Current Issue : October - December Volume : 2016 Issue Number : 4 Articles : 5 Articles
In the pursuit of finding subclasses of the makespan minimization problem on unrelated\nparallel machines that have approximation algorithms with approximation ratio better than 2, the\ngraph balancing problem has been of current interest. In the graph balancing problem each job can\nbe non-preemptively scheduled on one of at most two machines with the same processing time on\neither machine. Recently, Ebenlendr, KrÃ?â?¡cÃ?¡l, and Sgall (Algorithmica 2014, 68, 62ââ?¬â??80.) presented a\n7/4-approximation algorithm for the graph balancing problem. Let r, s âË?Ë? Z+. In this paper we\nconsider the graph balancing problem with two weights, where a job either takes r time units or\ns time units. We present a 3/2-approximation algorithm for this problem. This is an improvement\nover the previously best-known approximation algorithm for the problem with approximation\nratio 1.652 and it matches the best known inapproximability bound for it....
Deadlock prevention policy is widely used to design the liveness-enforcing supervisors because of its advantage that deadlocks are considered and solved in design and planning stages for flexible manufacturing systems modeled with Petri nets. However, how to evaluate the implementation cost of these liveness-enforcing supervisors is not done in the existing literature. This article proposes an algorithm to evaluate the implementation cost performance of different liveness-enforcing supervisors designed by deadlock prevention policy. By designing a multiple objective linear programming problem associated with two parameters (denoted as f1 and f2) to characterize the corresponding implementation costs for the added control places and the related input and out transitions and control arcs, the proposed algorithm first obtains the variable regions of f1 and f2 And then a satisfactory level coefficient (denoted as �» ) concentrating on the optimal compromise solutions of f1 and f2 (denoted as f1* and f2*) is solved by a linear programming problem. As a result, the implementation cost performance of the corresponding liveness-enforcing supervisor can be indicated conveniently on the basis of the values of �» , f1*, and f2*. The practical potential of the proposed algorithm is demonstrated via a theoretical analysis and several widely used examples from the existing literature....
The K-means method is one of the most widely used clustering methods and has been implemented\nin many fields of science and technology. One of the major problems of the k-means algorithm is that\nit may produce empty clusters depending on initial center vectors. Genetic Algorithms (GAs) are\nadaptive heuristic search algorithm based on the evolutionary principles of natural selection and\ngenetics. This paper presents a hybrid version of the k-means algorithm with GAs that efficiently\neliminates this empty cluster problem. Results of simulation experiments using several data sets\nprove our claim....
A fireworks algorithm (FWA) is a recent swarm intelligence algorithm that is inspired\nby observing fireworks explosions. An adaptive fireworks algorithm (AFWA) proposes additional\nadaptive amplitudes to improve the performance of the enhanced fireworks algorithm (EFWA).\nThe purpose of this paper is to add opposition-based learning (OBL) to AFWA with the goal of further\nboosting performance and achieving global optimization. Twelve benchmark functions are tested in\nuse of an opposition-based adaptive fireworks algorithm (OAFWA). The final results conclude that\nOAFWA significantly outperformed EFWA and AFWA in terms of solution accuracy. Additionally,\nOAFWA was compared with a bat algorithm (BA), differential evolution (DE), self-adapting control\nparameters in differential evolution (jDE), a firefly algorithm (FA), and a standard particle swarm\noptimization 2011 (SPSO2011) algorithm. The research results indicate that OAFWA ranks the highest\nof the six algorithms for both solution accuracy and runtime cost....
An algorithm for characterizing attosecond extreme ultraviolet pulses that is not bandwidth-limited,\nrequires no interpolation of the experimental data, and makes no approximations beyond the strongfield\napproximation is introduced. This approach fully incorporates the dipole transition matrix\nelement into the retrieval process. Unlike attosecond retrieval methods such as phase retrieval by\nomega oscillation filtering (PROOF), or improved PROOF, it simultaneously retrieves both the\nattosecond and infrared (IR) pulses, without placing fundamental restrictions on the IR pulse\nduration, intensity or bandwidth. The new algorithm is validated both numerically and experimentally,\nand is also found to have practical advantages. These include an increased robustness to noise,\nand relaxed requirements for the size of the experimental dataset and the intensity of the streaking\npulse....
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