An efficient cuckoo-inspired meta-heuristic algorithm for multiobjective short-term hydrothermal scheduling

This paper proposes an efficient Cuckoo-Inspired Meta-Heuristic Algorithm (CIMHA) for solving multi-objective short-term hydrothermal scheduling (ST-HTS) problem. The objective is to simultaneously minimize the total cost and emission of thermal units while all constraints such as power balance, water discharge, and generation limitations must be satisfied. The proposed CIMHA is a newly developed meta-heuristic algorithm inspired by the intelligent reproduction strategy of the cuckoo bird. It is efficient for solving optimization problems with complicated objective and constraints because the method has few control parameters. The proposed method has been tested on different systems with various numbers of objective functions, and the obtained results have been compared to those from other methods available in the literature. The result comparisons have indicated that the proposed method is more efficient than many other methods for the test systems in terms of total cost, total emission, and computational time. Therefore, the proposed CIMHA can be a favorable method for solving the multi-objective ST-HTS problems

Unified Power Flow Controller: Modeling And Dynamic Characteristic

Abstract: -Unified power flow controller (UPFC) consists two converters. There are three purposes of this paper, firstly to illustrate the UPFC device based VSC designs, then to describe a decoupling method the UPFC's controller into two separate control systems of the shunt and the series converters respectively in realizing an appropriate co-ordination between them. Finally, using the Matlab tool to build a discrete simulator for the UPFC with 12 pulse converters. The simulation results show that the developed UPFC model is reflected the static and dynamic characteristics of the UPFC. The harmonics of the output of the model are analyzed. Using the simple power system with UPFC as an example, the dynamics characteristics are studied. The fault status of the system with UPFC is analyzed too

An adaptive fuzzy logic controller for robot-manipulator

In this paper, an adaptive fuzzy controller is designed for the robot-manipulator. The synthesized controller ensures that 1) the close-loop system is globally stable and 2) the tracking error converges to zero asymptotically and a cost function is minimized. The fuzzy controller is synthesized from a collection of IF-THEN rules. The parameters of the membership functions characterizing the linguistic terms change according to some adaptive law for the purpose of controlling a plant to track a reference trajectory. The proposed control scheme is demonstrated in a typical nonlinear plant two link manipulator. The computer simulation of control is done by the language MATLAB. The results of simulation show that the adaptive controller well operates and provides good qualities of the control system. The presented results are analyzed

An adaptive fuzzy logic controller for robot-manipulator

In this paper, an adaptive fuzzy controller is designed for the robot-manipulator. The synthesized controller ensures that 1) the close-loop system is globally stable and 2) the tracking error converges to zero asymptotically and a cost function is minimized. The fuzzy controller is synthesized from a collection of IF-THEN rules. The parameters of the membership functions characterizing the linguistic terms change according to some adaptive law for the purpose of controlling a plant to track a reference trajectory. The proposed control scheme is demonstrated in a typical nonlinear plant two link manipulator. The computer simulation of control is done by the language MATLAB. The results of simulation show that the adaptipresented results are analyzed

A Study of Voltage Stability Enhancement of A System with an Isolated Hybrid Diesel and Wind Generators on Phu Qui Island

This paper focuses on voltage stability improvement of a system with an isolated hybrid diesel and wind generators with increased wind power penetration in order to reduce the number of existing diesel generators. The system is located on Phu Qui Island, Binh Thuan province, Vietnam, and consists of 6 x 0.5-MW diesel synchronous generators (SG) and 3 x 2-MW wind turbine-based doubly fed induction generators (DFIG) interconnected to the local 22-kV isolated grid. Simulation results are performed to test the stability of the voltage system with different wind energy penetration levels and a static VAR compensator (SVC). It can be concluded that the voltage of the studied system can remain stable with wind energy penetration of 77%

Multiple-objective optimization applied in extracting multiple-choice tests

Student evaluation is an essential part of education and is usually done through examinations. These examinations generally use tests consisting of several questions as crucial factors to determine the quality of the students. Test-making can be thought of as a multi-constraint optimization problem. However, the test-making process that is done by either manually or randomly picking questions from question banks still consumes much time and effort. Besides, the quality of the tests generated is usually not good enough. The tests may not entirely satisfy the given multiple constraints such as required test durations, number of questions, and question difficulties. In this paper, we propose parallel strategies, in which parallel migration is based on Pareto optimums, and applyan improved genetic algorithm called a genetic algorithm combined with simulated annealing, GASA, which improves diversity and accuracy of the individuals by encoding schemes and a new mutation operator of GA to handle the multiple objectives while generating multiple choice-tests from a large question bank. The proposed algorithms can use the ability to exploit historical information structure in the discovered tests, and use this to construct desired tests later. Experimental results show that the proposed approaches are efficient and effective in generating valuable tests that satisfy specified requirements. In addition, the results, when compared with those from traditional genetic algorithms, are improved in several criteria including execution time, search speed, accuracy, solution diversity, and algorithm stability.Web of Science105art. no. 10443

Multi-swarm optimization for extracting multiple-choice tests from question banks

In this study, a novel method for generating multiple-choice tests is presented, which extracts the required number of tests of the same levels of difficulty in a single attempt and approximates the difficulty level requirement given by users. We propose an approach using parallelism and Pareto optimization for multi-swarm migration in a particle swarm optimization (PSO) algorithm. Multi-PSO is proposed for shortening the computing time. The proposed migration of PSOs increases the diversity of tests and controls the overlap of extracted tests. The experimental results show that the proposed method can generate many tests from question banks satisfying predefined levels of difficulty. Additionally, the developed method is shown to be effective in terms of many criteria when compared with other methods such as manually extracted tests, a simulated annealing algorithm (SA), random methods and PSO-based approaches in terms of the number of successful solutions, accuracy, standard deviation, search speed, and the number of questions overlapping between the exam questions, as well as for changing the search space, changing the number of individuals, changing the number of swarms, and changing the difficulty requirements.Web of Science9321483213