A Parallel Genetic Algorithm for Floorplan Area Optimization

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.

Intelligent optimization algorithm (IOA) has been widely studied and applied to solve various optimization problems. When scholars improve IOA with mathematical methods, they also want to seek an effective method to implement algorithms with higher real time, especially for a complex problem. Parallel design is an effective method to improve the real time of IOA. Currently, the parallel programming based on open multi-processing (OpenMP) and compute unified device architecture (CUDA) are two popular methods. To find and develop a new IOA parallel method, in this paper, a parallel design and implementation method based on field programmable gate array (FPGA) is explored. In order to validate the proposed method, parallel genetic algorithm (GA) and parallel particle swarm optimization (PSO) algorithm are realized by the proposed method. Furthermore, the performance and advantage of the proposed FPGA-based parallel IOA method are tested by comparing with OpenMP-based parallel programming and CUDA-based parallel programming, the final results show that the proposed method with highest real-time performance in IOA parallel implementation. A case study by using FPGA-based parallel simulate annealing (SA) to address job shop scheduling problem (JSSP) to illustrate the proposed method has high potential in industrial applications.

In response to the problems of traditional genetic algorithms in evaluating English compositions, the stability of automatic grading of English compositions has been further enhanced. This article evaluates the teaching effectiveness of automatic grading of English compositions using an optimization fusion algorithm combined with genetic optimization algorithm and CNN model. By analyzing genetic content and optimization algorithms, a corresponding fusion optimization model was obtained, and the automatic evaluation of English compositions was analyzed and predicted through experimental verification. The results indicate that the curves corresponding to different parameters exhibit typical segmentation features through the variation curves of individual numbers under different scale factors. And through quantitative description and analysis of the curve, it can be seen that the change in proportion factor has an absolute advantage in the impact of genetic algorithm on the number of children. As the number of samples increases, the performance of genetic optimization algorithms under the f function shows an upward trend. Research has shown that the writing content index has the greatest impact on English writing, while the corresponding grammar errors have the smallest impact on English writing. Finally, the accuracy of the optimized model was verified by comparing the model curve with experimental data. This study provides theoretical support for the use of genetic optimization algorithms and CNN models in English, and provides ideas for the use of optimization algorithms in other fields.

Parallel genetic algorithm in bus route headway optimization

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.

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.

The genetic algorithm is a robust search and optimization technique based on the principles of natural genetics and survival of the fittest. Genetic algorithms (GA) are a promising new approach to global optimization problems, and are applicable to a wide variety of problems. HYPERGEN was developed as a research tool for investigating parallel genetic algorithms applied to combinatorial optimization problems. It provides the user with a wide variety of options to test the particular problem at hand. In addition, HYPERGEN is modular enough for a user to insert routines of his own for special needs, or for doing further research studies on parallel GAs. HYPERGEN was used successfully to find new 'best' tours on three 'standard' TSP problems, and out-performed a parallel simulated annealing algorithm on various package placement problems. The authors found it fairly easy to fine tune the parameters that drive a parallel GA for near optimal performance (population size, migration rate, and migration interval). >

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.

Path planning of the unmanned aerial vehicle (UAV) is one of the complex optimization problems due to the model complexity and a high number of constraints. Evolutionary algorithms are a conventional method to solve complex optimization problems with multiple constraints. However, they are time-consuming algorithms which make their implementation difficult. UAV path planning is usually a real-time problem that is needed to solve in limited time steps. Parallelization is one of the most effective methods for reducing the computation time of evolutionary algorithms. In the industrial applications, usually there is a communication platform between devices which can be used for the implementation of the parallelization process. For example, controller area network (CAN) is a protocol used for reliable communication in aerospace systems. Due to the development of this protocol on UAVs and its characteristics, it can be used for parallel optimization of path planning problem. In this paper, an asynchronous distributed multi-master parallel genetic algorithm and a parallel particle swarm optimization algorithm are implemented on CAN bus to improve the speed and performance of the UAV path planning.

In this article, evolutionary algorithms (EAs) are applied to solve the radio network design problem (RND). The task is to find the best set of transmitter locations in order to cover a given geographical region at an optimal cost. Usually, parallel EAs are needed to cope with the high computational requirements of such a problem. Here, we develop and evaluate a set of sequential and parallel genetic algorithms (GAs) to solve the RND problem efficiently. The results show that our distributed steady state GA is an efficient and accurate tool for solving RND that even outperforms existing parallel solutions. The sequential algorithm performs very efficiently from a numerical point of view, although the distributed version is much faster.

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.

[EN] In this research, a new model of computing is presented, within the framework of natural computing and computer models inspired by biology: Networks of Genetic Processors (NGP). This new model is based on the one hand, by the family of networks of bio-inspired processors, specifically Networks of Evolutionary Processors (NEP) and Networks of Splicing Processors (NSP), and on the other hand by Genetic Algorithms. We can define the new model as a network of biologically-inspired processors where operations used by processors are crossover and mutation. One of the major interests studying the NGP is the computational power of the operations of crossover and mutation acting together. The NEP is a complete model that uses operations of symbol mutation: insertion, substitution and deletion, the NSP is a complete model that uses splicing operations, but the NEP is no longer a complete model using only substitution operations, as it happens to the NSP if we restrict the context of its splicing rules to the empty context. The study of the new model presented here responds to what happens when we put together in a single model the substitution rules from the NEP (called mutation rules) and the splicing rules with empty context from the NSP (called crossover rules).\n\nWhen we work with networks of biologically-inspired processors there are two basic types of networks, accepting networks and generating networks. These types of networks are mainly used to work at a theoretical level or to solve decision problems. To work on a more practical level such as solving optimization problems, we propose a new type of network, Networks of Genetic processors as Parallel Genetic Algorithms, inspired by the Parallel Genetic Algorithms.\n\nIn this work we prove the computational completeness of our new model by showing that it is equivalent to the Turing machine. We prove the computational completeness of Parallel Genetic Algorithms by using this result and the similarity between the NGP and Parallel Genetic Algorithms. Moreover, we propose a characterization of the Chomsky hierarchy by using the NGP. Here, we simulate every grammar in the language classes of the Chomsky's hierarchy by using a NGP with an small number of processors required for each simulation. Hence, it gives an appreciable idea of the descriptional complexity of the different families of languages.\n\nFinally, in this work there is an experimental study of the behavior of the model for the resolution of some practical problems. First, we design and implement a simulator that allows the execution of networks of Genetic Processors in any of its three defined types: accepting networks, generating networks or as Parallel Genetic Algorithms. This allows us to test the model with different optimization problems. Then, we make a study to see if the new model could solve NP problems in polynomial time. We use the decision problem of Hamiltonian cycle in a graph. Finally, we test the simulator with two optimization problems showing a good computational behavior. The problems are the Multidimensional Knapsack problem and the Traveling Salesman problem.

Multicast routing is an effective way to communicate among multiple hosts in a network. For multimedia applications, the routing algorithms should consider many Quality of Service (QoS) parameters such as delay, cost and so on to find a new route. However, to find routes with two or more QoS parameters is an NP-hard problem. This paper describes a new evolutionary scheme for optimization of multicast QoS routing based on the hybrid of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), called HGAPSO. In HGAPSO, individuals in a new generation are created, not only by crossover and mutation operation as in GA, but also by PSO. The upper-half of the best-performing individuals in a population are regarded as elites. Instead of being reproduced directly to the next generation, these elites are first enhanced. The group constituted by the elites is regarded as a swarm, and each elite corresponds to a particle within it. In this regard, the elites are enhanced by PSO, an operation which mimics the maturing phenomenon in nature. This scheme can simultaneously optimize the cost of the tree, the maximum end-to-end delay, the average delay and the maximum link utilization. In this way, a set of optimal solutions, known as Pareto set, is calculated in only one run, without a priori restrictions. It has revealed an efficient method of the reconstruction of multicast tree topology and the experimental results demonstrated better performance than the conventional GA optimization method.

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.