Optimized SCC Processor by Using Parallel Genetic Algorithms

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

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.

In this article we present a comparison of the performance between a metaheuristic optimization method, Game of Patterns (GofP), so-called by the author, and the well-known genetic algorithms (GAs), through two implementations, namely: the GA of Scilab (SGA); and the GA of the R Project for Statistical Computing (RGA). For this purpose, we have selected a set of multimodal objective functions in the n-dimensional Euclidean space \(\mathbb {R}^{n}\) with a unique global minimum. For comparing both metaheuristic optimization approaches, a performance indicator of quality, denoted Q(p, n, s), was defined, which allows us to measure the quality of the obtained global optimal solution for each pth problem, in the n-dimensional space, when it is solved by each metaheuristic optimization method \(s\in \{\texttt {GofP},\texttt {SGA},\texttt {RGA}\}\). The indicator Q(p, n, s) then depends on: the number of evaluations of the pth optimization problem in the Euclidean space \(\mathbb {R}^{n}\), which has required the s metaheuristic optimization method for identifying the global minimum; and the distance between the location of its respective unique global minimum and the location of the minimum that has been identified by the s metaheuristic optimization method. The paper also offers a brief explanation of the GofP method, which has been developed for solving unconstrained mixed integer problems in the \(n\times m\)-dimensional Euclidean space \(\mathbb {R}^{n}\times \mathbb {Z}^{m}\).

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.

We investigated the usefulness of a parallel genetic algorithm for phylogenetic inference under the maximum-likelihood (ML) optimality criterion. Parallelization was accomplished by assigning each "individual" in the genetic algorithm "population" to a separate processor so that the number of processors used was equal to the size of the evolving population (plus one additional processor for the control of operations). The genetic algorithm incorporated branch-length and topological mutation, recombination, selection on the ML score, and (in some cases) migration and recombination among subpopulations. We tested this parallel genetic algorithm with large (228 taxa) data sets of both empirically observed DNA sequence data (for angiosperms) as well as simulated DNA sequence data. For both observed and simulated data, search-time improvement was nearly linear with respect to the number of processors, so the parallelization strategy appears to be highly effective at improving computation time for large phylogenetic problems using the genetic algorithm. We also explored various ways of optimizing and tuning the parameters of the genetic algorithm. Under the conditions of our analyses, we did not find the best-known solution using the genetic algorithm approach before terminating each run. We discuss some possible limitations of the current implementation of this genetic algorithm as well as of avenues for its future improvement.

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.

One of the important problems of sustainable logistics is routing vehicles in a sustainable manner, the green vehicle routing problem, or vehicle routing problems which aim to reduce CO2 emissions. In the literature research, it was seen that these problems were solved with heuristic, metaheuristic, or hyper-heuristic methods and hybrid approaches since they are in the NP-hard class. This work presents a parallel multi-process genetic algorithm that incorporates problem-specific genetic operators to minimize CO2 emissions in the capacity-constrained vehicle routing problem. Unlike previous research, the algorithm combines parallel computing with tailored genetic operators in order to enhance the diversity of solutions and speed up convergence. Genetic algorithm models were developed to minimize total distance, CO2 emissions, and both objectives simultaneously. Two genetic algorithm models were developed to minimize total distance and CO2 emissions. Experimental results using the reference CVRP examples such as A-n32-k5 and B-n44-k7 show that the proposed approach reduces CO2 emissions by 1.2% more than hybrid artificial bee colony optimization, 1.3% more than ant colony optimization, and 4% more than the traditional genetic algorithm. Experimental results using benchmark CVRP instances demonstrate that the proposed approach outperforms hybrid artificial bee colony optimization, ant colony optimization, and traditional genetic algorithms for most of the test cases. This is done by exploiting multi-core processors, and the parallel architecture has improved computational efficiency; the modules compare and update solutions against the global optimum. Results obtained show that prioritizing CO2 emissions as the only objective yields better results compared to multi-objective models. This study makes two significant contributions to the literature: (1) it introduces a novel parallel genetic algorithm framework optimized for CO2 emission reduction, and (2) it provides empirical evidence underscoring the advantages of emission-focused optimization in CVRP.

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.

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.

A genetic similarity algorithm for searching the Gene Ontology terms and annotating anonymous protein sequences

Evolution in Time and Space – The Parallel Genetic Algorithm

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.

Flexible job scheduling problem (JSP) as an optimization problem, tends to find solution for allowing different operations to be processed faster. This problem could be solved by genetic algorithm, as we have proven in another experiment. Now, we have tried to outperform state of the art, by using parallel genetic algorithm. Parallel genetic algorithm has two types and we have chosen the most popular one coarsed grained genetic algorithm, for our specific case. The results have improved time wise and are promising in some of the datasets, while a need exists for improving on other ones. In the future, we will compare both versions of parallel genetic algorithms but also compare the results to another algorithm.

With the introduction of Splash, Splash 2, PAM, and other reconfigurable computers, a wide variety of algorithms can now be feasibly constructed in hardware. In this paper, we describe the Splash 2 Parallel Genetic Algorithm (SPGA), which is a parallel genetic algorithm for optimizing symmetric traveling salesman problems (TSPs) using Splash 2. Each processor in SPGA consists of four Field Programmable Gate Arrays (FPGAs) and associated memories and was found to perform 6.8 to 10.6 times the speed of equivalent software on a state-of-the-art workstation. Multiple processor SPGA systems, which use up to eight processors, find good TSP solutions much more quickly than single processor and software-based implementations of the genetic algorithm. The four-processor island-parallel SPGA implementation out performed all other SPGA configurations tested. We conclude noting that the described parallel genetic algorithm appears to be a good match for reconfigurable computing machines and that Splash 2's various interconnect resources and support for linear systolic and MIMD computing models was important for the implementation of SPGA.