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Dendritic Cell algorithm with grouping genetic algorithm for Input Signal Generation

Computer Modeling in Engineering & Sciences 2023年第6期135卷 2025-2045页

作者： Dan Zhang Yiwen Liang Hongbin Dong School of Computer Science Wuhan UniversityWuhan430072China School of Cyber Science and Engineering Wuhan UniversityWuhan430072China

The artificial immune system,an excellent prototype for developingMachine Learning,is inspired by the function of the powerful natural immune *** one of the prevalent classifiers,the Dendritic Cell algorithm(DCA)has been widely used to solve binary problems in the real *** classification of DCA depends on a data preprocessing procedure to generate input signals,where feature selection and signal categorization are themain ***,the results of these studies also show that the signal generation of DCA is relatively weak,and all of them utilized a filter strategy to remove unimportant *** filtered features and applying expertise may not produce an optimal classification *** overcome these limitations,this study models feature selection and signal categorization into feature grouping *** study hybridizes grouping genetic algorithm(GGA)with DCA to propose a novel DCA version,GGA-DCA,for accomplishing feature selection and signal categorization in a search *** GGA-DCA aims to search for the optimal feature grouping scheme without expertise *** this study,the data coding and operators of GGA are redefined for grouping *** experimental results show that the proposed algorithm has significant advantages over the compared DCA expansion algorithms in terms of signal generation.

关键词： Dendritic cell algorithm combinatorial optimization grouping problems grouping genetic algorithm

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A hybrid grouping genetic algorithm for reviewer group construction problem

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第3期38卷 2401-2411页

作者： Chen, Yuan Fan, Zhi-Ping Ma, Jian Zeng, Shuo Northeastern Univ Sch Business Adm Dept Management Sci & Engn Shenyang Peoples R China City Univ Hong Kong Dept Informat Syst Kowloon Hong Kong Peoples R China Shanghai Univ Finance & Econ Dept Informat Management & Engn Shanghai Peoples R China Univ Arizona Dept Management Informat Syst Tucson AZ 85721 USA

It is a common task to construct the reviewer group with diverse background between reviewers. This task is complicated considering the multiple criteria and sizable reviewers and groups. However, it has not been clearly addressed in the current studies. This paper investigates this problem and proposes a solution approach. In our study, this problem is firstly formulated as an integrated model that covers the situations of different group number and group size. Then, considering the computational difficulties of solving this model, the grouping genetic algorithm hybridizing the local neighborhood search heuristic is proposed. In the grouping genetic algorithm, the initialization, crossover and mutation are designed according to our problem's characteristics. Extensive numerical experiments show that the proposed algorithm is computationally efficient. Moreover, the application of the proposed algorithm on a case from NSFC also indicates its effectiveness for practical problems. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： OR in government Reviewer group construction Hybrid genetic algorithm grouping genetic algorithm Heuristics

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A grouping genetic algorithm for the microcell sectorization problem

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2004年第6期17卷 589-598页

作者： Brown, EC Vroblefski, M Virginia Tech Dept Business Informat Technol Blacksburg VA 24061 USA

The number of wireless users has steadily increased over the last decade, leading to the need for methods that efficiently use the limited bandwidth available. Reducing the size of the cells in a cellular network increases the rate of frequency reuse or channel reuse, thus increasing the network capacity. The drawback of this approach is increased costs associated with installation and coordination of the additional base stations. A code-division multiple-access network where the base stations are connected to the central station by fiber has been proposed to reduce the installation costs. To reduce the coordination costs and the number of handoffs, sectorization (grouping) of the cells is suggested. We propose a dynamic sectorization of the cells, depending on the current sectorization and the time-varying traffic. A grouping genetic algorithm is proposed to find a solution which minimizes costs. The computational results demonstrate the effectiveness of the algorithm across a wide range of problems. The GGA is shown to be a useful tool to efficiently allocate the limited number of channels available. (C) 2004 Elsevier Ltd. All rights reserved.

关键词： code-division multiple-access dynamic channel allocation genetic algorithm grouping genetic algorithm microcell sectorization wireless communication networks

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Carbon-aware distributed cloud: multi-level grouping genetic algorithm

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2015年第1期18卷 477-491页

作者： Moghaddam, Fereydoun Farrahi Moghaddam, Reza Farrahi Cheriet, Mohamed Ecole Technol Super Synchromedia Lab Montreal PQ Canada Ecole Technol Super Automat Prod Engn Dept Montreal PQ Canada

Global warming caused by greenhouse gas (GHG) emissions is one of the main concerns for both developed and developing countries. In a fast growing Information and Communication Technology industry, current energy efficiency methodologies are not sufficient for new raising problems such as optimization of complex distributed systems. Therefore, proper methodologies tailored for this type of systems could significantly reduce their GHG emissions. In this paper, a new genetic algorithm (GA) is introduced, namely multi-level grouping GA (MLGGA), which is designed for multi-level bin packing problems such as that of carbon footprint reduction in a distributed cloud over a network of data centers. The new MLGGA algorithm is tested on real data in a simulation platform, and its results are compared with other state-of-the-art methodologies. The results show a significant increase in the performance achieved by the proposed algorithm.

关键词： Cloud computing Distributed cloud Green IT Carbon footprint genetic algorithm Multi-level grouping genetic algorithm

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A grouping genetic algorithm for the multi-objective cell formation problem

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2005年第4期43卷 829-853页

作者： Yasuda, K Hu, L Yin, Y Tohoku Univ Grad Sch Econ & Management Aoba Ku Sendai Miyagi 9808576 Japan Yamagata Univ Dept Publ Policy & Social Studies Yamagata 9908560 Japan

In this research, we propose an efficient method to solve the multi-objective cell formation problem (CFP) Partially adopting Falkenauer's grouping genetic algorithm (GGA). The objectives are the minimization of both the cell load variation and intercell flows considering the machines' capacities, part volumes and part processing times on the machines. We relax the cell size constraints and solve the CFP without predetermination of the number of cells, Which is Usually difficult to predict ill a real-world CFP design. We also make some effort to improve the efficiency of our algorithm with respect to initialization of the population, fitness Valuation, and keeping crossover operator from cloning. Numerical examples are tested and comparisons are made with general genetic algorithms (GAs). The result shows that our method is effective and flexible ill both grouping machines into cells and deciding on the number of cells for the optimal Solution.

关键词： cellular manufacturing cell formation multi-objective optimization grouping genetic algorithm

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A multiple objective grouping genetic algorithm for assembly line design

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JOURNAL OF INTELLIGENT MANUFACTURING 2001年第5-6期12卷 467-485页

作者： Rekiek, B De Lit, P Pellichero, F L'Eglise, T Fouda, P Falkenauer, E Delchambre, A Free Univ Brussels CAD CAM Dept B-1050 Brussels Belgium Optimal Design B-1400 Nivelles Belgium Univ Catholique Louvain Dept Mech Engn B-1348 Louvain Belgium

The purpose of this paper is to describe some of the main problems concerning assembly line design. The focus will be on the following steps: (1) the input data preparation, (2) the elaboration of the logical layout of the line, which consists in the distribution of operations among stations along the line and an assignment of resources to the different stations, (3) finally the mapping phase using a simulation package to check the obtained results. This work presents a new method to tackle the hybrid assembly line design, dealing with multiple objectives. The goal is to minimize the total cost of the line by integrating design (station space, cost, etc.) and operation issues (cycle time, precedence constraints, availability, etc.). This paper also presents in detail a very promising approach to solve multiple objective problems. It is a multiple objective grouping genetic algorithm hybridized with the multicriteria decision-aid method PROMETHEE II. An approach to deal with user's preferences in design problems is also introduced. The essential concepts adopted by the method are described and its application to an industrial case study is presented.

关键词： assembly line design multiple objective problems grouping genetic algorithm multicriteria decision-aid

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An efficient grouping genetic algorithm for U-shaped assembly line balancing problems with maximizing production rate

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MEMETIC COMPUTING 2017年第3期9卷 213-229页

作者： Sahin, Murat Kellegoz, Talip Celal Bayar Univ Engn Fac Ind Engn Manisa Turkey Gazi Univ Engn Fac Ind Engn Ankara Turkey

U-type assembly line is one of the important tools that may increase companies' production efficiency. In this study, two different modeling approaches proposed for the assembly line balancing problems have been used in modeling type-II U-line balancing problems, and the performances of these models have been compared with each other. It has been shown that using mathematical formulations to solve medium and large size problem instances is impractical since the problem is NP-hard. Therefore, a grouping genetic and simulated annealing algorithms have been developed, and a particle swarm optimization algorithm is adapted to compare with the proposed methods. A special crossover operator that always obtains feasible offspring has been suggested for the proposed grouping genetic algorithm. Furthermore, a local search procedure based on problem-specific knowledge was applied to increase the intensification of the algorithm. A set of well-known benchmark instances was solved to evaluate the effectiveness of the proposed and existing methods. Results showed that while the mathematical formulations can only be used to solve small size instances, metaheuristics can obtain high quality solutions for all size problem instances within acceptable CPU times. Moreover, grouping genetic algorithm has been found to be superior to the other methods according to the number of optimal solutions, or deviations from the lower bound values.

关键词： Assembly line balancing U-shaped assembly lines grouping genetic algorithm Simulated annealing Production rate

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A hybrid grouping genetic algorithm for the cell formation problem

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COMPUTERS & OPERATIONS RESEARCH 2007年第7期34卷 2059-2079页

作者： James, Tabitha L. Brown, Evelyn C. Keeling, Kellie B. Virginia Polytech Inst & State Univ RB Pamplin Coll Business Dept Business Informat Technol Blacksburg VA 24061 USA

The machine-part cell formation problem consists of constructing a set of machine cells and their corresponding product families with the objective of minimizing the inter-cell movement of the products while maximizing machine utilization. This paper presents a hybrid grouping genetic algorithm for the cell formation problem that combines a local search with a standard grouping genetic algorithm to form machine-part cells. Computational results using the grouping efficacy measure for a set of cell formation problems from the literature are presented. The hybrid grouping genetic algorithm is shown to outperform the standard grouping genetic algorithm by exceeding the solution quality on all test problems and by reducing the variability among the solutions found. The algorithm developed performs well on all test problems, exceeding or matching the solution quality of the results presented in previous literature for most problems. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： machine-part cell formation grouping genetic algorithm heuristics

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A novel group recommender system for domain-independent decision support customizing a grouping genetic algorithm

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USER MODELING AND USER-ADAPTED INTERACTION 2023年第5期33卷 1113-1140页

作者： Krouska, Akrivi Troussas, Christos Sgouropoulou, Cleo Univ West Attica Dept Informat & Comp Engn Egaleo 12243 Greece

Group formation is a complex task requiring computational support to succeed. In the literature, there has been considerable effort in the development of algorithms for composing groups as well as their evaluation. The most widely used approach is the genetic algorithm, as, it can handle numerous variables, generating optimal solutions according to the problem requirements. In this study, a novel genetic algorithm was developed for forming groups using innovative genetic operators, such as a modification of 1-point and 2-point crossover, the gene and the group crossover, to improve its performance and accuracy. Moreover, the proposed algorithm can be characterized as domain-independent, as it allows any input regardless of the domain problem;i.e., whether the groups concern objects, items or people, or whether the field of application is industry, education, healthcare, etc. The grouping genetic algorithm has been evaluated using a dataset from the literature in terms of its settings, showing that the tournament selection is better to be chosen when a quick solution is required, while the introduced gene and group crossover operators are superior to the classic ones. Furthermore, the combination of up to three crossover operators is ideal solution concerning algorithm's accuracy and execution time. The effectiveness of the algorithm was tested in two grouping cases based on its acceptability. Both the students participated in forming collaborative groups and the professors participated in evaluating the groups of courses created were highly satisfied with the results. The contribution of this research is that it can help the stakeholders achieve an effective grouping using the presented genetic algorithm. In essence, they have the flexibility to execute the genetic algorithm in different contexts as many times as they want until to succeed the preferred output by choosing the number of operators for either greater accuracy or reduced execution time.

关键词： Domain-independent genetic grouping grouping genetic algorithm Group formation Optimal group recommendation

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Near optimal citywide WiFi network deployment using a hybrid grouping genetic algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第8期38卷 9543-9556页

作者： Agustin-Blas, L. E. Salcedo-Sanz, S. Vidales, P. Urueta, G. Portilla-Figueras, J. A. Univ Alcala Dept Signal Theory & Commun Madrid 28871 Spain T Syst Mexico Qual & Innovat Dept Mexico City DF Mexico Deustche Telekom Labs Berlin Germany

This paper presents the application of a Hybrid grouping genetic algorithm (HGGA) to solve the problem of deploying metropolitan wireless networks. In particular, the exploitation of the existing broadband infrastructure (e.g.. ADSL networks) by "opening up" WiFi-enabled routers to third party users, is considered to produce a complex problem, henceforth call WiFi network Design Problem or WiFiDP. The application of a HGGA to this problem produces cost-effective network deployment plans, considering real life aspects such as budget (the total cost of deployment - i.e. the cost of opening all selected DSL routers for public use - should not exceed the allocated budget) and DSL router characteristics (coverage, DSL capacity at a specific location, unit price, etc.) The hybrid grouping genetic algorithm proposed incorporates a particular encoding to tackle the WiFiDP, in which the group part also includes the type of router to be installed. Also, a modification of this encoding to consider the working frequencies of routers is presented in this paper. Moreover, a repairing and local search procedures are added to the algorithm to obtain better performance and always find viable solutions. The performance and effectiveness of the proposed HGGA is evaluated using two randomly generated WiFiDP instances (considering 1000 and 2000 users), used to perform several experiments. The comparison of the proposed HGGA results against those of a greedy optimization algorithm (previously proposed to solve the WiFiDP) shows the better performance of this approach. Finally, the application of the HGGA to real datasets in the cities of Berlin (Germany) and Torrejon de Ardoz (Spain) is also reported in the experimental part. In real conditions, the HGGA keeps performing better than previous methods. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： WiFi network planning Network optimization algorithms grouping genetic algorithm Hybrid algorithms

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