Chapter 3 OPTIMIZATION 3.1 Introduction Optimization is a chronic and natural process usually witnessed in our daily life events. In various disciplines such as engineering designs, manufacturing systems, agricultural sciences, physical sciences, economics, pattern recognition etc. optimization is observed. Optimization is, thus a process of making best, effective and functional solution out of possible choices no way differs from the structural optimization which is being conceived in the present
deterioration, its management is a critical issue, for current and future generation. During the past three decades, many groundwater management models were developed by several researchers by linking groundwater flow/transport simulation model with optimization model (Shamir et al.,1984; Ahlfeld et al., 1986; Lefkoff and Gorelick, 1986; Willis and Finney, 1988; Finney and Samsuhadi, 1992; Emch and Yeh, 1998; Zheng and Wang, 2002,Wu and Zhu, 2006; Ayvaz,2009;Gaur et al.,2011a; Gaur et al.,2011b; Ghandour
ABSTRACT. In this modern science world, the usage of power is very high. As the usage is increased, the power demand is also gets increased. In order to comprise/compensate the power demand, different forms of power sources are preferred. Dispatchable energy resources (non-renewable energy sources) are the sources can be turned on and off in short amount of time and it is generated from different techniques. Non-dispatchable energy resources (renewable energy resources) includes the nuclear power
1. INTRODUCTION Optimization, in simple terms, means minimize the cost incurred and maximize the profit such as resource utilization. EAs are population based metaheuristic (means optimize problem by iteratively trying to improve the solution with regards to the given measure of quality) optimization algorithms that often perform well on approximating solutions to all types of problem because they do not make any assumptions about the underlying evaluation of the fitness function. There are many
necessary to choose a suitable algorithm for the training purpose based on the type of the data. The selection of a method depends primarily on the type of the data as the field of machine learning is data driven. The next important aspect is the optimization of the chosen machine learning
1. INTRODUCTION 1.1 BRIEF INTRODUCTION Grid computing is defining the combination of computer resources from many numbers of administrative domains to reach the existing goal. The grids have heterogeneity and geographic disreputability in its resources. Grids can solve grand challenge applications using the Computer Modeling, Simulation and Analysis. Grids can be available in the form of distributed computing and differs from the other architectures like as a cluster. Grid computing can overcome
• Operational restrictions at light load may assist the coordination calculations e.g. most large HV motors would not be running, hence their starting performances need not be considered, when switchboard feeder circuit breakers are being examined. • When all the overcurrent curves are plotted for the main generators, transformer feeders, large motors and downstream feeders, they tend to be located ‘close together’, and without much room for adjustment. 1. Radial System • The specific protective