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Annual Conference of genetic and Evolutionary Computation Conference

作者： Kim, Minkyu Aggarwal, Varun O'Reilly, Una-May Medard, Muriel MIT Informat & Decis Syst Lab 77 Massachusetts Ave Cambridge MA 02139 USA MIT Comp Sci & Artificial Intelligence Lab Cambridge MA 02139 USA

ISBN: (纸本)9781595936974

We present a genetic algorithm which is distributed in two novel ways: along genotype and temporal axes. Our algorithm first distributes, for every member of the population, a subset of the genotype to each network node, rather than a subset of the population to each. This genotype distribution is shown to offer a significant gain in running time, Then, for efficient use of the computational resources in the network, our algorithm divides the candidate solutions into pipelined sets and thus the distribution is in the temporal domain, rather that in the spatial domain. This temporal distribution may lead to temporal inconsistency in selection and replacement, however our experiments yield better efficiency in terms of the time to convergence without incurring significant penalties.

关键词： distributed genetic algorithm network coding optimization

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A distributed genetic algorithm for Swarm Robots Obstacle Avoidance 9

A Distributed Genetic Algorithm for Swarm Robots Obstacle Av...

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9th International Conference on Computer Engineering & Systems (ICCES)

作者： Rezk, Nesma M. Alkabani, Yousra Bedor, Hassan Hammad, Sherif Ain Shams Univ Fac Engn Comp & Syst Dept Cairo Egypt

ISBN: (纸本)9781479965946

Obstacle avoidance is an extremely important task in swarm robotics as it saves robots from hitting objects and being damaged. A genetic algorithm can be used to teach robots how to avoid obstacles in different environments. However the evaluation module of this genetic algorithm can be very time consuming module as each candidate solution should be evaluated N times. This paper explains the methodology used to distribute the evaluation module of genetic algorithm over a cluster of computers to speed up the algorithm. The proposed methodology can be used for any application which suffers from time consuming evaluation module. Experimental results showed that the speedup can reach 70x.

关键词： collision avoidance distributed algorithms genetic algorithms mobile robots multi-robot systems computer cluster distributed genetic algorithm evaluation module obstacle avoidance swarm robots Artificial neural networks ISO standards Ports (Computers) Protocols Robots Satellites evaluating modules genetic algorithm (GA) Obstacle avoidance Collision avoidance Robot multi-robot systems ISO standard distributed algorithms Mobile robots satellites Artificial neural networks dike swarms protocols Feasible Solution

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Music recommendation system improvement using distributed genetic algorithm 8

Music recommendation system improvement using distributed ge...

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Joint 8th International Conference on Soft Computing and Intelligent Systems (SCIS) / 17th International Symposium on Advanced Intelligent Systems (ISIS)

作者： Inoue, Masayoshi Takenouchi, Hiroshi Tokumaru, Masataka Kansai Univ Grad Sch 3-3-35 Yamate Cho Suita Osaka 5648680 Japan Fukuoka Inst Technol Fukuoka 8110295 Japan Kansai Univ 3-3-35 Yamate Cho Suita Osaka 5648680 Japan

ISBN: (纸本)9781509026784

Existing music recommendation systems don't suit user subjectivity and are not naturally intuitive. We previously proposed a music recommendation system to address these problems. Our music recommendation system uses Kansei agents and a Kansei retrieval system. The Kansei agents employ a Kansei model and are characterized by a three-layered neural network. Music fluctuation properties are defined as inputs to the neural networks of the Kansei agents. Our previously proposed system returns recommendations based on user subjectivity. However, this system incurs significant user evaluation load. In this study, we introduce a distributed genetic algorithm (DGA) as an optimization method to address this problem. We compare the proposed DGA method to our previous method by simulation. The simulation results indicate that the proposed DGA method significantly outperforms previous method.

关键词： distributed genetic algorithm Kansei retrieval Music recommendation system Neural network

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Design and Implementation of a Hybrid Intelligent System Based on Particle Swarm Optimization, Hill Climbing and distributed genetic algorithm for Node Placement Problem in WMNs: A Comparison Study 32

Design and Implementation of a Hybrid Intelligent System Bas...

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32nd IEEE International Conference on Advanced Information Networking and Applications (AINA)

作者： Sakamoto, Shinji Barolli, Admir Barolli, Leonard Takizawa, Makoto FIT Grad Sch Engn Higashi Ku 3-30-1 Wajirohigashi Fukuoka Fukuoka 8110295 Japan Aleksander Moisiu Univ Durres Dept Informat Technol L-1Rruga Currilave Durres Albania FIT Dept Informat & Commun Engn Higashi Ku 3-30-1 Wajirohigashi Fukuoka Fukuoka 8110295 Japan Hosei Univ Fac Sci & Engn Dept Adv Sci Kajino Machi Koganei Tokyo 1848584 Japan

ISBN: (纸本)9781538621950

The Wireless Mesh Networks (WMNs) have low cost and high speed wireless Internet connectivity, therefore they are becoming an important networking infrastructure. In our previous work, we implemented a hybrid intelligent system based on Particle Swarm Optimization (PSO) and Hill Climbing (HC), called WMN-PSOHC, and a simulation system based on genetic algorithm (GA), called WMN-GA, for solving node placement problem in WMNs. In this paper, we implement a new hybrid simulation system based on PSOHC and distributed GA (DGA), called WMN-PSOHC-DGA. We evaluate WMN-PSOHC-DGA system by computer simulations. The simulation results show that the WMN-PSOHC-DGA system has a better performance compared with WMN-PSODGA.

关键词： Wireless Mesh Networks Particle Swarm Optimization Hill Climbing distributed genetic algorithm Hybrid Intelligent algorithm

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Video Segmentation Using Neural Network and distributed genetic algorithm

Video Segmentation Using Neural Network and Distributed Gene...

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International Conference on Soft Computing for Problem Solving (SocProS 2011)

作者： Rai, Naveen Kumar Singh, Ashwini Mazhari, Sufian Ashraf Indian Inst Technol Guwahati Dept Elect & Elect Engn Gauhati India Hindustan Inst Technol Greater Noida Dept Elect & Commun Engn Noida India CRD Labs Head Tech Commerc Res New Delhi India

ISBN: (纸本)9788132204909

The paper presents a neural network and distributive genetic algorithm (DGA) based segmentation method which can extract and track moving objects in video. Each pixel in a frame is labeled and then this labeled configuration is modeled by MRF (Markovian random field) technique in accordance with the correlation between pixels of a frame across spatio-temporal domain. DGA is used for optimization due to its effective exploitation within search space. Chromosomes are not evolved randomly but based on the results of previous frames and only unstable chromosomes are evolved using crossover and mutation. These mutually exclusive labeled regions are fed to an unsupervised neural network which classify these regions into background and foreground with the help of sigmoid function. Each mutual exclusive region is separately modeled by neural network and wherein each pixel is modeled by two hidden layer neurons results in classification of that region of which pixels belong into either foreground or background. The parameters for hidden layer are obtained from subtraction of frames. Results are shown which confirms the efficiency of the method on both ends, resources available and time.

关键词： Background modeling distributed genetic algorithm Markov random field Neural Network Segmentation

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Applying brightness information in satellite image data search using distributed genetic algorithm

Applying brightness information in satellite image data sear...

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1st International Conference on Hybrid Information Technology

作者： Katayama, Kei Mackin, Kenneth J. Matsushita, Kotaro Nunohiro, Eiji Tokyo Univ Informat Sci Grad Sch 1200-2 Yatoh Cho Chiba 2658501 Japan

ISBN: (纸本)9780769526744

Tokyo University of Information Sciences maintains and distributes MODIS (Moderate Resolution Imaging Spectro-radiometer) satellite data as part of the research output for Frontier project. An intelligent image search system is being developed as part of the project, in order to retrieve requested images such as matching images patterns or forest and field fires extraction. The intelligent image search system applies genetic algorithm (GA) in the search algorithm. When searching for a target image area within the MODIS image database, it is possible that the search algorithm cannot match the optimal location when the brightness of the search image data and MODIS data image are very different. In order to solve this problem, we extended the search algorithm by implementing brightness information in the GA chromosome, so that brightness is adjusted within the GA image search. Further, we implemented the image search as distributed genetic algorithm search over a PC cluster network, in order to increase the search speed within the satellite image database. We tested the proposed system and verified the effectiveness of distributed genetic algorithm for the distributed MODIS satellite database search process.

关键词： satellite image data image search system brightness distributed genetic algorithm

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Solving the Energy Efficient Coverage Problem in Wireless Sensor Networks: A distributed genetic algorithm Approach with Hierarchical Fitness Evaluation

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ENERGIES 2018年第12期11卷 3526-3526页

作者： Wang, Zi-Jia Zhan, Zhi-Hui Zhang, Jun Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Guangdong Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangdong Prov Key Lab Computat Intelligence & Cy Guangzhou 510006 Guangdong Peoples R China

This paper proposed a distributed genetic algorithm (DGA) to solve the energy efficient coverage (EEC) problem in the wireless sensor networks (WSN). Due to the fact that the EEC problem is Non-deterministic Polynomial-Complete (NPC) and time-consuming, it is wise to use a nature-inspired meta-heuristic DGA approach to tackle this problem. The novelties and advantages in designing our approach and in modeling the EEC problems are as the following two aspects. Firstly, in the algorithm design, we realized DGA in the multi-processor distributed environment, where a set of processors run distributed to evaluate the fitness values in parallel to reduce the computational cost. Secondly, when we evaluate a chromosome, different from the traditional model of EEC problem in WSN that only calculates the number of disjoint sets, we proposed a hierarchical fitness evaluation and constructed a two-level fitness function to count the number of disjoint sets and the coverage performance of all the disjoint sets. Therefore, not only do we have the innovations in algorithm, but also have the contributions on the model of EEC problem in WSN. The experimental results show that our proposed DGA performs better than other state-of-the-art approaches in maximizing the number of disjoin sets.

关键词： wireless sensor networks energy efficient coverage distributed genetic algorithm

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distributed genetic algorithm for optimal planar arrays of aperture synthesis telescope

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Chinese Journal of Systems Engineering and Electronics 2004年第3期000卷

Sparse arrays of telescopes have a limited ( u, v)-plane coverage. In this paper, an optimization method for designing planar arrays of an aperture synthesis telescope is proposed that is based on distributed genetic algorithm. This distributed genetic algorithm is implemented on a network of workstations using community communication model. Such an aperture synthesis system performs with imperfection of (u, v) components caused by deviations and(or) some missing baselines. With the maximum ( u, v)-plane coverage of this rotation-optimized array, the image of the source reconstructed by inverse Fourier transform is satisfactory.

关键词： distributed genetic algorithm optical aperture synthesis optimum planar array (u v)-spectrum sampling

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distributed/Parallel genetic algorithm for Road Traffic Network Division using Step Parallelization 4

Distributed/Parallel Genetic Algorithm for Road Traffic Netw...

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Eastern European Regional Conference on the Engineering of Computer-Based Systems ECBS-EERC

作者： Potuzak, Tomas Univ West Bohemia Dept Comp Sci & Engn Fac Sci Appl NTIS European Ctr Excellence Plzen Czech Republic

ISBN: (纸本)9781467379670

In this paper, we present the step parallelization of a dividing genetic algorithm (DGA) for the parallel, distributed, and distributed/parallel computing environments. The DGA is employed in a method for road traffic network division, which we developed. The step parallelization, which performs all steps of the genetic algorithm at least partially concurrently, is an alternative to the commonly used island model for the parallelization of genetic algorithms. All three non-sequential executions of the DGA were thoroughly tested and compared to the sequential DGA. The results are also part of the paper.

关键词： traffic network division distributed genetic algorithm parallel genetic algorithm step paralellization

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distributed Stochastic algorithm Based on Enhanced genetic algorithm for Path Planning of Multi-UAV Cooperative Area Search

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第8期24卷 8290-8303页

作者： Zheng, Jibin Ding, Minghui Sun, Lu Liu, Hongwei Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China Dalian Maritime Univ Inst Informat Sci Technol Dept Commun Engn Dalian 16026 Peoples R China

Multiple unmanned aerial vehicle (Multi-UAV) cooperative area search is an important and effective means of intelligence acquisition and disaster rescue. Search path planning is a critical factor to improve multi-UAV search performance. Aiming at the search inefficiency resulting from insufficient cooperation between UAVs in existing researches, we present a novel distributed real-time search path planning method based on distributed model predictive control (DMPC) framework. Firstly, we formulate the overall search objective function in finite time domain, considering not only repeated searches, but also maintenance of connectivity and collision avoidance between UAVs. Secondly, we decompose the overall search objective function to establish a distributed constrained optimization problem (DCOP) model, so that all UAVs optimize the overall search objective by interacting with neighbors. Thirdly, aiming at the problem of falling into the local optima in existing algorithms, distributed stochastic algorithm based on enhanced genetic algorithm (DSA-EGA) is proposed to solve the established DCOP model. We design a point crossover operator and introduce anytime local search (ALS) framework that stores the global optimal solution explored. Finally, the simulation results of different benchmark problems demonstrate that the proposed DSA-EGA outperforms other state-of-the-art algorithms in terms of the quality of solution. The simulation results of cooperative area search problems illustrate that the established DCOP model improves the search efficiency by 7.7%, and DSA-EGA improves the search efficiency by 4.3% at least. In addition, we also verify that our method has high scalability.

关键词： Multi-UAV cooperative area search distributed model predictive control distributed constrained optimization problem distributed genetic algorithm

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