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This invention is a novel high-speed neural network based processor for solving the 'traveling salesman' and other global optimization problems. It comprises a novel hybrid architecture employing a binary synaptic array whose embodiment incorporates the fixed rules of the problem, such as the number of cities to be visited. The array is prompted by analog voltages representing variables such as distances. The processor incorporates two interconnected feedback networks, each of which solves part of the problem independently and simultaneously, yet which exchange information dynamically.

Recent years have witnessed an increasing interest in Wireless Sensor Networks (WSNs) for various applications such as environmental monitoring and military field surveillance. WSN have a number of sensor nodes that communicate wirelessly and it deployed to gather data for various environments. But it has issue with the energy efficiency of sensor nodes and network lifetime along with packet scheduling. The target coverage problem is another problem hence the overall network performance is reduced significantly. In this research, new Markov Chain Monte Carlo (MCMC) is introduced which solves the energy efficiency of sensor nodes in HWSN. At initially graph model is modeled to represent distributed and heterogeneous (HWSNs) with each vertex representing the assignment of a sensor nodes in a subset. Modified Bat Optimization (MBAT) is proposed to maximize the number of Disjoint Connected Covers (DCC) and K Coverage (KC) known as MBAT-MDCCKC. Based on echolocation capability from the MBAT, the bat seeks an optimal path on the construction routing for packet transmission that maximizes the MDCCKC. MBAT bats thus focus on finding one more connected covers and avoids creating subsets particularly. It designed to increase the search efficiency and hence energy efficiency is improved prominently. The proposed MBAT-MDCCKC approach has been applied to a variety of HWSNs. The results show that the MBAT-MDCCKC approach is efficient and successful in finding optimal results for maximizing the lifetime of HWSNs. Experimental results show that, proposed MBAT-MDCCKC approach performs better than, TFMGA, Bacteria Foraging Optimization (BFO) based approach, Ant Colony Optimization (ACO) method, and the performance of the MBAT-MDCCKC approach is closer to the energy conserving strategy. P. V. Ravindranath | Dr. D. Maheswari"Optimal Energy and Network Lifetime Maximization using a Modified Bat Optimization Algorithm (MBAT) under Coverage Constrained Problems over Heterogeneous Wireless Sensor Networks" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-6 , October 2017, URL: http://www.ijtsrd.com/papers/ijtsrd4731.pdf Article URL: http://www.ijtsrd.com/computer-science/computer-network/4731/optimal-energy-and-network-lifetime-maximization-using-a-modified-bat-optimization-algorithm-mbat-under-coverage-constrained-problems-over-heterogeneous-wireless-sensor-networks/p-v-ravindranath

This report briefly reviews three areas of distributed data management where optimization questions naturally arise. The areas, which are in widely different stages of development, are file allocation, file usage, and data organization. (Author)

This report briefly reviews three areas of distributed data management where optimization questions naturally arise. The areas, which are in widely different stages of development, are file allocation, file usage, and data organization. (Author)

There has been extensive work in many different fields on how phenomena of interest (e.g. diseases, innovation, product adoption) diffuse through a social network. As social networks increasingly become a fabric of society, there is a need to make optimal decisions with respect to an observed model of diffusion. For example, in epidemiology, officials want to find a set of k individuals in a social network which, if treated, would minimize spread of a disease. In marketing, campaign managers try to identify a set of k customers that, if given a free sample, would generate maximal buzz about the product. In this paper, we first show that the well-known Generalized Annotated Program (GAP) paradigm can be used to express many existing diffusion models. We then define a class of problems called Social Network Diffusion Optimization Problems (SNDOPs). SNDOPs have four parts: (i) a diffusion model expressed as a GAP, (ii) an objective function we want to optimize with respect to a given diffusion model, (iii) an integer k 0 describing resources (e.g. medication) that can be placed at nodes, (iv) a logical condition VC that governs which nodes can have a resource (e.g. only children above the age of 5 can be treated with a given medication). We study the computational complexity of SNDOPs and show both NP-completeness results as well as results on complexity of approximation. We then develop an exact and a heuristic algorithm to solve a large class of SNDOP problems and show that our GREEDY-SNDOP algorithm achieves the best possible approximation ratio that a polynomial algorithm can achieve (unless P = NP). We conclude with a prototype experimental implementation to solve SNDOPs that looks at a real-world Wikipedia data set consisting of over 103,000 edges.

In this paper the minmax (regret) versions of some basic polynomially solvable deterministic network problems are discussed. It is shown that if the number of scenarios is unbounded, then the problems under consideration are not approximable within $\log^{1-\epsilon} K$ for any $\epsilon>0$ unless NP $\subseteq$ DTIME$(n^{\mathrm{poly} \log n})$, where $K$ is the number of scenarios.

Recent years have witnessed an increasing interest in Wireless Sensor Networks (WSNs) for various applications such as environmental monitoring and military field surveillance. WSN have a number of sensor nodes that communicate wirelessly and it deployed to gather data for various environments. But it has issue with the energy efficiency of sensor nodes and network lifetime along with packet scheduling. The target coverage problem is another problem hence the overall network performance is reduced significantly. In this research, new Markov Chain Monte Carlo (MCMC) is introduced which solves the energy efficiency of sensor nodes in HWSN. At initially graph model is modeled to represent distributed and heterogeneous (HWSNs) with each vertex representing the assignment of a sensor nodes in a subset. Modified Bat Optimization (MBAT) is proposed to maximize the number of Disjoint Connected Covers (DCC) and K Coverage (KC) known as MBAT-MDCCKC. Based on echolocation capability from the MBAT, the bat seeks an optimal path on the construction routing for packet transmission that maximizes the MDCCKC. MBAT bats thus focus on finding one more connected covers and avoids creating subsets particularly. It designed to increase the search efficiency and hence energy efficiency is improved prominently. The proposed MBAT-MDCCKC approach has been applied to a variety of HWSNs. The results show that the MBAT-MDCCKC approach is efficient and successful in finding optimal results for maximizing the lifetime of HWSNs. Experimental results show that, proposed MBAT-MDCCKC approach performs better than, TFMGA, Bacteria Foraging Optimization (BFO) based approach, Ant Colony Optimization (ACO) method, and the performance of the MBAT-MDCCKC approach is closer to the energy conserving strategy. P. V. Ravindranath | Dr. D. Maheswari"Optimal Energy and Network Lifetime Maximization using a Modified Bat Optimization Algorithm (MBAT) under Coverage Constrained Problems over Heterogeneous Wireless Sensor Networks" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-6 , October 2017, URL: http://www.ijtsrd.com/papers/ijtsrd4731.pdf   http://www.ijtsrd.com/computer-science/computer-network/4731/optimal-energy-and-network-lifetime-maximization-using-a-modified-bat-optimization-algorithm-mbat-under-coverage-constrained-problems-over-heterogeneous-wireless-sensor-networks/p-v-ravindranath

Recent years have witnessed an increasing interest in Wireless Sensor Networks (WSNs) for various applications such as environmental monitoring and military field surveillance. WSN have a number of sensor nodes that communicate wirelessly and it deployed to gather data for various environments. But it has issue with the energy efficiency of sensor nodes and network lifetime along with packet scheduling. The target coverage problem is another problem hence the overall network performance is reduced significantly. In this research, new Markov Chain Monte Carlo (MCMC) is introduced which solves the energy efficiency of sensor nodes in HWSN. At initially graph model is modeled to represent distributed and heterogeneous (HWSNs) with each vertex representing the assignment of a sensor nodes in a subset. Modified Bat Optimization (MBAT) is proposed to maximize the number of Disjoint Connected Covers (DCC) and K Coverage (KC) known as MBAT-MDCCKC. Based on echolocation capability from the MBAT, the bat seeks an optimal path on the construction routing for packet transmission that maximizes the MDCCKC. MBAT bats thus focus on finding one more connected covers and avoids creating subsets particularly. It designed to increase the search efficiency and hence energy efficiency is improved prominently. The proposed MBAT-MDCCKC approach has been applied to a variety of HWSNs. The results show that the MBAT-MDCCKC approach is efficient and successful in finding optimal results for maximizing the lifetime of HWSNs. Experimental results show that, proposed MBAT-MDCCKC approach performs better than, TFMGA, Bacteria Foraging Optimization (BFO) based approach, Ant Colony Optimization (ACO) method, and the performance of the MBAT-MDCCKC approach is closer to the energy conserving strategy. P. V. Ravindranath | Dr. D. Maheswari"Optimal Energy and Network Lifetime Maximization using a Modified Bat Optimization Algorithm (MBAT) under Coverage Constrained Problems over Heterogeneous Wireless Sensor Networks" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-6 , October 2017, URL: http://www.ijtsrd.com/papers/ijtsrd4731.pdf Article URL: http://www.ijtsrd.com/computer-science/computer-network/4731/optimal-energy-and-network-lifetime-maximization-using-a-modified-bat-optimization-algorithm-mbat-under-coverage-constrained-problems-over-heterogeneous-wireless-sensor-networks/p-v-ravindranath

Training neural networks involves solving large-scale non-convex optimization problems. This task has long been believed to be extremely difficult, with fear of local minima and other obstacles motivating a variety of schemes to improve optimization, such as unsupervised pretraining. However, modern neural networks are able to achieve negligible training error on complex tasks, using only direct training with stochastic gradient descent. We introduce a simple analysis technique to look for evidence that such networks are overcoming local optima. We find that, in fact, on a straight path from initialization to solution, a variety of state of the art neural networks never encounter any significant obstacles.

The purpose of the work is to formulate and solve certain optimization problems arising in the fields of engineering economics, scarce resource allocation, and transportation systems planning. The scope and structure of optimization theory is presented in order to place subsequent work in proper perspective. A branch and bound algorithm is rigorously developed which can be applied to the optimization problems of interest. A rounding operation is defined, which provides a powerful rejection rule and permits the calculation, at each stage of the solution process, of an upper bound and a feasible solution in addition to the usual lower bound.