In scheduling, from both theoretical and practical points of view, a set of machines in Parallel is a setting that is important. From the theoretical viewpoint, it is a generalization of the single machine scheduling problem. From the practical point of view, the occurrence of resources in Parallel is common in real-world. When machines are computers, a Parallel program is necessary because the members of the program are performed in a Parallel fashion, and this performance is executed according to some precedence relationship. This paper shows the problem of allocating a number of non-identical tasks in a multi-processor or multicomputer system. The model assumes that the system consists of a number of identical processors, and only one task may be executed on a processor at a time. Moreover, all schedules and tasks are non-preemptive.

algorithmic skeleton has received attention as an efficient method of Parallel programming in recent years. Using the method, the programmer can implement Parallel programs easily. In this study, a set of efficient algorithmic skeletons is introduced for use in implementing Parallel genetic algorithm (PGA).A performance model is derived for each skeleton that makes the comparison of skeletons possible in order to select the best one for the application. The performance of the selected skeleton can be increased by specifying the virtual topology required by the application. This is a novel approach with no precedent Nesting of skeletons used here is another novelty of the study which has been employed only in few previous studies.

This study focuses on the investigation of intelligent form-finding and vibration analysis of a triangular polyhedral tensegrity that is enclosed within a sphere and subjected to external loads. The nonlinear dynamic equations of the system are derived using the Lagrangian approach and the finite element method. The proposed form-finding approach, which is based on a basic genetic algorithm, can determine regular or irregular tensegrity shapes without dimensional constraints. Stable tensegrity structures are generated from random configurations and based on defined constraints (nodes located on the sphere, Parallelism, and area of upper and lower surfaces), and shape finding is performed using the fitness function of the genetic algorithm and multi-objective optimization goals. The genetic algorithm's efficacy in determining the shape of structures with unpredictable configurations is evaluated in two distinct scenarios: one involving a known connection matrix and the other involving fixed or random member positions (struts and cables). The shapes obtained from the algorithm suggested in this study are validated using the force density approach, and their vibration characteristics are examined. The findings of the comparative study demonstrate the efficacy of the proposed methodology in determining the vibrational behavior of tensegrity structures through the utilization of intelligent shape seeking techniques.

An efficient Parallel strategy is presented for optimization of the aerodynamic shapes using genetic algorithm (GA). The method is a hybrid Parallel genetic algorithm (PGA) that combines a multi-population PGA and master-slave PGA using Message Passing Interface. GA parameters are firstly tuned according to the fact that subpopulations evolve independently. The effect of the number of sub-population on the computational time is investigated. Finally, a new strategy is presented based on the load balancing that aims to decrease the idle time of the processors. The algorithm is used for optimization of a transonic airfoil. An unstructured grid finite volume flow solver is utilized for objective function evaluations. For the considered class of problems, the suggested Hierarchical Parallel genetic algorithm (HPGA) results in more than 30% reduction in optimization time in comparison to regular master-slave PGA. A semi-liner speed-up is also obtained which indicates that the model is suited for modern cluster work stations.

Over the past few decades great efforts were made to solve uncertain hybrid optimization problems. The n-Queen problem is one of such problems that many solutions have been proposed for. The traditional methods to solve this problem are exponential in terms of runtime and are not acceptable in terms of space and memory complexity. In this study, Parallel genetic algorithms are proposed to solve the n-Queen problem. Parallelizing island genetic algorithm and Cellular genetic algorithm was implemented and run. The results show that these algorithms have the ability to find related solutions to this problem. The algorithms are not only faster but also they lead to better performance even without the use of Parallel hardware and just running on one core processor. Good comparisons were made between the proposed method and serial genetic algorithms in order to measure the performance of the proposed method. The experimental results show that the algorithm has high efficiency for large-size problems in comparison with genetic algorithms, and in some cases it can achieve super linear speedup. The proposed method in the present study can be easily developed to solve other optimization problems.

Minimizing mean tardiness by Job scheduling on Parallel robots is very important in the scheduling domain. In this problem, there is a series of n-number independent jobs which are ready to be scheduled at the time of zero. Corresponding to each work, the processing time and duration date are determined. The aim of this approach is to find the order of jobs on the robots for minimizing the mean tardiness. This problem is in the class of NP-Hard combinational problems. genetic algorithm is well known an effective tool for solving combinational optimization problems. In this study, an adaptive nonlinear genetic algorithm as well as two heuristic crossover and mutation operators are used. In the algorithm, there is a fitness function based on the mean tardiness. Therefore, the algorithm which can make the crossover and mutation probability adjusted adaptively and nonlinearly can avoid disadvantage such as premature convergence, low convergence speed and low stability. Experimental results demonstrate that the proposed genetic algorithm does not get stuck at a local optimum easily and yet it converges fast and is simple to implement.

The wireless sensor network has recently become an intensive research focus due to its potential applications many years. Sensor placement is one of the most important issues in wireless sensor networks. An efficient placement scheme can enhance the quality of monitoring in wireless sensor networks by increasing the coverage rate of interested area. This paper presents an efficient method based on Parallel genetic algorithms to solve a sensor placement optimization problem. We modify the general master-slave Parallel genetic algorithm to improve the convergence rate of this optimization method. The results indicate the effectiveness of the proposed method in comparison with genetic algorithm, general Parallel genetic algorithm, and some well-known evolutionary algorithms.

The aim of this paper is to present four strategies to increase the accuracy and speed of optimization of truss structures under the constraint of Structural System Failure Probability (SSFP). In the first strategy based on the probability rules, a criterion is defined to avoid producing many correlated paths and obtain more accurate upper bound of SSFP. In the second strategy, the force method formulation is improved and employed to analyze trusses with different topologies. In the third strategy two intelligent agents are utilized to identify the repeated paths and determine the fitness of the best chromosomes in each generation. Using the fourth strategy, the chromosomes, whose SSFP is much larger or smaller than the allowable value, are identified during the analysis and the analysis is terminated at that stage.

In this paper, we study different methods of solving joint redundancy-availability optimization for series-Parallel systems with multi-state components. We analyzed various effective factors on system availability in order to determine the optimum number and version of components in each sub-system and consider the effects of improving failure rates of each component in each sub-system and improving reliability of each sub-system. The target is to determine optimum values of all variables for improving the availability level and decreasing the total cost of the system. At first, the exact values of variables are determined using a mathematical model, then, the results of SAParallel, VDO-Parallel and genetic algorithms are compared with the exact solution.