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meta-heuristics for Feature Selection and Classification in Diagnostic Breast Cancer

Computers, Materials & Continua 2022年第10期73卷 749-765页

作者： Doaa Sami Khafaga Amel Ali Alhussan El-Sayed M.El-kenawy Ali E.Takieldeen Tarek M.Hassan Ehab A.Hegazy Elsayed Abdel Fattah Eid Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Communications and Computers Engineering Department Faculty of EngineeringDelta University for Science and TechnologyInternational Coastal RoadGamasa CityDakhliyaEgypt Faculty of Applied Health Science Technology Delta University for Science and TechnologyMansoura35712Egypt Faculty of Medicine Head of Internal Medicine DepartmentDelta University for Science and TechnologyMansouraEgypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

One of the most common kinds of cancer is breast *** early detection of it may help lower its overall rates of *** this paper,we robustly propose a novel approach for detecting and classifying breast cancer regions in thermal *** proposed approach starts with data preprocessing the input images and segmenting the significant regions of *** addition,to properly train the machine learning models,data augmentation is applied to increase the number of segmented regions using various scaling *** the other hand,to extract the relevant features from the breast cancer cases,a set of deep neural networks(VGGNet,ResNet-50,AlexNet,and GoogLeNet)are *** resulting set of features is processed using the binary dipper throated algorithm to select the most effective features that can realize high classification *** selected features are used to train a neural network to finally classify the thermal images of breast *** achieve accurate classification,the parameters of the employed neural network are optimized using the continuous dipper throated optimization *** results show the effectiveness of the proposed approach in classifying the breast cancer cases when compared to other recent approaches in the ***,several experiments were conducted to compare the performance of the proposed approach with the other *** results of these experiments emphasized the superiority of the proposed approach.

关键词： Breast cancer image segmentation dipper throated optimization feature selection meta-heuristics

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Q-Learning-Assisted meta-heuristics for Scheduling Distributed Hybrid Flow Shop Problems

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Computers, Materials & Continua 2024年第9期80卷 3573-3589页

作者： Qianyao Zhu Kaizhou Gao Wuze Huang Zhenfang Ma Adam Slowik Institute of Systems Engineering Macao University of Science and TechnologyMacao99078China Department of Electronics and Computer Science Koszalin University of TechnologyKoszalin75-453Poland

The flow shop scheduling problem is important for the manufacturing *** flow shop scheduling can bring great benefits to the ***,there are few types of research on Distributed Hybrid Flow Shop Problems(DHFSP)by learning assisted *** work addresses a DHFSP with minimizing the maximum completion time(Makespan).First,a mathematical model is developed for the concerned ***,four Q-learning-assisted meta-heuristics,e.g.,genetic algorithm(GA),artificial bee colony algorithm(ABC),particle swarm optimization(PSO),and differential evolution(DE),are *** to the nature of DHFSP,six local search operations are designed for finding high-quality solutions in local *** of randomselection,Q-learning assists meta-heuristics in choosing the appropriate local search operations during ***,based on 60 cases,comprehensive numerical experiments are conducted to assess the effectiveness of the proposed *** experimental results and discussions prove that using Q-learning to select appropriate local search operations is more effective than the random *** verify the competitiveness of the Q-learning assistedmeta-heuristics,they are compared with the improved iterated greedy algorithm(IIG),which is also for solving *** Friedman test is executed on the results by five *** is concluded that the performance of four Q-learning-assisted meta-heuristics are better than IIG,and the Q-learning-assisted PSO shows the best competitiveness.

关键词： Distributed scheduling hybrid flow shop meta-heuristics local search Q-learning

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Trust Based meta-heuristics Workflow Scheduling in Cloud Service Environment

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Circuits and Systems 2016年第4期7卷 520-531页

作者： G. Jeeva Rathanam A. Rajaram Department of Electronics and Communication Engineering SRS College of Engineering and Technology Salem Tamil Nadu India

Cloud computing has emerged as a new style of computing in distributed environment. An efficient and dependable Workflow Scheduling is crucial for achieving high performance and incorporating with enterprise systems. As an effective security services aggregation methodology, Trust Work-flow Technology (TWT) has been used to construct composite services. However, in cloud environment, the existing closed network services are maintained and functioned by third-party organizations or enterprises. Therefore service-oriented trust strategies must be considered in workflow scheduling. TWFS related algorithms consist of trust policies and strategies to overcome the threats of the application with heuristic workflow scheduling. As a significance of this work, trust based meta heuristic workflow scheduling (TMWS) is proposed. The TMWS algorithm will improve the efficiency and reliability of the operation in the cloud system and the results show that the TMWS approach is effective and feasible.

关键词： Workflow Scheduling Cloud Computing Trust Metrics meta-heuristics Trust Strategies

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Q-learning Based meta-heuristics for Solving Distribute Permutation Flowshop Problems

Q-learning Based Meta-heuristics for Solving Distribute Perm...

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第43届中国控制会议

作者： Hanxiang Zheng Kaizhou Gao Bohan Qiu Hui Yu Weiyu Tang Macau Institute of System Engineering Macau University of Science and Technology

This paper addresses the distributed permutation flowshop scheduling problems with minimize the total flowtime. First, a mathematical model is developed to describe the concerned problems. Then, three meta-heuristics, Jaya, artificial bee colony, and iterative greedy, and their variants are employed for solving the problems. To enhance the performance of the algorithms, six local search strategies and a Q-learning mechanism are designed. During the iterative process, Q-learning is used to guide algorithms selecting high-quality local search strategies. Finally, the comprehensive numerical experiments are conducted to evaluate the effectiveness of the proposed algorithms. The experimental results demonstrate that Q-learning-based local search strategies achieves can improve algorithms' performance.

关键词： Distributed permutation flowshop scheduling meta-heuristics total flowtime Q-learning

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Task assignment for minimizing application completion time using honeybee mating optimization

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Frontiers of Computer Science 2013年第3期7卷 404-415页

作者： Qinma KANG Hong HE School of Information Engineering Shandong University Weihai 264209 China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing 100190 China

Effective task assignment is essential for achieving high performance in heterogeneous distributed computing systems. This paper proposes a new technique for minimizing the parallel application time cost of task assignment based on the honeybee mating optimization （HBMO） algorithm. The HBMO approach combines the power of simulated annealing, genetic algorithms, and an effective local search heuristic to find the best possible solution to the problem within an acceptable amount of computation time. The performance of the proposed HBMO algorithm is shown by comparing it with three existing task assignment techniques on a large number of randomly generated problem instances. Experimental results indicate that the proposed HBMO algorithm outperforms the competing algorithms.

关键词： heterogeneous distributed computing task assignment task interaction graph honeybee mating optimization meta-heuristics

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Evolutionary Optimization: Pitfalls and Booby Traps

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Journal of Computer Science & Technology 2012年第5期27卷 907-936页

作者： Thomas Weise Raymond Chiong Ke Tang Nature Inspired Computation and Applications Laboratory School of Computer Science and Technology University of Science and Technology of China Hefei 230027 China Faculty of Higher Education Swinburne University of Technology 50 Melba Avenue Lilydale Victoria 3140 Australia

Evolutionary computation （EC）, a collective name rithms, is one of the fastest-growing areas in computer science. for a range of metaheuristic black-box optimization algo- Many manuals and ＂how-to＇s on the use of different EC methods as well as a variety of free or commercial software libraries are widely available nowadays. However, when one of these methods is applied to a real-world task, there can be many pitfalls and booby traps lurking certain aspects of the optimization problem that may lead to unsatisfactory results even if the algorithm appears to be correctly implemented and executed, These include the convergence issues, ruggedness, deceptiveness, and neutrality in the fitness landscape, epistasis, non-separability, noise leading to the need for robustness, as well as dimensionality and scalability issues, among others. In this article, we systematically discuss these related hindrances and present some possible remedies. The goal is to equip practitioners and researchers alike with a clear picture and understanding of what kind of problems can render EC applications unsuccessful and how to avoid them from the start.

关键词： evolutionary computing problem difficulty optimization meta-heuristics

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An efficient migrating birds optimization algorithm with idle time reduction for Type-I multi-manned assembly line balancing problem

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Journal of Systems Engineering and Electronics 2021年第2期32卷 286-296页

作者： ZHANG Zikai TANG Qiuhua LI Zixiang HAN Dayong Key Laboratory of metallurgical Equipment and Control Technology Wuhan University of Science and TechnologyWuhan 430081China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and TechnologyWuhan 430081China

Multi-manned assembly line,which is broadly utilized to assemble high volume products such as automobiles and trucks,allows a group of workers to assemble different tasks simultaneously in a multi-manned *** additional characteristic of parallel operators increases the complexity of the traditional NP-hard assembly line balancing ***,this paper formulates the Type-I multi-manned assembly line balancing problem to minimize the total number of workstations and operators,and develops an efficient migrating birds optimization algorithm embedded into an idle time reduction *** this algorithm,a new decoding mechanism is proposed which reduces the sequence-dependent idle time by some task assignment rules;three effective neighborhoods are developed to make refinement of existing solutions in the bird improvement phases;and temperature acceptance and competitive mechanism are employed to avoid being trapped in the local *** experiments suggest that the new decoding and improvements are effective and the proposed algorithm outperforms the compared algorithms.

关键词： multi-manned assembly line balancing migrating birds optimization meta-heuristics

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HAPE3D—a new constructive algorithm for the 3D irregular packing problem

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Frontiers of Information Technology & Electronic Engineering 2015年第5期16卷 380-390页

作者： Xiao LIU Jia-min LIU An-xi CAO Zhuang-le YAO School of Civil and Transportation Engineering South China University of Technology School of Information Science and Engineering Shenyang University of Technology College of Ocean Science and Engineer Shanghai Maritime University

We propose a new constructive algorithm, called HAPE3 D, which is a heuristic algorithm based on the principle of minimum total potential energy for the 3D irregular packing problem, involving packing a set of irregularly shaped polyhedrons into a box-shaped container with fixed width and length but unconstrained height. The objective is to allocate all the polyhedrons in the container, and thus minimize the waste or maximize profit. HAPE3 D can deal with arbitrarily shaped polyhedrons, which can be rotated around each coordinate axis at different angles. The most outstanding merit is that HAPE3 D does not need to calculate no-fit polyhedron(NFP), which is a huge obstacle for the 3D packing problem. HAPE3 D can also be hybridized with a meta-heuristic algorithm such as simulated annealing. Two groups of computational experiments demonstrate the good performance of HAPE3 D and prove that it can be hybridized quite well with a meta-heuristic algorithm to further improve the packing quality.

关键词： 3D packing problem Layout design Simulation Optimization Constructive algorithm meta-heuristics

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An Inverse Power Generation Mechanism Based Fruit Fly Algorithm for Function Optimization

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Journal of Systems Science & Complexity 2019年第2期32卷 634-656页

作者： LIU Ao DENG Xudong REN Liang LIU Ying LIU Bo School of Management Wuhan University of Science and Technology Center for Service Science and Engineering Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System School of Economics and Management Beihang University Academy of Mathematics and Systems Science Chinese Academy of Sciences

As a novel population-based optimization algorithm, fruit fly optimization(FFO) algorithm is inspired by the foraging behavior of fruit flies and possesses the advantages of simple search operations and easy implementation. Just like most population-based evolutionary algorithms, the basic FFO also suffers from being trapped in local optima for function optimization due to premature *** this paper, an improved FFO, named IPGS-FFO, is proposed in which two novel strategies are incorporated into the conventional FFO. Specifically, a smell sensitivity parameter together with an inverse power generation mechanism(IPGS) is introduced to enhance local exploitation. Moreover,a dynamic shrinking search radius strategy is incorporated so as to enhance the global exploration over search space by adaptively adjusting the searching area in the problem domain. The statistical performance of FFO, the proposed IPGS-FFO, three state-of-the-art FFO variants, and six metaheuristics are tested on twenty-six well-known unimodal and multimodal benchmark functions with dimension 30, respectively. Experimental results and comparisons show that the proposed IPGS-FFO achieves better performance than three FFO variants and competitive performance against six other meta-heuristics in terms of the solution accuracy and convergence rate.

关键词： Evolutionary algorithms fruit fly optimization function optimization meta-heuristics

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A Novel Binary Firefly Algorithm for the Minimum Labeling Spanning Tree Problem

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Computer Modeling in Engineering & Sciences 2020年第10期125卷 197-214页

作者： Mugang Lin Fangju Liu Huihuang Zhao Jianzhen Chen College of Computer Science and Technology Hengyang Normal UniversityHengyang421002China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002China School of Computer Science University of South ChinaHengyang421001China

Given a connected undirected graph G whose edges are labeled,the minimumlabeling spanning tree(MLST)problemis to find a spanning tree of G with the smallest number of different *** is anNP-hard combinatorial optimization problem,which is widely applied in communication networks,multimodal transportation networks,and data *** approximation algorithms and heuristics algorithms have been proposed for the *** algorithm is a new meta-heuristic *** of its simplicity and easy implementation,it has been successfully applied in various ***,the basic firefly algorithm is not suitable for discrete *** this end,a novel discrete firefly algorithm for the MLST problem is proposed in this paper.A binary operation method to update firefly positions and a local feasible handling method are introduced,which correct unfeasible solutions,eliminate redundant labels,and make the algorithm more suitable for discrete *** results show that the algorithm has good *** algorithm can be extended to solve other discrete optimization problems.

关键词： Minimum labeling spanning tree problem binary firefly algorithm meta-heuristics discrete optimization

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