation time by avoiding the explicit computation of Brownian paths in an infinite domain and serves to generate statistics, without following all trajectories at the same time, a process that can become numerically expensive quickly. Moreover, these computational approaches are used to reconstruct a point source and estimating the uncertainty in the source reconstruction due to an additive noise perturbation present in the fluxes. We also discuss the influence of various window configurations (cluster vs uniform distributions) on recovering the source position. Finally, the applications in

关键词： fast Brownian simulations hybrid algorithms modeling cell sensing diffusion inverse problem cell navigation asymptotic computations

来源：评论

学校读者我要写书评

暂无评论

A Multi-Objective Model and algorithms of Aggregate Production Planning of Multi-Product with Early and Late Delivery

引用

algorithms 2022年第6期15卷 182-182页

作者： Liu, Lanfen Yang, Xinfeng Lanzhou Jiaotong Univ Sch Traff & Transportat Engn Lanzhou 730070 Peoples R China

Due to the influence of insufficient production capacity or shortage of production materials, production enterprises may produce products in advance or be backordered. In order to improve the adaptability of enterprises and reduce production costs, the impacts of early delivery and delayed delivery are analyzed, and the method to determine the loss threshold is put forward. Moreover, the maximum allowable shortage of customers with different tardiness is calculated, and the cost of delayed delivery and loss of sales is determined. Considering the production cost, raw material cost, inventory cost, staff cost, stockout, and lost sales cost, an early/delay multi-objective optimization model is developed for an aggregate production planning (APP) problem to minimize total production costs and instability in the workforce. Three algorithms and three different hybrid strategies are designed to solve the model. Finally, some test experiments are employed in order to validate the performance of the proposed evaluation of the three algorithms. The results show that: The method of determining the loss threshold can effectively reflect the double influence of customer satisfaction with waiting time and shortage quantity. The definition of unit tardiness cost reflects the law that it increases gradually with waiting time. The determination of the feasible range of product output and the number of workers in the workforce can reduce the search scope of the algorithm and improve the efficiency of the algorithm.

关键词： aggregate production planning multi objective multi-product late delivery hybrid algorithms hybrid strategies

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：