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In this paper, we first propose a Quality of Experience (QoE) evaluation model for dynamic adaptive streaming over HTTP (DASH) services. The proposed model predicts the perceived quality of user based on segment media quality, playback continuity and perceptual quality fluctuations caused by bitrate switching. Large quantities of subjective mean-opinion-score (MOS) tests demonstrate that our QoE evaluation...
In this work a new improved version of Teatching Learnning Based Optimization algorithm, TLBO, is proposed. The new strategy is obtained by tne inclusion of the Bat Algorithm, BA, random local search part in the optimization process with TLBO algorithm. The developped hybrid algorihm is applied jointly with 2D non-linear finite elment method to solv the Team workshop problem 25.
Mean-variance mapping optimization (MVMO) is an emerging metaheuristic optimization algorithm, whose evolutionary mechanism performs within a normalized search space. The most remarkable aspect of this mechanism resides in the application of a special mapping function to generate new values of the optimization variables based on their statistical significance throughout the search process. This paper...
With the development of web service technology, identifying and discovering the users with similar preferences have an important significance to service selection and service optimization in the service environment. In order to divide the users into groups based on their preference similarity in the process of service selection, a combination-based clustering algorithm, named AAK, is presented in...
The work offers a new method for analyzing and optimizing the topologie observability of cognitive maps of complex spatially distributed systems in terms of various approaches to setting the optimization problems. Such method is based on a new criterion of optimal level of its observability. The method has been elaborated on the basis of the well-known topologic observability analysis technique applicable...
We consider a general class of stochastic optimization problems, in which the state represents a certain level or amount which can be partly used and depleted, and subsequently filled by a random amount. This is motivated by energy harvesting applications, in which one manages the amount of energy in a battery, but is also related to inventory models and queuing models. We propose a simple policy...
Many methods have been introduced as economic load dispatchers. However, all these methods depend on the optimization field, where most of these studies mainly focus on how to strengthen the optimization algorithms themselves. This means the mystery key that segregate between the good and bad ELD solvers is the optimization algorithm itself. However, there is a practical fact known in many real power...
Spider Monkey Optimization is a well known meta-heuristic in the arena of nature inspired algorithms. It is basically known for its stagnation removal power in its original design. To balance the meta-heuristics mechanisms while preserving premature convergence, a new variant is developed which is named as Modified spider monkey optimization. In this paper, metropolis principle is used from simulated...
Gravitational search algorithm is a nature inspired optimization algorithm, inspired by newton's law of gravity and law of motion. In this paper, a new variant of Gravitational search algorithm is presented. The exploration and exploitation capability of GSA is balanced by splitting the whole swarm into two groups. The search process is modified so that one group better exploits and one group becomes...
The purpose of this article is to explain how a fuzzy linear programming model can be transformed into a fuzzy multi-objective linear programming model and then solved. An algorithm is developed to present our approach and fuzzy optimal solution is obtained. To demonstrate the efficiency and feasibility of the proposed approach, one numerical example has been solved.
This paper presents an assessment of different computational intelligences, i.e evolutionary algorithm (EP), firefly algorithm (FA) and cuckoo search algorithm (CSA) for solving single-objective optimization problem. Recently, these algorithms have been widely used and applied to solve different types of optimization problems. However, the performance of these optimization algorithms have not been...
In optimizing PID controller's parameters using any optimization algorithm, fitness function is one of the important area to be explored. Among frequently used fitness function is a weighted transient response index (WTRI) where the weight is given to each of the transient response component including rise time, settling time, overshoot and steady state error based on their priority set in specification...
The implementation of PSO algorithm in optimizing PID controller's parameters has been in active research area recently. One of the popular plant to be controlled using this control strategy is a DC motor and various types of DC motors with variations in parameters value have been used in this research area. Moment of inertia is one of the important parameters of a DC motor which can affect the output...
The basic interphase power controller (IPC) presents the interesting features of simplicity and low cost despite being less flexible than most FACTS controllers. A compromise performance can be achieved by replacing the phase shifting transformers (PSTs) with a reduced rating unified power flow control (UPFC), also known as Unified Interphase Power Controller (UIPC). This comparison is the main goal...
In nature, the DC-DC converter deals with high switching phenomenon that contributes to the higher conduction losses. Its operation conventionally associated with PI/PID controller in order to meet the desired output. To increase the DC-DC converter performance, an advanced controller has been proposed. However, it is deals with complex algorithm that made the controller cost higher. In this paper,...
This study considers offline identification of switched linear MIMO systems using measurements from their inputs and outputs. This is a class of non-convex optimization and ill-posed problems. To convert this optimization into a binary integer programming problem, the proposed approach assumes that the number of switches among the subsystems is upper-bounded. The state-space realization of each sub-system...
This paper proposes a new method to analyze driver behavior. Analysis of the behavior is done through the observation of the time-evolution of parameters of simple driver models. The behavior analysis is decomposed in two steps. First the driver model have to be selected or designed to represent the average behavior of a large sample of drivers. Then personal driver's behavior evolution can be analyzed...
Recent efforts in the evolutionary multi-objective optimization (EMO) community focus on addressing shortcomings of current solution techniques adopted for solving many-objective optimization problems (MaOPs). One such challenge faced by classical multi-objective evolutionary algorithms is diversity preservation in optimization problems with more than three objectives, namely MaOPs. In this vein,...
The paper deals with human-computer interaction in which the cooperation leads to solve a difficult issue of discrete optimization. Considered jobs scheduling problem in a robotic cell consisting of two machines and a robotic operator. Only one of two machines can work at a time and there are setup times between successive operations on a machine. The goal is to determine a schedule — permutation...
This study used the multi-objective evolution algorithm and conventional method to research the problem of grouping interview. Firstly, we analyzed this problem and build the mathematical model of that. Then, we used the conventional method to solve this problem. In addition, the multi-objective evolution with matrix-coding scheme was used to solve this problem. Experimental comparisons show that...
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