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local search optimization Immune Algorithm based on job insert method for HFSS

International Conference on Materials Engineering for Advanced Technologies (ICMEAT2011)

作者： Liu Zhifeng Qiao Peng Yang Wentong Wang Jianhua Beijing Univ Technol Coll Mech Engn & Appl Elect Technol Beijing Peoples R China

ISBN: (纸本)9783037851517

Hybrid flow shop (HFS), as a production organization mode with characteristics of high flexibility, low cost and quick replacement job, has attracted extensive attention of research units and enterprises. In order to resolve the complex HFS scheduling optimization problem, an improved immune scheduling algorithm (ISA) model is proposed in this paper. A local search optimization algorithm,which can optimize the scheduling instance, accelerate the convergence of ISA, and improve scheduling results eventually, is adopted in the model on the basis of the job insert method. The results which simulate the manufacturing model of a camshaft enterprise show that the improved algorithm model is effective.

关键词： Hybrid flow shop scheduling Immune algorithm local search optimization Camshaft

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The non-monopolize search (NO): a novel single-based local search optimization algorithm

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NEURAL COMPUTING & APPLICATIONS 2023年第10期36卷 5305-5332页

作者： Abualigah, Laith Al-qaness, Mohammed A. A. Abd Elaziz, Mohamed Ewees, Ahmed A. Oliva, Diego Cuong-Le, Thanh Al al Bayt Univ Prince Hussein Bin Abdullah Fac Informat Technol Comp Sci Dept Mafraq 25113 Jordan Ho Chi Minh City Open Univ Ctr Engn Applicat & Technol Solut Ho Chi Minh City Vietnam Zhejiang Normal Univ Coll Phys & Elect Informat Engn Jinhua 321004 Peoples R China Zagazig Univ Fac Sci Dept Math Zagazig Egypt Damietta Univ Geol Dept New Damietta 34517 Egypt Univ Guadalajara Dept Matemat CUCEI Guadalajara Jalisco Mexico Jinhua Polylechn Jinhua 321004 Zhejiang Peoples R China Ajman Univ Artificial Intelligence Res Ctr AIRC POB 346 Ajman U Arab Emirates Lebanese Amer Univ Dept Elect & Comp Engn POB 13-5053 Byblos Lebanon Middle East Univ MEU Res Unit Amman 11831 Jordan Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman 19328 Jordan Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan Emirates Int Univ Coll Engn & Informat Technol Sanaa 16881 Yemen

Several optimization-based population search methods have been proposed;they use various operators that permit exploring the search space. These methods typically suffer from local search (LS) problems and are unbalanced between exploration and exploitation. Consequently, recent researchers sought to modify the algorithms to avoid search problems using local search techniques to intensify the exploitation when is necessary. This paper proposes a novel single-based local search optimization algorithm called the non-monopolize search (NO). The NO is a single-solution metaphor-free algorithm, and its operators are designed based to explore and exploit along the iterative process. The NO works only with a candidate solution, and the operators modify the dimension to move the current solution along the search space. The NO is an effective LS method that combines the benefits of exploration with exploitation. Different from other LS, the NO can escape from suboptimal solutions thanks to the randomness incorporated into its operators. This is the main advantage of the NO. Experiments are conducted on standard benchmark functions to validate the performance of the proposed non-monopolize search optimization technique. The results are compared with other well-known methods, and the proposed NO got better results. Moreover, the proposed NO can be considered a powerful alternative to improve the optimization algorithms' performance and help avoid local search problems. Source codes of NO are publicly available at https://***/matlabcentral/fileexchange/156154-the-non-monopolize-search-no.

关键词： Non-monopolize search (NO) local search optimization Metaheuristic algorithms optimization problems

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Firefly Algorithm Order Batching Problem Based on local search optimization 24

Firefly Algorithm Order Batching Problem Based on Local Sear...

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5th International Conference on Computing, Networks and Internet of Things (CNIOT)

作者： Miao, Yumo Jia, Luyun Yu, Han Changan Univ Changan Dublin Int Coll Transportat Xian Peoples R China

ISBN: (纸本)9798400716751

Order processing efficiency determines the stability and efficiency of the whole logistics system, which is of great significance for warehouse operation management. The order batch problem(OBP) is a combinatorial optimization problem that arises in the warehouse order picking process. In this paper, we propose to use the Firefly Algorithm (FA) to solve the order batch processing problem and optimize the algorithm using local search optimization. The algorithm is utilized to verify the effectiveness and efficiency of the Firefly algorithm on the order batching problem for an instance. Also, a comparison highlights the usefulness of local search operations for optimizing the firefly algorithm. The experimental results show that the optimized FA exhibits faster convergence speed during the iteration process and is not easy to fall into the local optimum, and the final distance required by the picker is 1113 m. Compared with the unoptimized FA, the optimized algorithm significantly improves the optimization effect while maintaining a lower time overhead. Specifically, the average running time of the optimized FA is 10.8 seconds, which is a significant optimization effect with low additional time overhead. This study provides a new implementation idea for the solution of OBP problems and the application of FA.

关键词： Order Batch Problem Firefly Algorithm local search optimization Combinatorial optimization Warehouse Operations Management

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local search optimization Immune Algorithm based on job insert method for HFSS

Local search optimization Immune Algorithm based on job inse...

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The 2011 International Conference on Materials Engineering for Advanced Technologies(ICMEAT 2011)

作者： Liu Zhifeng Qiao Peng Yang Wentong Wang Jianhua College of Mechanical Engineering and Applied Electronics Technology Beijing University of Technology

Hybrid flow shop(HFS), as a production organization mode with characteristics of high flexibility, low cost and quick replacement job, has attracted extensive attention of research units and enterprisesIn order to resolve the complex HFS scheduling optimization problem, an improved immune scheduling algorithm(ISA) model is proposed in this paperA local search optimization algorithm,which can optimize the scheduling instance, accelerate the convergence of ISA, and improve scheduling results eventually, is adopted in the model on the basis of the job insert methodThe results which simulate the manufacturing model of a camshaft enterprise show that the improved algorithm model is effective.

关键词： Hybrid flow shop scheduling Immune algorithm local search optimization Camshaft

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Shift of pairwise similarities for data clustering

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MACHINE LEARNING 2023年第6期112卷 2025-2051页

作者： Haghir Chehreghani, Morteza Chalmers Univ Technol Dept Comp Sci & Engn Gothenburg Sweden

Several clustering methods (e.g., Normalized Cut and Ratio Cut) divide the Min Cut cost function by a cluster dependent factor (e.g., the size or the degree of the clusters), in order to yield a more balanced partitioning. We, instead, investigate adding such regularizations to the original cost function. We first consider the case where the regularization term is the sum of the squared size of the clusters, and then generalize it to adaptive regularization of the pairwise similarities. This leads to shifting (adaptively) the pairwise similarities which might make some of them negative. We then study the connection of this method to Correlation Clustering and then propose an efficient local search optimization algorithm with fast theoretical convergence rate to solve the new clustering problem. In the following, we investigate the shift of pairwise similarities on some common clustering methods, and finally, we demonstrate the superior performance of the method by extensive experiments on different datasets.

关键词： Unsupervised learning Clustering Shift of pairwise similarities local search optimization Correlation clustering

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Pneumonia detection from lung X-ray images using local search aided sine cosine algorithm based deep feature selection method

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2022年第7期37卷 3777-3814页

作者： Chattopadhyay, Soumitri Kundu, Rohit Singh, Pawan Kumar Mirjalili, Seyedali Sarkar, Ram Jadavpur Univ Dept Informat Technol Kolkata India Jadavpur Univ Dept Elect Engn Kolkata India Torrens Univ Ctr Artificial Intelligence Res & Optimizat 90 Bowen Terrace Fortitude Valley Qld 4006 Australia Yonsei Univ Yonser Frontier Lab Seoul South Korea Jadavpur Univ Dept Comp Sci & Engn Kolkata India

Pneumonia is a major cause of death among children below the age of 5 years, globally. It is especially prevalent in developing and underdeveloped nations where the risk factors for the disease such as unhygienic living conditions, high levels of pollution and overcrowding are higher. Radiological examination (usually X-ray scans) is conducted to detect pneumonia, yet it is prone to subjective variability and can lead to disagreements among different radiologists. To detect traces of pneumonia from X-ray images, a more robust method is therefore required, which can be achieved by using a computer-aided diagnosis (CAD) system. In this study, we develop a two-stage framework, using the combination of deep learning and optimization algorithms, which is both accurate and time-efficient. In its first stage, the proposed framework extracts feature using a customized deep learning model called DenseNet-201 following the concept of transfer learning to cope with the scanty available data. In the second stage, we then reduce the feature dimension using an improved sine cosine algorithm equipped with adaptive beta hill climbing-based local search algorithm. The optimized feature subset is utilized for the classification of "Pneumonia" and "Normal" X-ray images using a support vector machines classifier. Upon an evaluation on a publicly available data set, the proposed method demonstrates the highest accuracy of 98.36% and sensitivity of 98.79% with a feature reduction of 85.55% (74 features selected out of 512), using a five-fold cross-validation scheme. Extensive additional experiments on continuous benchmark functions as well as the CEC-2017 test suite further showcase the superiority and suitability of our proposed approach in application to real-valued optimization problems. The relevant codes for the proposed method can be found in .

关键词： deep learning local search optimization lung X-ray meta-heuristic pneumonia detection

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EEM-CRP: Energy-Efficient Meta-Heuristic Cluster-Based Routing Protocol for WSNs

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IEEE SENSORS JOURNAL 2023年第23期23卷 29679-29693页

作者： Chaurasia, Soni Kumar, Kamal Kumar, Neeraj Natl Inst Technol Uttarakhand Dept Comp Sci & Engn Srinagar 246174 India Thapar Inst Engn & Technol Dept Comp Sci & Engn Patiala 147004 Punjab India

Wireless sensor network (WSN) is an infrastructure-less network that deploys many interconnected sensors to monitor physical environmental conditions and sends data to the cloud/base station. Sensors monitor, analyze, and route data in Internet of Things (IoT)/WSNs. Nodes consume maximum energy for processing and routing the data from the physical environment. This article proposes an energy-efficient meta-heuristic cluster-based routing protocol (EEM-CRP) for WSNs to address energy issues. EEM-CRP protocol exploits dragonfly meta-heuristic algorithm for selecting optimal cluster head (CH) and route. The dragonfly algorithm (DA) is a kind of decision-making approach (DMA) where it works for exploitation and exploration. DA-based EEM-CRP protocol is divided into two parts: 1) optimal cluster head selection (OCHS) and 2) optimal route selection (ORS). OCHS is based on the exploitation aspect of DA and it selects the CH based on parameters such as node density, residual energy, and distance. ORS is based on exploration and choosing an optimal path based on the random walk/levy distribution function. It covers a large region with reduced energy consumption and overcomes network overhead issues. EEM-CRP ensures the participation of the maximum number of active sensor nodes' inflow of data without much or no delay. EEM-CRP performance is compared with LEACH, whale and gray wolves (WGWO), and hybridization of meta-heuristic algorithm for dynamic cluster-based routing protocol (HMBCR) algorithms on important factors including packet delivery ratio (PDR), delay, number of active nodes, and average residual energy (ARE). According to the simulation results, the EEM-CRP significantly exceeds the methodologies used by its rivals in terms of energy efficiency.

关键词： Clustering dragonfly algorithm (DA) global search optimization local search optimization routing.

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3D geo-graphs: Efficient flip verification for the spherical zoning problem

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DISCRETE APPLIED MATHEMATICS 2023年 338卷 329-346页

作者： Ludden, Ian G. King, Douglas M. Jacobson, Sheldon H. Univ Illinois Dept Comp Sci Champaign IL 61820 USA Univ Illinois Dept Ind & Enterprise Syst Engn Champaign IL USA

The Spherical Zoning Problem (SZP) seeks to partition a finite 3D volume comprised of convex polytope cells, or units, into k zones such that each zone's surface is spherical (i.e., homeomorphic to a sphere). A special case of the classical Connected Graph Partition Problem, SZP is motivated by applications in 3D geospatial partitioning such as airspace sectorization and oceanographic clustering. For general graph partitioning problems, local search optimization often considers the flip operation, which transfers one cell from its current zone, or part, to a neighboring zone in each iteration. Applying local search optimization to SZP requires new computational methods to assess flip feasibility;methods tailored to planar graph partitioning (e.g., for geographic districting) break down when faced with the spherical zones constraint due to inherent complexities when extending from 2D to 3D environments. This paper introduces the 3D geo-graph, a model for enforcing zone sphericity during a local search flip for solving SZP. The time complexity of SZP flip verification with the 3D geo-graph is, in the worst case, linear in the number of cells sharing some boundary with the flipped cell and linearithmic in the number of faces, edges, and vertices on the cell's surface. Computational experiments on space-filling honeycombs demonstrate that the 3D geo-graph enables efficient flip verification for large-scale SZP instances.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： Graph partitioning Combinatorial topology Combinatorial optimization local search optimization 3D clustering

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Extended Topological Active Nets

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IMAGE AND VISION COMPUTING 2013年第12期31卷 905-920页

作者： Bova, Nicola Ibanez, Oscar Cordon, Oscar European Ctr Soft Comp Mieres 33600 Asturias Spain Univ Granada Dept Comp Sci & Artificial Intelligence DECSAI E-18071 Granada Spain Univ Granada Res Ctr Informat & Commun Technol CITIC UGR E-18071 Granada Spain

Topological Active Nets are promising parametric deformable models that integrate features of region-based and boundary-based segmentation techniques. Problems associated with the complexity of the model, however, have limited their utility. This paper introduces an extension of the model, defining a new behavior for changing its topology, as well as a novel external force definition and a new local search optimization procedure. In particular, we propose a new automatic pre-processing phase, a new external energy term based on the Extended Vector Field Convolution, node movement constraints to avoid crossing links, and different procedures to perform link cuts and hole detection. Moreover, the new local search procedure also incorporates heuristics to correct the position of eventually misplaced nodes. The proposal has been tested on 18 synthetic images which present different segmentation difficulties along with 3 real medical images. Its performance has been compared with that of the original Topological Active Net optimization approach along with both state-of-the-art parametric and geometric active contours: two snakes (based on Gradient Vector Flow and Vector Field Convolution), and two level sets (Chan and Vese, and Geodesic Active Contour). Our new method outperforms all the others for the given image sets, in terms of segmentation accuracy measured by using four standard segmentation metrics. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Deformable models Active contours Snakes Level sets Chan and Vese Geodesic Active Contours Active nets Topological Active Nets Extended Topological Active Nets local search optimization Extemal force Gradient Vector Flow Vector Field Convolution

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Large-Scale Evacuation Using Subway and Bus Transit: Approach and Application in City of Toronto

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JOURNAL OF TRANSPORTATION ENGINEERING 2012年第10期138卷 1215-1232页

作者： Abdelgawad, Hossam Abdulhai, Baher Univ Toronto Dept Civil Engn Toronto ON M5S 1A4 Canada Univ Toronto Toronto Intelligent Transportat Syst Ctr Toronto ON M5S 1A4 Canada

Public transportation systems play a significant role in emergency evacuation. Therefore, this paper is geared towards harnessing subway and bus transit to alleviate congestion pressure during evacuation of busy urban areas. Routing and scheduling of transit vehicles and subway operation is envisioned as a new variant of the well-established vehicle routing problem. The model presented in this paper combines multiple variants of the traditional vehicle routing problem while reflecting on the operational characteristics during emergency evacuation, to include (1) multiple depots to better distribute the transit fleet, (2) time constraints to account for the evacuation time window, and (3) constraints for pick-up and delivery locations of evacuees. The evacuation problem is hereafter defined as a multi-depot time-constrained pick-up delivery vehicle route problem. A framework, using constraint programming and local search methods, was developed to model and solve the problem. An optimal spatio-temporal evacuation model was performed first to optimize evacuation of background vehicular traffic, generating transit travel cost (i.e., link travel times) as an input to the evacuation problem. The methodology was applied to evacuate the entire city of Toronto. The results show that the Toronto Transit Commission fleet is capable of evacuating the transit-dependent population (1.34 million) within 2 h on average. The four subway lines of the city of Toronto carry approximately 0.62 million people and can evacuate these people in less than 3 h on average. Toronto Transit Commission shuttle buses (1,320 vehicles) can evacuate the remainder of the transit-dependent population (0.72 million) in approximately 1.5 h on average. DOI: 10.1061/(ASCE)TE.1943-5436.0000371. (C) 2012 American Society of Civil Engineers.

关键词： Mass transit evacuation Multimodal emergency evacuation Constraint programming local search optimization Variant of vehicle routing problem Large-scale transit evacuation Subway and shuttle buses evacuation

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