A novel intelligent method for task scheduling in multiprocessor systems using genetic algorithm

Static task scheduling in multiprocessor frameworks is one of the well-defined NP hard problem. Due to optimal utilization of processors and in addition investing less time, the scheduling of tasks in multiprocessor frameworks is of extraordinary significance. To solve NP hard problem using traditional strategies takes reasonable measures of time. Over the time, various heuristic procedures were presented for comprehending it. Therefore, heuristic methods such as genetic algorithms are appropriate methods for task scheduling in multiprocessor system. In this paper, a new GA for static task scheduling in multiprocessor systems has been presented whose priority of tasks’ execution is based on the height of task in graph and other mentioned parameters and then scheduling is performed. This proposed method is simulated and then compared with basic genetic algorithm.

Fault-tolerant LPT task scheduling in multiprocessor systems

We develop a new method to map (i.e. allocate and schedule) real-time applications into certain multiprocessor systems. Its objectives are: the minimization of the number of processors used; and the minimization of the deadline missing time. Given a parallel program with real time constraints and a multiprocessor system, our method finds schedules of the program in the system which satisfy all the real time constraints with minimum number of processors. The minimization is carried out through a Pareto-based genetic algorithm which independently considers the both goals, because they are non-commensurable criteria. Experimental results show that our scheduling algorithm achieved better performance than previous ones. The advantage of our method is that the algorithm produces not a single solution but a family of solutions known as the Pareto-optimal set, out of which designers can select optimal solutions appropriate for their environmental conditions.

Based on Latin Hypercube Sampling method, the crossover operation in GA is redesigned; combined with immune mechanism, chromosome concentration is defined and clonal selection strategy is designed, thus an Immune Genetic Algorithm is given based on Latin Hypercube Sampling for solving the Maximum Clique Problem in this paper. The examples shows the new algorithm in solution quality, convergence speed, and other indicators is better than the classical genetic algorithm and good point set genetic algorithm. On the other hand, the new algorithm is not inferior to such classical algorithms as dynamic local search, and it got better solutions to some examples.KeywordsMaximum Clique Problem(MCP)Genetic Algorithm (GA)Latin Hypercube Sampling(LHS)Artificial Immune Systems(AIS)Immune Genetic Algorithm based on Latin Hypercube Sampling(LIGA)

In Thailand, the cost in transporting cane from farms to mill is a large portion of sugar cane production cost. In order to reduce the transportation cost, many methods have been proposed. One of the methods is to fix the number of trucks used in system. The trucks that will be used to carry sugar cane from farms to mill in the coming year crop must be registered. The registered trucks must be allocated to harvesting regions to assure that all mature cane can be carried and the mill gets enough cane. In this paper, two algorithms based on genetic algorithm have been developed to allocate trucks. The first algorithm is based on classical genetic algorithm concept, while the second algorithm is the genetic algorithm with weighted chromosome encoding (WCE). By applying the first method, a low utilization of chromosomes and many infeasible solutions have been found. In the second algorithm, the weighting system has been applied to assist in allocating trucks. By improving GA with WCE, a high utilization of chromosomes and many feasible solutions are obtained. In addition, the computation time is more efficient and the solution is also more reasonable than that obtained from the classical algorithm.

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

This paper proposed and implemented an genetic and weed invasion hybrid algorithm in Sparse Array Synthesis. The classical weed invasion algorithm has high requirements on the convergence parameters in the process of generating weeds from seeds. Inappropriate parameters can easily lead to local convergence of evolution. The mutation and crossover operations similar to the genetic algorithm were introduced into the classical weed invasion algorithm. The method improves convergence speed, avoids local convergence, and enhances the stability of the algorithm. The algorithm is used in practical application in the synthesis of linear sparsely distributed array pattern, and the predetermined index is obtained through calculation. At the same time, compared with the genetic algorithm and the classical weed invasion algorithm, the results show that the hybrid algorithm has certain advantages.

Genetic algorithm is heuristic searching algorithm which based on nature selection of mechanism and nature genetic. The basic concept that inspires the genetic algorithm is that evolution theory. In the process of searching the best solution on classic genetic algorithm often occurs optimum local. Optimum local is a common problem which is often occurs in genetic algorithm, and one of the reasons is that because of the population diversity. If population diversity is too low is led to optimum local, and if it is too high caused more times to look for the best solution. Mutation operator plays an important role in the process of genetic algorithm to manage that population diversity and an important element of mutation operator is mutation rate. On classic genetic algorithm that mutation rate is set in the beginning while the process of genetic algorithm depends on how many generations are. Therefore is needed to control mutation rate in generation. Controlling mutation operator, especially mutation rate based on Fuzzy Logic Controller (FLC) to manage population diversity, not too high or too low, in order to get optimal result. Evaluation is done 10 times execution by comparing the performance of standard genetic algorithm (GA), IAG and genetic algorithm based on Fuzzy Logic Controller (FLC) and experimental results show that there is an improvement on genetic algorithm performance based on FLC. Keywords: Genetic Algorithm, Fuzzy Logic Controller (FLC), Mutation Rate

As faults are unavoidable in large scale multiprocessor systems, it is important to be able to determine which units of the system are working and which are faulty. System-level diagnosis is a long-standing realistic approach to detect faults in multiprocessor systems. Diagnosis is based on the results of tests executed on the system units. In this work we evaluate the performance of evolutionary algorithms applied to the diagnosis problem. Experimental results are presented for both the traditional genetic algorithm (GA) and specialized versions of the GA. We then propose and evaluate specialized versions of Estimation of Distribution Algorithms (EDA) for system-level diagnosis: the compact GA and Population-Based Incremental Learning both with and without negative examples. The evaluation was performed using four metrics: the average number of generations needed to find the solution, the average fitness after up to 500 generations, the percentage of tests that got to the optimal solution and the average time until the solution was found. An analysis of experimental results shows that more sophisticated algorithms converge faster to the optimal solution.

With the rapid development of the social economy, the requirements for the production efficiency of job shop have also increased, but the production efficiency of the traditional flow shop is no longer suitable for the efficient speed of economic development. Therefore, it is an urgent task to study the problem of optimal scheduling of flow shop. In this study, the problem of optimal scheduling of the flow shop was taken as the main research subject, the machining sequence and maximum completion time were taken as the key points, a method of optimal scheduling by genetic algorithm (GA) was proposed, and a mathematical model for optimal scheduling of the flow shop based on GA was established. The optimal processing sequence of the six jobs in the production was obtained: 2–1–6–5–4–3; the average completion time was 381.6[Formula: see text]min, and the optimal solution frequency was 0.8. Moreover, the total processing time of the GA-based flow shop was shorter than that of the traditional flow shop. This work provides a new route for the optimal scheduling of flow shop.

Scheduling tasks in a multiprocessor system is found to be a NP-hard problem and a considerable amount of time is used up when it is solved using conventional techniques. Therefore, evolutionary algorithms like Genetic Algorithms (GA) have been explored for scheduling tasks in a multiprocessor system. GA can be implemented in various manners. This paper investigates the performance of GA with two different selection operators. This paper also studies how introducing elitism effects the performance of GA. Extensive simulations have been carried out in order to find the better candidate among the two selection operators. The decision is made depending on stability of the GA output, the rate of convergence of output and the ability of GA to give an output which is as close as possible to the actual output.

We consider using hardware and software redundancy to guarantee task deadlines in a hard real-time mu1 tiprocessor system even in the presence of processor failures. A set of scheduling requirements for the real-time fault-tolerant multiprocessor scheduling problem is first identified and a heuristic algorithm is then proposed to solve the problem. Experimental results show that the algorithm finds optimal solutions in most of the cases.

One of the main problems in modern mathematical modeling is to obtain high-precision solutions of boundary value problems. This study proposes a new approach that combines the methods of artificial intelligence and a classical analytical method. The use of the analytical method of fictitious canonic regions is proposed as the basis for obtaining reliable solutions of boundary value problems. The novelty of the approach is in the application of artificial intelligence methods, namely, genetic algorithms, to select the optimal location of fictitious canonic regions, ensuring maximum accuracy. A general genetic algorithm has been developed to solve the problem of determining the global minimum for the choice and location of fictitious canonic regions. For this genetic algorithm, several variants of the function of crossing individuals and mutations are proposed. The approach is applied to solve two test boundary value problems: the stationary heat conduction problem and the elasticity theory problem. The results of solving problems showed the effectiveness of the proposed approach. It took no more than a hundred generations to achieve high precision solutions in the work of the genetic algorithm. Moreover, the error in solving the stationary heat conduction problem was so insignificant that this solution can be considered as precise. Thus, the study showed that the proposed approach, combining the analytical method of fictitious canonic regions and the use of genetic optimization algorithms, allows solving complex boundary-value problems with high accuracy. This approach can be used in mathematical modeling of structures for responsible purposes, where the accuracy and reliability of the results is the main criterion for evaluating the solution. Further development of this approach will make it possible to solve with high accuracy of more complicated 3D problems, as well as problems of other types, for example, thermal elasticity, which are of great importance in the design of engineering structures.

This paper is a comparative between classical, hybrid and heuristic shortest path algorithms. Shortest path algorithms are necessary in graph theory and has different applications in operations research, artificial intelligence, networking, and transportation. In dynamic or huge graphs, the deterministic correctness and limitations of classical algorithms such as Dijkstra's and Bellman-Ford have been investigated. The current research examined the flexibility and scalability of heuristic techniques like Ant Colony Optimization, A*, and Genetic Algorithms. Furthermore, examined the potential to improve and enhance the accuracy of hybrid algorithms that include characteristics from both categories has been studied. This work employs important evaluation standards, for instance, optimality, time complexity, and scalability, to inspect the theoretical underpinnings, practical applications, and performance compromises of each strategy.

This paper forms the annealing genetic hybrid algorithm, which is about the genetic algorithm and simulated annealing algorithm improved and integrated. For the problem about feature selection of nonlinear analog circuit fault diagnosis based on Volterra kernel, using annealing genetic hybrid algorithm to research, put forward annealing genetic intelligent selection method of circuit fault diagnosis feature. Experiments show that this method has realized effective selection of analog circuit diagnostic features and improved the prediction accuracy of fault diagnosis system based on BP neural network.