Omar Al Jadaan

Genetic algorithm for grid scheduling using best rank power

The large computing capacity provided by grid systems is beneficial for solving complex problems by using many nodes of the grid at the same time. The usefulness of a grid system largely depends, among other factors, on the efficiency of the system regarding the allocation of jobs to grid resources. This paper proposes an Roulette Wheel Selection Genetic Algorithm using Best Rank Power(PRRWSGA) for scheduling independent tasks in the grid environment. The modified algorithm speeds up convergence and shortens the search time more than IRRWSGA, at the same time the heuristic initialization of initial population using MCT algorithm allow the algorithm to obtain a high quality feasible scheduling solution. The simulation results, show that PRRWSGA has better search time than both IRRWSGA and standard genetic algorithms. Real-world scheduling problems may utilize this algorithm for better results.

An improved rank-based genetic algorithm with limited iterations for grid scheduling

In most cases, the number of resources and tasks in Grid Computing environment is large. Accordingly, the complexity of task scheduling is significantly increased. This results very complex optimization problem.

Parameterless penalty function for solving constrained evolutionary optimization

A criticism of Evolutionary Algorithms might be the lack of efficient and robust generic methods to handle constraints. The most widespread approach for constrained search problems is to use penalty methods, because of their simplicity and ease of implementation. The penalty function approach is generic and applicable to any type of constraint (linear or nonlinear). Nonetheless, the most difficult aspect of the penalty function approach is to find an appropriate penalty parameters needed to guide the search towards the constrained optimum. In this paper, GAs population-based approach and Ranks are exploited to devise a penalty function approach that does not require any penalty parameter called Adaptive GA-RRWS. Adaptive penalty parameters assignment among feasible and infeasible solutions are made with a view to provide a search direction towards the feasible region. Rank-based Roulette Wheel selection operator (RRWS) is used. The new adaptive penalty and rank-based roulette wheel selection operator allow GAs to continuously find better feasible solutions, gradually leading the search near the true optimum solution. GAs with this constraint handling approach have been tested on five problems commonly used in the literature. In all cases, the proposed approach has been able to repeatedly find solutions closer to the true optimum solution than that reported earlier.

Mutation based simulated annealing algorithm for minimizing Makespan in Grid Computing Systems

Computational grids have become attractive and promising platforms for solving large-scale high-performance applications of multi-institutional interest. However, the management of resources and computational tasks is a critical and complex undertaking as these resources and tasks are geographically distributed and are heterogeneous in nature. This paper presents a new stochastic approach for scheduling independent tasks in the grid environment by minimizing Makespan. The novel algorithm, Mutation Based Simulated Annealing Algorithm (MSA), speeds up convergence better than the previous algorithms by using the selection of Simulated Annealing, single change Mutation and a new Random Minimum Completion Time (Random-MCT) heuristic. In order to make the algorithm MSA working fast, it maintains two solutions at a time. The experiments on the algorithm MSA provide a reduction in Makespan equals to eighteen (18) when it is compared with algorithm Min-Min, and equals to three (3) when it is compared with previous genetic algorithm. The simulation results display that the assumed algorithm has better performance than previous genetic algorithm and Min-Min algorithm in terms of quality of solution and Load Balancing, as well as Resource Utilization. However, in this work the gain in average time consumed by algorithm is about 93%, which makes MSA algorithm very high QoS and more preferable for realistic scheduling in Grid environment.

Rank-Based Genetic Algorithm with Limited Iteration for Grid Scheduling

In Grid Computing the number of resources and tasks is usually very large, which makes the scheduling task very complex optimization problem. Genetic algorithms (GAs) have been broadly used to solve these NP-complete problems efficiently. On the other hand, the Standard Genetic algorithm (SGA) is too slow when used in a realistic scheduling due to its time-consuming iteration. This paper proposes a new Rank-based Roulette Wheel Selection Genetic Algorithm (RRWSGA) for scheduling independent tasks in the grid environment, which increases the performance and the quality of schedule with a limited number of iterations, RRWSGA improves the reliability in the selection process while matching an acceptable output. A fast reduction of makespan making the RRWSGA of practical concern for grid environment. The results are encouraging, and can be used for real-world scheduling problems.

Solving Constrained Multi-objective Optimization Problems Using Non-dominated Ranked Genetic Algorithm

A criticism of Evolutionary Algorithms might be the lack of efficient and robust generic methods to handle constraints. The most widespread approach for constrained search problems is to use penalty methods, because of their simplicity and ease of implementation. Nonetheless, the most difficult aspect of the penalty function approach is to find an appropriate penalty parameters. In this paper, a method combining the new Non-dominated Ranked Genetic Algorithm (NRGA), with a parameterless penalty approach are exploited to devise the search to find Pareto optimal set of solutions. The new Parameterless Penalty and the Nondominated Ranked Genetic Algorithm (PP-NRGA) continuously find better Pareto optimal set of solutions. This new algorithm have been evaluated by solving four test problems, reported in the multi-objective evolutionary algorithm (MOEA) literature. Performance comparisons based on quantitative metrics for accuracy, coverage, and spread are presented.

Task Scheduling in Grid Environment Using Simulated Annealing and Genetic Algorithm

Grid computing enables access to geographically and administratively dispersed networked resources and delivers functionality of those resources to individual users. Grid computing systems are about sharing computational resources, software and data at a large scale. The main issue in grid system is to achieve high performance of grid resources. It requires techniques to efficiently and adaptively allocate tasks and applications to available resources in a large scale, highly heterogeneous and dynamic environment.

Engineering Case Studies Using Parameterless Penalty Non-dominated Ranked Genetic Algorithm

The new elitist multi-objective genetic algorithm PPNRGA have been used for solving engineering design problems with multiple objectives. Although there exists a number of classical techniques, evolutionary algorithms (EAs) have an edge over the classical methods where they can find multiple Pareto optimal solutions in one single simulation run. The new proposed algorithm is a parameterless penalty non-dominated ranking GA (PP-NRGA), uses a fast non-dominated sorting procedure, an elitist-preserving approach, a two tier ranked based roulette wheel selection operator, and it does not require fixing any niching parameter. PP-NRGA tested on two engineering design problems borrowed from the literature, where the PP-NRGA can find a much wider spread of solutions than NSGA-II other evolutionary algorithm. The results are encouraging and suggests immediate application of the proposed method to other more complex engineering design problems.

Rank Based Genetic Algorithm for solving the Banking ATM's Location Problem using convolution

In order t o satisfy the client needs, his Utility should be increased by covering his Demand. The service Utility should be maximized through effective deployment of ATMs. Genetic Algorithm is one of widely used techniques to solve complex optimization problems, such as Banking ATMs Location Problem. This paper proposes a novel Rank Based Genetic Algorithm using convolution for solving the Banking ATMs Location Problem (RGAC). The proposed RGAC maximizes demand Coverage Percentage with less number of ATM machines. The novel RGAC speeds up the convergence using Rank Concept, with limited number of iterations to obtain a high quality feasible solution in resonable time. The proposed algorithm RGAC performs more effectively in the large scale deployments, thus it can be used in the marketing study of Banks which have highly complex operations. The simulation results show that RGAC improves the Percentage Coverage up to 16.2 over the previous algorithm [1] using the same number of ATMs. Also they exhibit that RGAC reduces the number of ATM machines up to ten (10).