The classical job-shop scheduling problem (JSP) is a combinatorial optimization problem, which is among the most complicated problems in the scheduling area. The JSP has been proven to be NP-hard (Zhang et al., 2008). Flexible job-shop scheduling problem (FJSP) is a generalization of the classical JSP. It takes shape when alternative production routing is allowed in the classical job-shop (Al-Hinai, 2011). FJSP is NP-hard due to; (a) assignment decisions of operations to a subset of machines and
Scheduling algorithms say how much time is allocated to Processes and Threads. Their goal is to fulfil a number of criteria: All tasks should get their chance to use CPU resources. When time to use priority, lower priority should not be starved for higher time. The scheduler should scale well with a growing number of tasks, ideally being O(1). This is observed in the Linux kernel. Existing Scheduling Algorithms: These are off three types. They are: 1. Interactive Scheduling Algorithm 2. Batch
Genetic Algorithm Operations The basic GA that can produce acceptable results in many practical problems is composed of three operations: a. Reproduction b. Crossover c. Mutation The reproduction process is to allow the genetic information, stored in the good fitness for survive the next generation of the artificial strings, whereas the population's string has assigned a value and its aptitude in the object function. This value has the probability of being chosen as the parent in the reproduction
function of each neuron. The network maps an input vector from one space to another. In case of the network mapping it is not specified but is learned. Out of these methods of optimization, mostly chosen and the one chosen in the present study is genetic algorithm, a detailed discussion on which is been given in chapter 4.
Chapter 4 GENETIC ALGORITHM Overview Genetic Algorithm is a sequential procedure developed from the science involved in genetic behaviour organisms for optimization purpose. Working Principle of GA includes the simulation of evolution theory in which, the initial set of “population” is selected in random, and then successive "generations" of solutions are reproduced till the optimal convergence. Existence of the fittest individual and natural selection operators is the main agenda of GA process
Abstract Genetic algorithms are a randomized search method based on the biological model of evolution through mating and mutation. In the classic genetic algorithm, problem solutions are encoded into bit strings which are tested for fitness, then the best bit strings are combined to form new solutions using methods which mimic the Darwinian process of "survival of the fittest" and the exchange of DNA which occurs during mating in biological systems. The programming of genetic algorithms involves
fascinated by programming subjects such as C, OOPC and Data Structures. During my third year I also learned Java and Theory of Computation. During the fourth year, I was fascinated by subjects such as computer networks, network security and encryption algorithms like
a number of statistical algorithms had been applied to perform clustering to the data including the text documents. There are recent endeavors to enhance the performance of the clustering with the optimization based algorithms such as the evolutionary algorithms. Thus, document clustering with evolutionary algorithms became an emerging topic that gained more attention in the recent years. This paper presents an up-to-date review fully devoted to evolutionary algorithms designed for document clustering
2 Evolutionary Computation Algorithms 2.1 Introduction Evolutionary computation algorithms are based on the biology evolution theory. Have you ever heard the phrase "Survival of the fittest" - Herbert Spencer? Imagine an island of castaways and the only resource of food are coconut trees. It make sense that whoever is tall enough will feed and survive. A few years after those people will match and give birth to children with better characteristics, in our case taller. So as the years gone by and
Extrimi liernong Mechoni (ELM) [1] os e songli hoddin leyir fiid furwerd nitwurk (SLFN) ontrudacid by G. B. Haeng on 2006. In ELM, thi wioghts bitwiin onpat end hoddin niaruns end thi boes fur iech hoddin niarun eri essognid rendumly. Thi wioght bitwiin uatpat niaruns end hoddin niaruns eri giniretid asong thi Muuri Pinrusi Ginirelozid Invirsi [18]. Thos mekis ELM e fest liernong clessofoir. It sarmuants verouas tredotounel gredoint besid liernong elgurothms [1] sach es Beck Prupegetoun (BP) end
theories will discussed and tested against three buildings. The theories are: parametric design, genetic architecture and emergence, which characterize some of the contemporary architectural design approaches. One of the common designing techniques using in Architecture is parametric design. The term of parametric Design “is a methodology of using advanced visualization technology and mathematical algorithms to optimize structure and material form to advance resource efficiency and innovative solutions
overall about genetic algorithms. An introduction of algorithm is given and how and why they work is explained with help of examples. Different procedures are explained that are used in genetic algorithms Chapter-4 describes the factors that differentiate one test case from other according as their fitness. How these factors are estimated mathematically for a particular test case using an example is given. Chapter-5 describes my whole work i.e. generation of testcases using genetic algorithm. Process
of entire grid system in order to give definite grid system with its work effectiveness. The grid core service is an important part in the grid computing and the task scheduling strategy is the part of grid core service. The grid resources are required more number of tasks and the system can optimizes the resource through scheduling the tasks reasonably. Grid Computing is an ultimate framework to meet the improving methods of computational demands for the new generation. To meet the improving methods
Grid computing is became an important technology in distributed computing technology. The Concept is focused on grid computing has Load balancing, Fault tolerance and recovery from Fault failure. Grid computing is a set for techniques and methods applied for the coordinated use of multiple servers. These servers are specialized and works as a single, logic integrated system. Grid computing is defined as a technology that allows strengthening, accessing and managing IT resources in a distributed
CHAPTER TWO – LITERATURE REVIEW 2.1 Concept of Site Layout Planning The site layout in every construction sites requiring a good planning. A proper planned site layout would definitely reduce the cost and time for construction. Before planning, there are three issues needed to be considered. The first one is identify the temporary facilities needed to support the overall site operation and all the temporary facilities are not a part of the permanent structure. The next issue is to find out the shape
role in many numerical algorithms, many kinds of researches have been done to make matrix multiplication algorithms efficient. The Strassen’s matrix multiplication [4] is most widely algorithm use to reduce the complexity. Various works have been done in order to implement strassen’s algorithm in many applications. Coppersmith-Winograd algorithm was asymptotically fastest known algorithm until 2010. Strassen-Winograd’s matrix multiplication plays a vital role in scheduling memory efficiently [7]
techniques used by them varied in nature between mathematical techniques or conventional techniques to heuristic techniques. Over a past 20 years researchers had emphasized on Evolutionary Algorithms (EA) to solve construction optimization problems. Different Evolutionary Algorithms techniques such as Genetic Algorithm (GA), Ant colony method etc, had been used by many researchers to optimize the time-cost of a construction project. In this paper a detailed literature review of different approaches used
1.1 Background of Study The demand for electricity has grown due to the rapid economic development and gradual increase in the world’s population. The effective economic operation and management of electrical power generating system has always been an important concern in the electrical power industry. The growing size of power grids, huge demand and crisis of energy across the world, continuous rise in price of fossil fuel necessitate the optimal combination of generation level of power generating
for 4 queens placement The data I have gathered is in the form of execution time required to find all possible unique solutions for a given number of queens. I have used two time stamps to find the actual execution time required for this serial algorithm. I have placed one timestamp named as 'start' at the beginning of the function and one timestamp named as 'end' after completion of this function. Then I have calculated the total execution time to find all solutions by simply taking the difference
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