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Presentation of a New and Beneficial Method Through Problem Solving Timing of Open Shop by Random Algorithm Gravitational Emulation Local Search
 International Journal of Computer Science Issues
, 2013
"... One of the most important problems of timing in engineering and industry is timing of open shop. The problem of timing of the open shop induces big and complicated solve space. So, this problem is a kind of NPComplete. In timing of the open shop, there some works, that each work has several operati ..."
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One of the most important problems of timing in engineering and industry is timing of open shop. The problem of timing of the open shop induces big and complicated solve space. So, this problem is a kind of NPComplete. In timing of the open shop, there some works, that each work has several operation. Each operation should do in machine whit should do in the same machine the aim of timing of the open shop is to catch a suitable timing for doing all of the operation, how that enough time to minimize to makespan. In problem solve of timing of the open shop. Until now different algorithm were presented. In this article, a new algorithm that is called TIME_GELS is presented which used of a random. Algorithm Gravitational Emulation Local Search (GELS) for following problem solving. This algorithm is basic of the random local search use of two of the four main parameter of speed and the power of gravity in physics. A suggestive algorithm compared with Genetic Algorithm and result is show that a proposed algorithm has a better efficient and finding the answer very soon.
Problem of Teaching Hidden Markov Model
"... Hidden Markov Model is a finite series of states that is continues with a probability distribution in a special state, an output can be obtained by continuous probability distribution. Since states are hidden from outside, this model is called Hidden Markov Model. In ordinary Markov Model, the posit ..."
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Hidden Markov Model is a finite series of states that is continues with a probability distribution in a special state, an output can be obtained by continuous probability distribution. Since states are hidden from outside, this model is called Hidden Markov Model. In ordinary Markov Model, the position is directly visible to observer so probabilities transference state will be the only parameters. In Hidden Markov Model, the position is not visible directly but the affected variants by the position are visible. Each state taken for a possible output will have a probability distribution. Therefore, the sequence of taken states created by HMM would provide some information about the sequence state. Hidden Markov Models will be distinguished for their instruction in identifying the temporary patterns such as speech, handwriting, identifying hint and pointing, bioinformatics and so on. In this paper, a new method based on Modified Gravitational Search Algorithm (MGSA) has been used to improve the teaching of Hidden Markov Model (HMM). The teaching of HMM is based on BaumWelch algorithm (BW). One of the problems of HMM teaching is the absence of any assurance about reaching of this algorithm to global optimum and the convergence of this method is often towards a local optimum. In this paper, the Modified Gravitational Search Algorithm has been used to exit BaumWelch from local optimum and search for other optimal points. Furthermore, we have compared the proposed algorithm with two algorithms, PSO and Ant Colony, which have been used finally in Speech Recognition.
On The Performance of the Gravitational Search Algorithm
"... Abstract—Gravitational Search Algorithms (GSA) are heuristic optimization evolutionary algorithms based on Newton's law of universal gravitation and mass interactions. GSAs are among the most recently introduced techniques that are not yet heavily explored. An early work of the authors has succ ..."
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Abstract—Gravitational Search Algorithms (GSA) are heuristic optimization evolutionary algorithms based on Newton's law of universal gravitation and mass interactions. GSAs are among the most recently introduced techniques that are not yet heavily explored. An early work of the authors has successfully adapted this technique to the cell placement problem, and shown its efficiency in producing high quality solutions in reasonable time. We extend this work by fine tuning the algorithm parameters and transition functions towards better balance between exploration and exploitation. To assess its performance and robustness, we compare it with that of Genetic Algorithms (GA), using the standard cell placement problem as benchmark to evaluate the solution quality, and a set of artificial instances to evaluate the capability and possibility of finding an optimal solution. Experimental results show that the proposed approach is competitive in terms of success rate or likelihood of optimality and solution quality. And despite that it is computationally more expensive due to its hefty mathematical evaluations, it is more fruitful on the long run.
A New Energy Consumption Algorithm with Active Sensor Selection Using GELS in Target Coverage WSN
"... In wireless sensor network, due to impossibility of replacing battery, the problem of energy and network lifetime is one of the important parameters. In asymmetric sensor networks, due to limited range of normal sensors it is not possible to communicate directly with central station by these sensors ..."
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In wireless sensor network, due to impossibility of replacing battery, the problem of energy and network lifetime is one of the important parameters. In asymmetric sensor networks, due to limited range of normal sensors it is not possible to communicate directly with central station by these sensors. In noted network, manager nodes are used which have more energy, processing power and broader telecommunication range. Connectivity and sending information to central station are done through them. The optimal selection and considering the energy of intermediate nodes to select and transmit data and also increasing network lifetime is one of the most important parts of wireless network design. In this paper, a gravitational force algorithm is used to solve the problem that is a power aware Selection algorithm in sensor network.