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In this paper, we introduce a novel active queue management scheme based on a Proportional-Integral-Derivative controller with ant colony optimization for the Internet system. The ACO algorithm searching technique is applied to search for the best gain parameters of the PID controller among a stability area. This stability region is determined using an extension of Hermite-Biehler theorem applied...
This paper describes a novel technique for creating the test program sets (TPSs) that are used by automatic test equipment (ATE) to test electronic circuits and devices. This paper presents an architecture consisting of a genetic algorithm (GA) test proposer and a pattern classifier test evaluator. This architecture has been shown to produce optimized test sequences without human intervention. In...
In this paper, a multi-agent load frequency balancing control algorithm based on Genetic Algorithm for a storage-less Photo Voltaic (PV) generation is proposed. For maximum deployment of available renewable energy, the PV generation is tracked at its maximum power point however through the use of a virtual synchronous generator converter control, the PV generator is able to follow the load demand...
In this paper, a novel approach for classification rule mining is presented. The remarkable relationship between the rule extraction procedure and the concept of multiobjective optimization is emphasized. The range values of features composing the rules are handled as decision variables in the modelled multiobjective optimization problem. The proposed method is applied to three well-known datasets...
We present a novel multigrid approach for the shape optimizations of corrugated metallic sheets by using genetic algorithms (GAs) and the multilevel fast multipole algorithm (MLFMA). The overall mechanism is obtained by an efficient integration of GAs and MLFMA, while the optimizations are improved by applying multiple grids at different layers. We show that the multigrid approach provides more effective...
A hybrid genetic algorithm (HGA) is proposed to optimize the rotor shape of an Interior permanent magnet machine which is used in EV application. The novel method obtain lower iron loss and torque ripple as well as higher average torque and efficiency for the machine. The optimization results of the HGA design is compared with the initial and genetic algorithm designs. It is shown that the performance...
A number of investigations were undertaken to enhance the behavior of high voltage outdoor insulators by adopting numerical methods of optimization, but no work is performed to account for the presence of pollution. In this paper, a shape optimization of a high voltage insulator is achieved with the objective of reducing the tangential electric field along its polluted surface by means of numerical...
In the paper, the Wind Driven Optimization WDO algorithm is applied to the optimal shape design of a class of switched reluctance motors. The goal of the optimization is to identify a class of geometries which maximize the torque and simultaneously minimize the iron losses of the motor. Because of the twofold design criterion, the multi-objective version of the wind driven algorithm M-WDO is used...
Amplitude, phase angle, active and reactive powers flowing in each busbar of a power system can be seen by performing a load flow analysis. From these data, it is possible to determine the voltage drop, the distribution of the forces, the loading of the equipment and the losses of the related power system. Then, Active power losses can be reduced by making improvements at the points where losses are...
The Evolutionary algorithm (EA) for researching parameters of nonlinear system is a rapidly growing field of identification. This can owe to the importance of EA for both the theoretical field and the engineering community. However, the identification of the nonlinear system is still a knotty problem, especially when heavy-tailed noises exists. Compared to classical identification methods, EA has...
The trend in the automotive industry is towards electric vehicles (EV), however, the industry will depend on gasoline engines for many years to come. There is also increased demand for the reduction of greenhouse gases. This work develops an adaptive model-based optimal control algorithm based on Sub-Structured Neural Network (SSANN), Multi-Objective Genetic Algorithms (GA), Multi-Objective Dragonfly...
Aiming at the problem of optimizing the structural of X-band standing wave accelerator with multi-parameter, a method for automatic cavities structure optimized and based on genetic algorithm was proposed in this paper. In this method accelerated structure model was built with multi-parameter, using MATLAB and electromagnetic stimulation software CST to work together to optimize multi-parameter structure,...
Numerous genetic algorithms with Pareto-ranking were proposed for solving multiobjective optimisations (MOOs). Mainly, these algorithms compute the fitness values of the solutions via dominance analysis. For few conflicting objectives, dominance analysis is suitable for managing the partial sorting; however, this technique is not capable to handle other common requirements of MOOs, such as preserving...
The increase of noise immunity of circuits with redundancy is considered. A new method for packaging and tracing of printed circuit boards (PCBs) with redundancy is described. The method is characterized by increased noise immunity due to modal filtration. Optimization of stack parameters of a multi-layer PCB for the implementation of the new method is performed by the genetic algorithm. For the formulation...
In this paper, an adaptive genetic algorithm based on multi-population elite selection strategy is proposed. The multi-population elite selection strategy is used to preserve the optimal individuals of each group. Finally, these optimal individuals formed a population, and then use the improved adaptive genetic algorithm to finish the solution. By comparing the simulation experiments of TSP problem...
We take inspirations from nature very often in solving many complex scientific and day to day problems. Nature inspired computing is a branch of computer engineering deals with the development of algorithms simulating behaviors of natural species for solving complex problems not easily solvable by available computational models. Based on biological systems, various algorithms have been presented in...
This paper proposes an efficient methodology to optimally determine the best location and optimum size for Distributed Generators (DG), in a distribution network, while minimizing energy losses and improving voltage profile. The proposed methodology has been designed to consider variable demand and variable DG production scenarios, offering a set of optimum values for DG active power output and power...
A RFID (Radio-Frequency Identification) system consists of RFID readers and tags. RFID technology uses electromagnetic fields to identify the location of the objects attached with the tag and track them continuously. The tags contain electronically stored information about the objects. The most essential requirement for setting up a RFIDenabled system is to deploy (locating and positioning) the RFID...
In Non cooperative Game Theory, Nash Equilibrium can be computed by finding the best response strategy for each player. However this problem cannot be solved deterministically in polynomial time. For some finite games, there might be more than one pure strategy Game Equilibrium. In such cases, the most optimal set of solutions give the Game Equilibria. Evolutionary Algorithms and specifically Genetic...
The crucial objective of this paper is to design a hybrid model of the genetic algorithm for fuzzy extreme learning machine classifier (GA-FELM), which selects an optimal feature subset by using the multilevel parameter optimization technique. Feature subset selection is an important task in pattern classification and knowledge discovery problems. The generalization performance of the system is not...
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