Research on Computer Networks Reliability Optimization by Parallel Genetic Algorithms Method

Summary form only given. This article gives a brief overview of theoretical advances, computing trends, applications and future perspectives in parallel genetic algorithms. It explains basic terms and behavior of (parallel) genetic algorithms. Genetic algorithms are easily parallelized algorithms, therefore two kinds of possible parallelism, data parallelism and control parallelism, are mentioned and described towards them. Parallelism of genetic algorithms brings many advantages and gains. Classifications of these algorithms are often based on the type of computing model, a walk strategy and the used computing machinery. Afterwards significant milestones in the theory with latest advances are briefly mentioned. Then current trends in parallel computing with stress computer architectures of parallel systems, interconnection topologies, operating systems, parallel (genetic) libraries and programming paradigms are reviewed shortly. The sufficient space is devoted to the latest applications of parallel genetic algorithms. After the discussion section, perspectives of the algorithms are predicted till the year 2005. The information in the article is segregated into two periods before and after the year 2000 in all chapters. The second period is more interesting and of higher importance, because it highlights recent research efforts and gives some hints about possible future trends. That is why we devote much space to the second period. As there is no such an overview of the recent period of parallel genetic algorithms, our investigation could be appealing and useful in many aspects.

A scalable parallel genetic algorithm for the Generalized Assignment Problem

Floorplanning is an important problem in very large-scale integrated-circuit (VLSI) design automation as it determines the performance, size, yield and reliability of VLSI chips. From the computational point of view, floorplan area minimization is an NP-hard problem. This paper presents a parallel genetic algorithm (GA) for floorplan area optimization. The parallel GA is based an island model with an asynchronous migration mechanism, and is implemented using Web services and multithreading technologies. The parallel GA is compared with a sequential GA that the parallel GA is based on. Experimental results show that the parallel GA can produce better results than the sequential GA when they use the same amount of computing resources. In addition, since the number of islands and migration interval are two important parameters that directly affect the performance of island-based parallel GAs, the impact of the two parameters on the performance of the parallel GA are empirically studied in this paper.

The Evolution of Genetic Algorithms: Towards Massive Parallelism

Evolution in Time and Space – The Parallel Genetic Algorithm

Genetic algorithms offer very good performances for solving large optimization problems, especially in the domain of error-correcting codes. However, they have a major drawback related to the time complexity and memory occupation when running on a uniprocessor computer. This paper proposes a parallel decoder for linear block codes, using parallel genetic algorithms (PGA). The good performance and time complexity are confirmed by theoretical study and by simulations on BCH(63,30,14) codes over both AWGN and flat Rayleigh fading channels. The simulation results show that the coding gain between parallel and single genetic algorithm is about 0.7 dB at BER = 10﹣5 with only 4 processors.

Wireless sensor network (WSN) technology is widely used in environment monitoring, health care, surveillance systems and unmanned space or planet exploration. This paper focuses on routing problems with data gathering by a mobile robot in a WSN, also referred to as a Traveling Salesman Problem with Neighborhoods (TSPN) or NP-hard problem. In this paper, we propose a clustering-based parallel genetic algorithm with migration (CBPGA), so that the mobile robot can gather all data from all sensors and the travel costs of the mobile robot clearly decrease. First, a clustering algorithm is used to effectively reduce the number of visited nodes, especially in situations with dense sensor distributions or large sensing radii. Next, the set of visited nodes is encoded as chromosomes by a chromosome generation algorithm (CGA), and the master-slave parallel genetic algorithm with migration is performed to more efficiently generate the near-optimal route. Lastly, a travel cost-reduction scheme is used to remove redundant travel costs. Simulation results confirm that the clustering-based parallel genetic algorithm with migration more efficiently generates a near-optimal route that reduces the travel costs of a mobile robot in robot routing problems with WSN.

Genetic algorithms(GA), which are based on the idea of natural selection and survival of the fittest, have proven successful in solving complex problems that are not easily solved through conventional methods. Parallel genetic algorithm(PGA) is an extension of the classical GA. The important aspect in PGA is migration and GA operation. This paper presents PGAs that use fuzzy algorithm and natural select concept to migration of subpopulations. Fuzzy algorithm is used to change GA operators. Experimental results show that the proposed methods exhibit good performance compared to the classical method.

Parallel generic algorithms (PGAs) have been developed to reduce the large execution times that are associated with serial generic algorithms (SGAs). They have also been used to solve larger problems and to find better solutions. A comparative analysis of five different coarse-grained PGAs is conducted using the traveling salesman problem as the basis of this case study. To make fair comparisons, all of these PGAs are based on the same baseline SGA, implemented on the same parallel machine (IBM SP2), tested on the same set of traveling salesman problem instances, and started from the same set of initial populations. As a result of the experiments conducted in this study, a particular PGA that combines a new subtour technique with a known migration approach is identified to be the best for the traveling salesman problem among the five PGAs being compared.

Genetic algorithms are adaptive search algorithms that have been shown to be robust optimization algorithms for multimodal real-valued functions and a variety of combinatorial optimization problems. In contrast to more standard search algorithms, genetic algorithms base their progress on the performance of a population of candidate solutions, rather than on a single candidate solution. The authors will concentrate on the application of genetic algorithms to the traveling salesman problem. For this problem, there exist several such algorithms, ranging from pure genetic algorithms to genetic algorithms that incorporate heuristic information. These algorithms will be reviewed and their performance contrasted. A serious drawback of genetic algorithms is their inefficiency when implemented on a sequential machine. However, due to their inherent parallel properties, they can be successfully implemented on parallel machines, resulting in considerable speedup. Parallel genetic algorithms will be reviewed and their uses in the traveling salesman problem will be indicated.

This paper presents an application of parallel genetic algorithms (PGA) to the optimal long‐range generation expansion planning. The problem can be formulated as a combinatorial optimization problem that determines the order of introduced generation units at each interval of the year. The proposed method considers introduced power limits of each technology, maximum loads at each interval, and load duration curves at each interval. Appropriate string representation for the problem is presented. Binary and decimal coding and three selection methods are compared. The method is developed on a transputer that is one of the parallel processors. The feasibility of the proposed method is demonstrated using a typical expansion problem with four technologies and five intervals. The method is then compared with conventional dynamic programming and a simple genetic algorithm with promising results.

Modern group communication based applications require multiple parameters to be considered for routing in a Cellular network. Traditional algorithms fail in the situations where these parameters frequently change due to the dynamism prevailing in the network. A new technique for topology discovery in these types of networks using ant colony optimization (ACO) has been proposed based on the restricted flooding principle. To provide a better quality of service in routing with multiple constraints, a genetic algorithm based routing has been proposed to find optimal routes within a shorter span of time than the traditional deterministic routing algorithms. Moreover, with the exponential growth in the number of mobile users, to enable a large number of users to participate in a group communication, a parallel genetic algorithm (GA) is proposed in this paper. Our simulation results show that the topology discovery using ant colony optimization is faster. The Call service rate using parallel genetic algorithm is more than that of sequential genetic algorithm and the Call blocking rate of parallel genetic algorithm is less than that of sequential genetic algorithm, for large number of routers in the network.

this paper is based on the analyses of current logistics vehicles dispatching system at home and abroad as well as a contrast between its implementation techniques. It uses genetic algorithm to solve vehicles dispatching problems in logistics. Genetic algorithm has always had its advantage in overall searching and is therefore adopted in this case. But in order to overcome the precocity of basic genetic algorithm and at the same time improve the quality of the optimum solution and cut searching time, the paper adopts the Parallel Genetic Algorithm (PGA). Based on the above theoretical research, together with an object-oriented system analysis and design method, this study designed and developed an intelligent dispatching system based on PGA and carried out a series of experimental tests. The test results show this PGA dispatching system can effectively fulfill the dispatching tasks and meet the demand of logistics dispatching center. This dispatching system is highly effective in elevating the competitiveness of small and medium-sized logistics companies.

The class of MIMD parallel computers includes multiprocessor architectures, such as UMA, COMA, and NUMA, as well as multicomputer architectures, such as MPP and COW. Despite huge technological differences, machines with different architectures may be used to run certain similar applications. An interesting category of applications that may be successfully executed on a variety of parallel architectures is that of genetic algorithms (GAs) that provide more and more attractive solution of many complex engineering problems for which classical optimization methods cannot be used. GAs prove to be parallelizable in a natural way. Parallel genetic algorithms (PGAs) highlight good balance between calculations and communications that allows obtaining good results on different classes of parallel architectures. The paper describes the results obtained in a comparative study of PGAs, implemented on a cluster of SunBlade 100 workstations (COW) and on a Sun Enterprise E10000 (UMA) computer. The study demonstrates suitability of these different architectures for the development and execution of this class of applications. Also, some relevant facts concerning the performance of PGAs are presented.

Parallel genetic algorithm in bus route headway optimization