Implementation of a parallel Genetic Algorithm on a cluster of workstations: Traveling Salesman Problem, a case study

In this paper, we present a parallel iterative solution for large banded systems of linear equations based on incomplete LU-factorization (ILU). A master—workers parallel computing scheme is used. The proposed algorithm incurs reduced storage and communication overhead as compared to previous methods. The reduction in communication overhead has been achieved by pipelining the inter-workers message passing and limiting the communication between master and workers to initial work distribution and final results collection.The performance of the proposed algorithm is evaluated analytically and experimentally using Parallel Virtual Machine (PVM) as a parallel programming environment on a cluster of Linux work-stations. The obtained performance results show that the proposed algorithm exhibits higher efficiency for coarse grain computations corresponding to large matrix sizes and reduced number of processors.

The linear quadtree is a useful data structure for representing an image for the sake of the storage saving and further image manipulations. In this paper we propose a linear quadtree coding scheme and implement this algorithm on the parallel virtual machine (PVM). Our goal is to demonstrate the applicability of using the PVM in combining the computing power of computers in a network to solve this kind of image processing problems. The processors in the PVM are organized as a master-slave paradigm and various numbers of processors are applied for different PVM’s to compare their performances. Experimental results show that the speedup of solving this image encoding problem in parallel is quite satisfactory. With such a PVM environment which is easily accessible in the public domain, high performance computing is truly possible without additional hardware cost.

In this work a detailed study about the implementation of genetic algorithms (GAs) using parallelism and field programmable gate arrays (FPGAs) is presented. Concretely, we use the traveling salesman problem (TSP) as case study. First at all, the TSP is described as well as the GA used for solving it. Afterwards, we present the hardware implementation of this algorithm. We detail 13 different hardware versions, searching that each new version improves the previous one. Many of these improvements are based on the use of parallelism techniques. Finally, the found results are shown and analysed: hardware/software comparisons, resource use, operation frequency, etc. We conclude indicating the parallelism techniques that obtain better results and stating FPGA implementation is better when the problem size increases or when better solutions (nearer to the optimum) must be found.

Problem statement: The aim of this research is to investigate the Traveling Salesman Problems with Multi-Relations (TSPMR) in which each of the vertices has several edges and different weights. The study concerns the weights fluctuated by time in which the problem has more complexity than general TSP problem. However, this type of problem closes to the real-world situation in engineering aspect. Approach: The new genetic algorithm was developed specially for the TSPMR. With the new genetic algorithm, the new coding system is a mixer between the integer numbers (represents the vertices) and the binary (represents the edges). The experiments were done to investigate the suitable input variables, which are a population size, generation, crossover rate and mutation rate. Results: The suitable generation between 300 and 400, the suitable crossover rate between 20 and 60% and the suitable mutation rate between 30 and 80%. Conclusion: The new developed method in this research is found to efficiently solve the problem with less than 100 vertices, which can reduce 50% of the transportation cost. In case of 100 -1,000 vertices problem, the transportation cost is reduced approximately by 20%. If the vertices are more than 1,000, the transportation cost can be reduced less than 20%. For the large size of problem as 30,000 vertices, the transportation cost is reduced for 6.07%.

Parallel workstation clusters and MPI for sparse systems in computational science

The implementation of a distributed digital logic simulation algorithm on a network of workstations is presented. The simulation of digital circuits is done using a demand driven approach. The simulation is performed on a network of workstations using the Parallel Virtual Machine (PVM) software. The use of the PVM as a platform for parallel and distributed applications is assessed in the light of the test results. The feasibility of running parallel logical simulation algorithms on such platform is considered.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Tissue analysis is essential for dealing with a number of problems in cancer research. This research tries to address the problem of identifying normal, dysplastic and cancerous colonic mucosa by means of Texture analysis techniques. A genetic algorithm is used to interpret the results of those operations. Image- processing operations and genetic algorithms are both tasks requiring vast processing power. PVM (Parallel Virtual Machine), a message-passing library for distributed programming has been selected for implementing a parallel classification system. To further enhance PVM functionality a C++ object-oriented “PVM wrapper” was constructed.

Genetic algorithms (GAs) are search algorithms based on population genetics and natural selection concepts. Maintaining population variety in GAs is critical for ensuring global exploration and mitigating the risks of premature convergence. Rapid convergence to local optima is one such challenge in the application of genetic algorithms. To address this issue, we provide Cave-Surface GA (CSGA), an alternative method based on the Dual Population Genetic Algorithm and inspired by the genetic variety observed in Mexican cavefish. Through inter-population crossbreeding, CSGA increases diversity via a secondary population (Cave population) and facilitates the exchange of information between populations, effectively counteracting premature convergence. Several experiments are carried out utilizing benchmark instances of the Traveling Salesman Problem (TSP) obtained from TSPLIB, a well-known TSP problem library. Our experimental results over many TSP instances show that CSGA outperforms both classic GAs and other GAs that use diversity preservation techniques, such as Multipopulation GA (MPGA). CSGA has the potential to give promising solutions to challenging optimization issues like TSP.

Traveling salesman problem (TSP) is a problem of determining the shortest path for a salesman to take to visit all cities. Although a small number of cities is easy to solve, as the number of cities increases, it’s not possible to solve in polynomial time as it was a combinatorial nondeterministic polynomial (NP-hard) problem. Hence, this project is implementing a genetic algorithm (GA) to solve TSP using Python programming. The focus of this paper is to analyze the GA using order crossover (OX) and random crossover (RX) and propose a combination mechanism, direct combination (OX-RX) and Dynamic Linear combination (OXRX-Linear) to optimize TSP. We test GA for OX and RX in a random set of cities, up to 75 total cities. Then compare the result of the proposed combination OX-RX and OX-RXLinear. The result shows that both proposed combined mechanisms OX-RX and OX-RX-Linear improve the performance of GA in solving TSP.

This research report introduces two new hybrid genetic algorithms and compares them to solve the traveling salesman problem (TSP). The new algorithms are a combination of a genetic algorithm (GA) with a nearest neighbor algorithm (NNA). The NNA is used to influence the population of the GA and the selection of the parents in the GA. The results of the conducted experiments, performed in MATLAB environment, have shown that the new algorithms can be successfully used to solve the TSP. Depending on the size of the TSP, the new algorithms achieve better results than the initial GA, which is also used. However, in terms of selecting the best algorithm, no conclusive results were obtained and the best choice depends on the particular TSP.

The probabilistic travelling salesman problem (PTSP) is an extension of the well-known travelling salesman problem (TSP), which has been extensively studied in the field of combinatorial optimization. The goal of the TSP is to find the minimum length of a tour to all customers, given the distances between all pairs of customers whereas the objective of the PTSP is to minimize the expected length of the a priori tour where each customer requires a visit only with a given probability (Bertsimas, 1988; Bertsimas et al., 1990; Jaillet, 1985). The main difference between the PTSP and the TSP is that in the PTSP the probability of each node being visited is between 0.0 and 1.0 while in TSP the probability of each node being visited is 1.0. Due to the fact that the element of uncertainty not only exists, but also significantly affects the system performance in many real-world transportation and logistics applications, the results from the PTSP can provide insights into research in other probabilistic combinatorial optimization problems. Moreover, the PTSP can also be used to model many real-world applications in logistical and transportation planning, such as daily pickup-delivery services with stochastic demand, job sequencing involving changeover cost, design of retrieval sequences in a warehouse or in a cargo terminal operations, meals on wheels in senior citizen services, trip-chaining activities, vehicle routing problem with stochastic demand, and home delivery service under e-commerce (Bartholdi et al., 1983; Bertsimas et al., 1995; Campbell, 2006; Jaillet, 1988; Tang & Miller-Hooks, 2004). Early PTSP computational studies, dating from 1985, adopted heuristic approaches that were modified from the TSP (e.g., nearest neighbor, savings approach, spacefilling curve, radial sorting, 1-shift, and 2-opt exchanges) (Bartholdi & Platzman, 1988; Bertsimas, 1988; Bertsimas & Howell, 1993; Jaillet, 1985, 1987; Rossi & Gavioli, 1987). With its less than satisfactory performance in yielding solution quality, researchers in the recent years switch to metaheuristic methods, such as ant colony optimization (Bianchi, 2006; Branke & Guntsch, 2004), evolutionary algorithm (Liu et al., 2007), simulated annealing (Bowler et al., 2003), threshold accepting (Tang & Miller-Hooks, 2004) and scatter search (Liu, 2006, 2007, 2008). Because the genetic algorithm (GA), a conceptual framework of the population-based metaheuristic method, has been shown to yield promising outcomes for solving various complicated optimization problems in the past three decades (Back et al., 1997; Davis, 1991; O pe n A cc es s D at ab as e w w w .ite ch on lin e. co m

Adaptive data parallel computing on workstation clusters

The document clustering process groups the unstructured text documents into a predefined set of clusters in order to provide more information to the users. There are many studies conducted in clustering monolingual documents. With the enrichment of current technologies, the study of bilingual clustering would not be a problem. However clustering bilingual document is still facing the same problem faced by a monolingual document clustering which is the “curse of dimensionality”. Hence, this encourages the study of term reduction technique in clustering bilingual documents. The objective in this study is to study the effects of reducing terms considered in clustering bilingual corpus in parallel for English and Malay documents. In this study, a genetic algorithm (GA) is used in order to reduce the number of feature selected. A single-point crossover with a crossover rate of 0.8 is used. Not only that, this study also assesses the effects of applying different mutation rate (e.g., 0.1 and 0.01) in selecting the number of features used in clustering bilingual documents. The result shows that the implementation of GA does improve the clustering mapping compared to the initial clustering mapping. Not only that, this study also discovers that GA with a mutation rate of 0.01 produces the best parallel clustering mapping results compared to GA with a mutation rate of 0.1.

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). >

Objectives: The implementation of Genetic algorithm in the symmetric block cipher Advanced Encryption Standard -128 (AES-128) algorithms to enhance the performance of cryptographic operations. Methods: Genetic algorithm is used for generating the best fit non-repetitive cipher key and for key distribution to design a dynamic Substitution box in AES-128. Findings: The study reveals that the efficiency of the cryptographic algorithm treated with Genetic algorithm is dependent on the variations in the number of generations and initial population size. The result shows that an optimum population size has less encryption and decryption time. Among the sample population size taken for the experiment, almost the average population size has minimum encryption and decryption time. Results from iteration variations shows that the average number of iterations has less encryption and decryption time. Improvements: The hybrid combination of Genetic algorithm and AES-128 can be further modified for images and audio messages also. Keywords: Genetic Algorithm, Iterations, Non-Repetitive Cipher Key, Population Size, Substitution Box