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 EAs available viz. Genetic Algorithm (GA) [1] , Artificial Immune Algorithm (AIA) [2], Ant Colony Optimization (ACO) [3], Particle Swarm Optimization (PSO) [4], Differential Evolution (DE) [5, 6], Harmony Search (HS) [7], Bacteria Foraging Optimization (BFO) [8], Shuffled Frog Leaping (SFL) [9], Artificial Bee Colony (ABC) [10, 11], Biogeography-Based Optimization (BBO) [12], Gravitational Search Algorithm (GSA) [13], Grenade Explosion Method (GEM) [14] etc. To use any EA, a model of decision problem need to be built that specifies: 1) The decisions to be made, called decision variables, 2) The measure to be optimized, called the objective, and 3) Any logical restrictions on potential solutions, called constraints. These 3 parameters are necessary while building any optimization model. The solver will find values for the decision variables that satisfy the constraints while optimizing (maximizing or minimizing) the objective. But the problem with all the above EAs is that, to get optimal solution, besides the necessary parameters (explained above), many algorithms-specific parameters need to be handled appropriately. For example, in case of GA, adjustment of the algorithm-specific parameters such as crossover rate (or probability, PC), mu... ... middle of paper ... ... the algorithm are identified and modified suitably, using OpenMP, one can easily exploit the functionality of multi-core CPU and can maximize the utilization of all the cores of multi-core system which is necessary from the optimization point of view (which says, maximize the resource utilization). This paper contributes towards this direction and undertakes a detailed study by investigating the effect of number of cores, dimension size, population size, and problem complexity on the speed-up of TLBO algorithm. In the remainder of this paper, we give a brief literature review of TLBO and its applications. Thereafter, we discuss the possibilities of tweaking a TLBO to make it suitable for parallel implementation on a multi-core system. Then, we present results on few test problems of different complexities and show appreciable speed-ups using our proposed algorithm.
The tuning strategy process parameters are designed such that they do not effect 1) the maximum throughput of multi-core processors and 2) the overall execution time of workloads. There have been experiments
Helping Jessica to reach her narrative writing goals, Mrs. Tracy has several strategies at her disposal. Jessica’s joy of sharing her personal experiences and the ability to verbally express those idea is a strength that should be utilized. The POW and C.SPACE strategies discussed in the Star Sheets would be excellent tools for her to use to gather her thoughts in an orderly fashion. Her sample writing shows she has good ideas but little development and simple sentences. If Jessica is really overwhelmed by the writing task I would start her with a simple organizer to outline the beginning, middle, and end of her story (see attached organizer). Because Jessica is comfortable verbally tell her stories I would start by walking her through the
Genetic Algorithms provide a holistic search process based on principles of natural genetics and survivals of the fittest……
Let us now see the quality of individual the population over the time. As shown below at the starting point of the algorithm individuals are of less quality. However as the time goes by population’s individuals are getting of higher quality and reaching the pick of global and local optima. The image below illustrate these stages of the algorithm.
Living in a divided society based upon the religions of the Puritans and the Quakers, Evan Feversham sought out his own religious faith through his daily interactions with both religious groups.
On the other hand, artificial selection is the exact opposite of natural selection. Artificial selection occurs when humans manually modify or manipulate certain desirable trait(s) that will appear in the offspring (Artificial Selection). Charles Darwin formed this term when he did selective breeding of animals such as pigeons, cat...
The six key planning considerations are as follows: environmental considerations, mission assurance, Force protection, operations, communication synchronization, and facility requirements.
It is optimized by using evidence-based, thoughtful approaches associated with performance technology, quality control, communications, organization / employee development, project management, business processes, human resources, instructional design, change management and strategic planning and many more.
...imal solution. Then, a simulation method is developed to compare the centralized and decentralized SCs. In addition, to reach the near optimal solution in the centralized model, evolution strategy (ES) algorithm and imperialist competitive algorithm (ICA) as meta-heuristic approaches are applied.
Cormen T. H, Leiserson C. E., Rivest R. L. and Stein C. [1990] (2001). “Introduction to Algorithms”, 2nd edition, MIT Press and McGraw-Hill, ISBN 0-262-03293-7, pp. 27–37. Section 2.3: Designing algorithms..
The development of linear programming has been ranked among the most important scientific advances of the mid 20th century. Its impact since the 1950’s has been extraordinary. Today it is a standard tool used by some companies (around 56%) of even moderate size. Linear programming uses a mathematical model to describe the problem of concern. Linear programming involves the planning of activities to obtain an optimal result, i.e., a result that reaches the specified goal best (according to the mathematical model) among all feasible alternatives.
Linear programming is an supple and adaptive technique which can be analyses in a diversity of multi-dimensional problems effectively. This in turn will improve the mini factory’s production effectively.
The range of task environments that can be characterized by well - defined problems is vast. We can distinguish between so - called, toy problems, which are intended to illustrate or exercise various problem - solving methods, and so - called real - world problems, which tend to be more difficult and whose solutions people actually care about. In this section, we will give examples of both. By nature, toy problems can be given a concise, exact descri ption. This means that they can be easily used by different researchers to compare the performance of algorithms. Real - world problems, on the other hand, tend not to have a single agreed - upon description, but we will attempt to give the general flavor of t heir formulations.
Natural selection is based on the concept “survival of the fittest” where the most favourable individual best suited in the environment survive and pass on their genes for the next generation. Those individual who are less suited to the environment will die.
To be able to stop or at least decrease damages in the environment, Environmental management should be evident in the world. There are several different approaches to environmental management. For example, Eco profit. This approach focuses on protecting the environment through a systematic way, furthermore this approach is used all over the world and is one of the most basic approaches to environmental management. The next approach is human ecology approach. This type of approach studies the relationship of humans and nature. It tackles how humans can help the environment be better, by not harming nature. In most environmental management plans, it includes a certain model, called the Plan, Do, and Check. The first step is "Plan". In this step, companies