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WMN-PSODGA hybrid intelligent simulation system and its web GUI tool: performance evaluation considering different parameters of genetic algorithm

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INTERNATIONAL JOURNAL OF WEB AND GRID SERVICES 2022年第2期18卷 213-228页

作者： Barolli, Admir Sakamoto, Shinji Aleksander Moisiu Univ Durres Dept Informat Technol L1 Durres Albania Kanazawa Inst Technol KIT Dept Informat & Comp Sci 7-1 Ohgigaoka Nonoichi Ishikawa 9218501 Japan

Wireless mesh networks (WMNs) have many advantages compared with the conventional wireless local area networks (WLANs) such as system resiliency, low cost, flexible coverage areas, rapid deployment, easy deployment, easy maintenance and high robustness. However, WMNs have some problems such as hidden and exposed node problems, which are related with the node placement problem that affect the network connectivity and user coverage. In order to solve these problems, we consider to optimise the location of mesh routers in WMNs. However, the mesh router placement is known to be an NP-hard problem. For this reason, we consider meta-heuristic algorithms. In this paper, we propose and evaluate a hybrid intelligent simulation system based on distributed genetic algorithm (DGA) and particle swarm optimisation (PSO). The simulation results show that the proposed system has a good performance to solve the node placement problem in WMNs. The best performance is achieved for 64 GA islands. Also, the SPX method has better performance than other crossover methods for the considered scenarios.

关键词： wireless mesh network WMN node placement problem hybrid intelligent system particle swarm optimisation PSO distributed genetic algorithm DGA

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Fast and coupled solution for cooperative mission planning of multiple heterogeneous unmanned aerial vehicles

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AEROSPACE SCIENCE AND TECHNOLOGY 2018年 79卷 131-144页

作者： Wu, Weinan Wang, Xiaogang Cui, Naigang Harbin Inst Technol Dept Aerosp Engn Harbin 150001 Heilongjiang Peoples R China

This paper studies a problem in which a fleet of heterogeneous fixed-wing unmanned aerial vehicles (UAVs) must identify the optimal flyable trajectory to traverse over multiple targets and perform consecutive tasks. To obtain a fast and feasible solution, a coupled and distributed planning method is developed that integrates the task assignment and trajectory generation aspects of the problem. With specific constraints and a relaxed Dubins path, the cooperative mission-planning problem is reformulated. A distributed genetic algorithm is then proposed to search for the optimal solution, and chromosomal genes are modified to adapt to the heterogeneous characteristic of UAVs. Then, a fixed-wing UAV model with 6 degrees of freedom (DOF) and a path-following method is used to verify this proposed mission planning method. The simulation results show that the proposed approach obtains feasible solutions and significantly improves the operating rate, with the potential for use in a real mission. (C) 2018 Elsevier Masson SAS. All rights reserved.

关键词： Heterogeneous UAVs Mission planning distributed genetic algorithm Specific constraints Coupled solution

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distributed manufacturing resource selection strategy in cloud manufacturing

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2018年第9-12期94卷 3375-3388页

作者： Wang, Lei Guo, Shunsheng Li, Xixing Du, Baigang Xu, Wenxiang Wuhan Univ Technol Sch Mech & Elect Engn Wuhan 430070 Peoples R China

With the development of the information technology and logistics industry, industrial production models are more likely to be innovated than ever before. Therefore, there is a tendency for a large number of manufacturing enterprises to start outsourcing their manufacturing activities to more professional subcontractors so they could pay more attention to their core business. Cloud manufacturing (CMfg), as a supplement to cloud computing and big data, is also a new network manufacturing mode that is service-oriented. This mode makes it even more complex and impractical to organize and optimize manufacturing resources. Considering this problem, this paper proposes a manufacturing resource selection strategy based on an improved distributed genetic algorithm (DGA) for manufacturing resource combinatorial optimization (MRCO) in CMfg. We divided the DGA into several sections and distributed and optimized the process, which not only guaranteed algorithm speed but also expanded the search range and improved the accuracy. A case study, a performance comparison between a simple genetic algorithm (SGA) and a working procedure priority-based algorithm (WPPBA) is presented later in this paper. Experimental results showed that the proposed method is preferable and a more effective choice for searching for the optimal solution.

关键词： Cloud manufacturing Manufacturing resource combinatorial optimization distributed genetic algorithm Parallel optimization

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A genetic algorithm-based segmentation of Markov random field modeled images

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IEEE SIGNAL PROCESSING LETTERS 2000年第11期7卷 301-303页

作者： Kim, EY Park, SH Kim, HJ Kyungpook Natl Univ Dept Comp Engn Taegu 702701 South Korea

An unsupervised method is presented for segmenting video sequences degraded by noise. Each frame in a sequence is modeled using a Markov random field (MRF), and the energy function of each MRF is minimized by chromosomes that evolve using distributed genetic algorithms. To improve the computational efficiency, only unstable chromosomes corresponding to moving object parts are evolved. Experimental results show the effectiveness of the proposed method.

关键词： distributed genetic algorithm energy function Markov random field (MRF)

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Effect of mutation schemes on convergence to optimum design of steel frames

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JOURNAL OF CONSTRUCTIONAL STEEL RESEARCH 2010年第7期66卷 954-961页

作者： Issa, Honar K. Mohammad, Fouad A. Nottingham Trent Univ Sch Architecture Design & Built Environm Nottingham England

In this paper an attempt is made to modify a distributed genetic algorithm (DGA) to minimize the weight of steel frames. The main aspects of modifications include the twin analogy and a number of mutation schemes. Although the mutation is a secondary operator in the GA, it plays an influential role in diversifying the population and exploring more feasible design space. In this research, four different mutation schemes are examined and their effects on convergence to optimum solutions are evaluated. Using three benchmark examples, it is demonstrated that mutation operator can efficiently influence on the speed of convergence and consequently saving computation time. As coding a program is required, the DO-DGA software has been developed to handle the algorithm. DO-DGA applies modified DCA, including different mutation schemes, to obtain the global optimum solution. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： distributed genetic algorithm Mutation schemes Structural optimization

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WMN-PSODGA - An Intelligent Hybrid Simulation System for WMNs Considering Load Balancing: A Comparison for Different Client Distributions

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INTERNATIONAL JOURNAL OF distributed SYSTEMS AND TECHNOLOGIES 2020年第4期11卷 39-52页

作者： Ohara, Seiji Qafzezi, Ermioni Barolli, Admir Sakamoto, Shinji Liu, Yi Barolli, Leonard Fukuoka Inst Technol Fukuoka Japan Aleksander Moisiu Univ Durres Durres Albania Seikei Univ Fac Sci & Technol Dept Comp & Informat Sci Musashino Tokyo Japan

Wireless mesh networks (WMNs) are becoming an important networking infrastructure because they have many advantages, such as low cost and increased high-speed wireless Internet connectivity. In the authors' previous work, they implemented a hybrid simulation system based on particle swarm optimization (PSO) and distributed genetic algorithm (DGA), called WMN-PSODGA. Moreover, they added to the fitness function a new parameter for mesh router load balancing a number of covered mesh clients per router (NCMCpR). In this article, the authors consider Exponential, Weibull, and Normal distributions of mesh clients and carry out a comparison study. The simulation results show that the performance of the Exponential, Weibull and Normal distributions was improved by considering load balancing when using WMN-PSODGA. For the same number of mesh clients, the Normal distribution behaves better than the other distributions. This is because all mesh clients are covered by a smaller number of mesh routers and the standard deviation is improved by effectively using NCMCpR.

关键词： distributed genetic algorithm Exponential Distribution Normal Distribution Particle Swarm Optimization Weibull Distribution Wireless Mesh Networks

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A web interface for WMN-PSODGA hybrid intelligent simulation system: performance evaluation for different mesh client distributions

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INTERNATIONAL JOURNAL OF WEB INFORMATION SYSTEMS 2022年第1期18卷 41-54页

作者： Barolli, Admir Sakamoto, Shinji Aleksander Moisiu Univ Durres Dept Informat Technol Durres Albania Kanazawa Inst Technol Dept Informat & Comp Sci Nonoichi Ishikawa Japan

Purpose The purpose of this paper is to implement a web interface for a hybrid intelligent system. By using the implemented web interface, one can find optimal assignments of mesh routers in wireless mesh networks (WMNs). This study evaluates the implemented system considering three distributions of mesh clients to solve the node placement problem in WMNs. Design/methodology/approach The node placement problem in WMNs is well known to be a computationally hard problem. Therefore, intelligent algorithms are used for solving this problem. The implemented system is a hybrid intelligent system based on meta-heuristics algorithms: particle swarm optimization (PSO) and distributed genetic algorithm (DGA). The proposed system is called WMN-PSODGA. Findings This study carried out simulations using the implemented simulation system. From the simulations results, it was found that the WMN-PSODGA system performs better for chi-square distribution of mesh clients compared with Weibull and exponential distributions. Research limitations/implications For simulations, three different distributions of mesh clients were considered. In the future, other mesh client distributions, number of mesh nodes and communication distance need to be considered. Originality/value This research work, different from other research works, implemented a hybrid intelligent simulation system for WMNs. This study also implemented a web interface for the proposed system, which make the simulation system user-friendly.

关键词： Wireless mesh networks Particle swarm optimization distributed genetic algorithm Hybrid intelligent system

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Implementation of a Web interface for hybrid intelligent systems A comparison study of two hybrid intelligent systems

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INTERNATIONAL JOURNAL OF WEB INFORMATION SYSTEMS 2019年第4期15卷 420-431页

作者： Sakamoto, Shinji Barolli, Admir Barolli, Leonard Okamoto, Shusuke Seikei Univ Dept Comp & Informat Sci Musashino Tokyo Japan Aleksander Moisiu Univ Durres Dept Informat Technol Durres Albania Fukuoka Kogyo Daigaku Dept Informat & Commun Engn Fukuoka Fukuoka Japan

Purpose The purpose of this paper is to implement a Web interface for hybrid intelligent systems. Using the implemented Web interface, this paper evaluates two hybrid intelligent systems based on particle swarm optimization, hill climbing and distributed genetic algorithm to solve the node placement problem in wireless mesh networks (WMNs). Design/methodology/approach The node placement problem in WMNs is well-known to be a computationally hard problem. Therefore, the authors use intelligent algorithms to solve this problem. The implemented systems are intelligent systems based on meta-heuristics algorithms: Particle Swarm Optimization (PSO), Hill Climbing (HC) and distributed genetic algorithm (DGA). The authors implement two hybrid intelligent systems: WMN-PSODGA and WMN-PSOHC-DGA. Findings The authors carried out simulations using the implemented Web interface. From the simulations results, it was found that the WMN-PSOHC-DGA system has a better performance compared with the WMN-PSODGA system. Originality/value In this research work, the authors implemented a Web interface for hybrid intelligent systems. The implemented interface can be extended for other metaheuristic algorithms.

关键词： Particle swarm optimization distributed genetic algorithm Hill climbing Hybrid intelligent system Web interface Wireless mesh networks

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distributed Modified Extremal Optimization using Island Model for Reducing Crossovers in Reconciliation Graph

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ENGINEERING LETTERS 2013年第2期21卷 81-88页

作者： Tamura, Keiichi Kitakami, Hajime Nakada, Akihiro Hiroshima City Univ Grad Sch Informat Sci Minami Ku 3-4-1 Ozuka HigashiAsa Hiroshima 7313194 Japan

To determine the mechanism of molecular evolution, molecular biologists need to carry out reconciliation work. In reconciliation work, they compare the relation between two heterogeneous phylogenetic trees and the relation between a phylogenetic tree and a taxonomic tree are compared. Phylogenetic trees and taxonomic trees are referred to as ordered trees and a reconciliation graph is constructed from two ordered trees. In the reconciliation graph, the leaf nodes of the two ordered trees face each other. Furthermore, leaf nodes with the same label name are connected to each other by an edge. To carry out reconciliation work efficiently, it is necessary to find the state with the minimum number of crossovers of edges between leaf nodes. Reducing crossovers in a reconciliation graph is the combinatorial optimization problem that finds the state with the minimum number of crossovers. In this paper, we propose a novel bio-inspired heuristic called distributed modified extremal optimization (DMEO) using the island model. This heuristic is a hybrid of population-based modified extremal optimization (PMEO) and the distributed genetic algorithm using the island model that is used for reducing crossovers in a reconciliation graph. We have evaluated DMEO using actual data sets. DMEO shows better performance compared with PMEO.

关键词： extremal optimization distributed genetic algorithm island model evolutionary computation reconciliation graph

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Capacity Analysis Method for MLSN Based on Improved DGA 11

Capacity Analysis Method for MLSN Based on Improved DGA

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11th International Conference on Natural Computation (ICNC) / 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Xu, Shuang Wang, Xingwei Huang, Min Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China

ISBN: (纸本)9781467376792

Capacity analysis of satellite network, especially multi-layer one, is of theoretical and practical significance for improving the network efficiency and strengthening the guaranteed ability of network services. However, existing solutions, which usually focus on Up/Down links (UDLs) of satellite network, without considering the characteristics of satellite network, e.g., dynamic topology, limited resources, periodicity, are not scalable for the capacity analysis of Inter-Satellite links (ISLs) of Multi-Layer Satellite Network (MLSN). In this paper, we present a capacity analysis method for MLSN based on an improved distributed genetic algorithm (DGA). The improved DGA, as an intelligent optimization algorithm, is used to solve the problems of network capacity analysis. Moreover, the capacity analysis method is applied in the MLSN to analyze the network capacity and extensively evaluated. Simulation results demonstrate that the method can efficiently calculate the upper bound and lower bound of network capacity with faster convergence speed, higher searching precision and ability of avoiding premature convergence. In addition, we conclude that node failures lead to the decrease of network capacity and different constellation structures have different effects on the upper bound and lower bound of network capacity.

关键词： Multi-Layer Satellite Network resources limitation distributed genetic algorithm capacity analysis

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