Crow Search Algorithm (CSA) can be defined as one of the new swarm intelligence algorithms that has been developed lately, simulating the behavior of a crow in a storage place and the retrieval of the additional food when required. In the theory of the optimization, a crow represents a searcher, the surrounding environment represents the search space, and the random storage of food location represents a feasible solution. Amongst all the food locations, the one where the maximum amount of the food is stored is considered as the global optimum solution, and objective function represents the food amount. Through the simulation of crows’ intelligent behavior, the CSA attempts to find the optimum solutions to a variety of the problems that are related to the optimization. This study presents a new adaptive distributed algorithm of routing on CSA. Because the search space may be modified according to the size and kind of the network, the algorithm can be easily customized to the issue space. In contrast to population-based algorithms that have a broad and time-consuming search space. For ten networks of various sizes, the technique was used to solve the shortest path issue. And its capability for solving the problem of the routing in the switched networks is examined: detecting the shortest path in the process of a data packet transfer amongst the networks. The suggested method was compared with four common metaheuristic algorithms (which are: ACO, AHA, PSO and GA) on 10 datasets (integer, weighted, and not negative graphs) with a variety of the node sizes (10 - 297 nodes). The results have proven that the efficiency of the suggested methods is promising as well as competing with other approaches.
Electrical distribution system loads are permanently not fixed and alter in value and nature with time. Therefore, accurate consumer load data and models are required for performing system planning, system operation, and analysis studies. Moreover, realistic consumer load data are vital for load management, services, and billing purposes. In this work, a realistic aggregate electric load model is developed and proposed for a sample operative substation in Baghdad distribution network. The model involves aggregation of hundreds of thousands of individual components devices such as motors, appliances, and lighting fixtures. Sana’a substation in Al-kadhimiya area supplies mainly residential grade loads. Measurement-based
... Show MoreThis paper presents a new design of a nonlinear multi-input multi-output PID neural controller of the active brake steering force and the active front steering angle for a 2-DOF vehicle model based on modified Elman recurrent neural. The goal of this work is to achieve the stability and to improve the vehicle dynamic’s performance through achieving the desired yaw rate and reducing the lateral velocity of the vehicle in a minimum time period for preventing the vehicle from slipping out the road curvature by using two active control actions: the front steering angle and the brake steering force. Bacterial forging optimization algorithm is used to adjust the parameters weights of the proposed controller. Simulation resul
... Show MoreThe drones have become the focus of researchers’ attention because they enter into many details of life. The Tri-copter was chosen because it combines the advantages of the quadcopter in stability and manoeuvrability quickly. In this paper, the nonlinear Tri-copter model is entirely derived and applied three controllers; Proportional-Integral-Derivative (PID), Fractional Order PID (FOPID), and Nonlinear PID (NLPID). The tuning process for the controllers’ parameters had been tuned by using the Grey Wolf Optimization (GWO) algorithm. Then the results obtained had been compared. Where the improvement rate for the Tri-copter model of the nonlinear controller (NLPID) if compared with
A simulation study is used to examine the robustness of some estimators on a multiple linear regression model with problems of multicollinearity and non-normal errors, the Ordinary least Squares (LS) ,Ridge Regression, Ridge Least Absolute Value (RLAV), Weighted Ridge (WRID), MM and a robust ridge regression estimator MM estimator, which denoted as RMM this is the modification of the Ridge regression by incorporating robust MM estimator . finialy, we show that RMM is the best among the other estimators
The maritime borders of the coastal state are the scene of the human tragedies that we are witnessing in the context of the perilous plight of refugees at sea، and the failure to help people at risk by the coastal state will entail international responsibility towards them، and the three international maritime conventions، commitments aimed at coastal states with the duty to provide assistance and rescue to persons in danger of being lost at sea، is without doubt one of the most basic principles established in the law of the sea، in addition to establishing and operating And maintenance of the search and rescue service، and not to expel them and return them to their country of origin or another country.
This paper proposes a new structure of the hybrid neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Weight parameters of the hybrid neural structure with its serial-parallel configuration are adapted by using the Back propagation learning algorithm. The ability of the proposed hybrid neural structure for nonlinear system has achieved a fast learning with minimum number
... Show MoreWireless Sensor Networks (WSNs) are composed of a collection of rechargeable sensor nodes. Typically, sensor nodes collect and deliver the necessary data in response to a user’s specific request in many application areas such as health, military and domestic purposes. Applying routing protocols for sensor nodes can prolong the lifetime of the network. Power Efficient GAthering in Sensor Information System (PEGASIS) protocol is developed as a chain based protocol that uses a greedy algorithm in selecting one of the nodes as a head node to transmit the data to the base station. The proposed scheme Multi-cluster Power Efficient GAthering in Sensor Information System (MPEGASIS) is developed based on PEGASIS routing protocol in WSN. The aim
... Show MoreIn this paper, a FPGA model of intelligent traffic light system with power saving was built. The intelligent traffic light system consists of sensors placed on the side's ends of the intersection to sense the presence or absence of vehicles. This system reduces the waiting time when the traffic light is red, through the transition from traffic light state to the other state, when the first state spends a lot of time, because there are no more vehicles. The proposed system is built using VHDL, simulated using Xilinx ISE 9.2i package, and implemented using Spartan-3A XC3S700A FPGA kit. Implementation and Simulation behavioral model results show that the proposed intelligent traffic light system model satisfies the specified operational req
... Show MoreAutomated clinical decision support system (CDSS) acts as new paradigm in medical services today. CDSSs are utilized to increment specialists (doctors) in their perplexing decision-making. Along these lines, a reasonable decision support system is built up dependent on doctors' knowledge and data mining derivation framework so as to help with the interest the board in the medical care gracefully to control the Corona Virus Disease (COVID-19) virus pandemic and, generally, to determine the class of infection and to provide a suitable protocol treatment depending on the symptoms of patient. Firstly, it needs to determine the three early symptoms of COVID-19 pandemic criteria (fever, tiredness, dry cough and breat
... Show MoreIncreased downscaling of CMOS circuits with respect to feature size and threshold voltage has a result of dramatically increasing in leakage current. So, leakage power reduction is an important design issue for active and standby modes as long as the technology scaling increased. In this paper, a simultaneous active and standby energy optimization methodology is proposed for 22 nm sub-threshold CMOS circuits. In the first phase, we investigate the dual threshold voltage design for active energy per cycle minimization. A slack based genetic algorithm is proposed to find the optimal reverse body bias assignment to set of noncritical paths gates to ensure low active energy per cycle with the maximum allowable frequency at the optimal supply vo
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