Called Crow Search Algorithm Research Articles

Security-constrained optimal placement of PMUs using Crow Search Algorithm

For precise power system monitoring, a major focus is on involvement of the latest technology based on phasor measurement units (PMUs). As the sole system monitor, state estimator plays an important role in the security of power system operations. Optimal placement of PMUs (OPP) with numerical observability ensures reliable state estimation. For economical and efficient utilization, there is a need to optimize the placement of PMUs in the power system network. A new approach called Crow Search Algorithm (CSA) devised by others, has been used to solve an OPP problem. The performance of this new approach is compared to the dominant method for an optimization problem — binary integer linear programming (BILP). Comparison studies have also been carried out with particle swarm optimization (PSO) method. The major constraints such as topological, numerical observability conditions with and without zero-injection buses (ZIBs) are considered. Contingencies and limitation of measurement channels in a PMU device are also incorporated as constraints. The main advantage of using the CSA is that it provides multiple location sets for same optimal number of PMUs (optimal number same as obtained by BILP). While BILP method provides only one set of locations for the optimal number of PMUs obtained. This becomes advantageous in planning stage for power engineers for placing PMUs. Test systems considered for the case studies are of varied sizes such as IEEE 14-bus, IEEE 30-bus, IEEE 57-bus and 72-bus practical equivalent system of Indian southern region power grid.

An Efficient Metaheuristic Technique to Control the Maximum Power Point of a Partially Shaded Photovoltaic System Using Crow Search Algorithm (CSA)

The field of research in maximum power point tracking (MPPT) methods is experiencing great progress with a wide range of techniques being suggested, ranging from simple but ineffective methods to more effective but complex ones. Therefore, it is very important to propose a strategy that is both simple and effective in controlling the global maximum power point (GMPP) for a photovoltaic (PV) system under changing weather conditions, especially in partial shading cases (PSCs). This paper proposes a new design of an MPPT controller based on a metaheuristic optimization technique called Crow Search Algorithm (CSA) to attenuate the undesirable effects of partial shading on the tracking performances of standalone PV systems. CSA is a nature-inspired method based on the intelligent skills of the crow in the search process of hidden food places. CSA technique combines efficiency and simplicity using only two tuning parameters. The stability analysis of the proposed system is performed using a Lyapunov function. The simulation results for three different partial shading cases that are zero, weak and severe shading confirm the superior performance of CSA compared to PSO and P&O techniques in term of easy implementation, high efficiency and low power loss, decreasing considerably the convergence time by an average of 38.53%.

RETRACTED ARTICLE: Preserving mobile commerce IoT data using light weight SIMON block cipher cryptographic paradigm

Internet of Things (IoT) data security is one of the critical ideas in data security as the data to be transferred should be made secure. In the field of mobile commerce is the high effect that it will have on a few parts of the regular life and behavior of potential clients. In this present examination, we focused to build up the IoT security in the field of mobile commerce utilizing new cryptographic strategies. Fundamentally the sensitive data are classified from the entire dataset to enhance the accuracy utilizing Feed Forward Back Propagation Algorithm (FFBN). At that point the security of sensitive data is upgraded by utilizing Light Weight Cryptography (LWC) which encodes the input sensitive data called encryption. To enhance the data privacy and secrecy, an imaginative security model is proposed for example Lightweight SIMON block cipher. This will encrypt the data alongside optimal key selection; this LWC upgrades the m commerce data security level in cloud. For key optimization, a meta-heuristic algorithm called Crow Search Algorithm (CSA) is introduced. The proposed SIMON-CSA accomplishes least time to generate key value to decrypt the data.

A Binary Crow Search Algorithm for Solving Two-dimensional Bin Packing Problem with Fixed Orientation

This work aims to propose a binary version of a new metaheuristic in order to solve the two-dimensional bin packing problem (2D-BPP) which is a NP-hard optimization problem in a strong sense. Various metaheuristics are dedicated to solve 2D-BPP, however, the swarm intelligence algorithms are rarely frequent in this problem. A new algorithm based on the principles of swarm intelligence was recently introduced, called crow search algorithm (CSA), which has been successfully applied to continuous optimization problems. In this paper, the CSA is binarized by applying a sigmoid transformation so as to adapt it for solving the 2D-BPP. An evaluation of the performance of the proposed binary CSA is carried out by standard instances of 2D-BPP, and the experimental results are compared with two state-of-the-art algorithms that are particle swarm optimization algorithm and the well-known heuristic Finite First Fit. The results obtained by the proposed algorithm prove its effectiveness in terms of solution quality and computational time. Until recently, no study exists that deals with CSA algorithm for solving the packing problems in general and 2D-BPP in particular.

A crow search algorithm for aircraft maintenance check problem and continuous airworthiness maintenance program

This research discusses the maintenance problem of a small commer­cial aircraft with propeller engine, typed ATR-72. Based on the main­­tenance records, the aircraft has average 294 routine activities that have to be monitored and done based on determined threshold interval. This research focuses on developing a meta­heuristic model to optimize the aircraft’s utility, called Crow Search Algorithm (CSA) to solve the Aircraft Maintenance Problem (AMP). The algorithm is developed and tested whether a younger meta­heuristic method, CSA, is able to give better performance compar­ed to the older methods, Particle Swarm Optimization (PSO) and other hybri­dized method PSO with Greedy Randomized Adaptive Search Optimization (PSO-GRASP). Several experiments are performed by using parameters: 1000 maximum iteration and 600 maximum computa­tion time by using four dataset combinations. The results show that CSA can give better performance than PSO but worse than PSO-GRASP.

Automatic generation control of a solar thermal and dish‐stirling solar thermal system integrated multi‐area system incorporating accurate HVDC link model using crow search algorithm optimised FOPI Minus FODF controller

This article demonstrates the automatic generation control of a multi-area system incorporating various sources. Area-1 and area-2 consist of thermal and parabolic trough solar thermal plant (PTSTP) of fixed and random solar insolation, respectively, and area-3 comprises of thermal and realistic dish-stirling solar thermal system units. A maiden effort has been made to use a fractional order (FO) proportional-integral minus FO derivative with filter coefficient (FOPI-FODF) controller as a secondary controller. The controller gains are optimised using a novel algorithm called crow search algorithm. Comparisons of system dynamic response with and without renewable energy sources for FOPI-FODF, and for some commonly used integer order and FO controller's reveals the better performance of FOPI-FODF controller. A study comparing both fixed and random solar insolation in PTSTP of different areas have been tested. An accurate high-voltage direct current (AHVDC) line model is designed using inertia emulation strategy and is placed instead of AC tie line, and with parallel AC tie lines resulting in better system dynamic performances than AC tie line alone. The selection of the optimum location of AHVDC line in parallel AC tie line exposes the AHVDC line connected with the area where the system is disturbed.

Optimal design of IIR filters based on least ℓp‐norm using a novel meta‐heuristic algorithm

AbstractIn this work, an optimal infinite impulse response (IIR) filter is designed using least Pth norm and new meta‐heuristic optimization algorithm called Crow Search Algorithm (CSA). The two approaches try to get a resolution to the specified problem but use unlike approaches to perform so. Furthermore, the ℓp‐norm is used to design optimum IIR filters. The main motivation of using CSA is its user‐friendly optimization tool for both beginner and professional users. This Algorithm provides a good stability between variety and strength which are mainly controlled by the parameter of awareness probability. Some examples of IIR filter design cases are treated, and a comparison is done. It is revealed that the design objective is successfully attained, and the resultant filter parameters are better using the ℓp‐norm combined with the proposed CSA meta‐heuristic optimization algorithms compared with IIR filter designed using butter synthesis approach in terms of accuracy and robustness. Classical approaches are largely outperformed by CSA for the same filter order.

Crow Search based Multi-objective Optimization of Irreversible Air Refrigerators

This study proposes the optimum performance of the irreversible air refrigerators through recently developed metaheuristic algorithm called Crow search algorithm by means of finite time thermodynamics. Finite time thermodynamics is based on choosing the optimum pathways for any kind of thermodynamic system in order to reach the maximum efficiency of the thermodynamic cycle. Handful of objectives for assessing the performance of the irreversible air refrigerators such as coefficient of performance (COP), exergetic efficiency (η II ) , ecological coefficient of performance (ECOP), thermoeconomic optimization (F), and thermoecologic optimization functions (ECF) have been successfully applied on the system. Three optimization scenarios have been studied for the multi objective optimization of irreversible air refrigerators. First scenario evaluates the concurrent optimization of objectives including exergetic efficiency (η II ), coefficient of performance (COP), and ecological coefficient of performance (ECOP). In second scenario, coefficient of performance (COP), thermoeconomic parameter (F), and thermoecological coefficient of performance (ECOP) have been simultaneously maximized to retain optimum working point of the cycle. Third case studies the simultaneous optimization of the imposed objectives such as second law efficiency (η II ), coefficient of performance (COP), and thermoecological function (ECF). Widely known decision-making theorems of LINMAP, TOPSIS, and Shannon’s entropy theorem have been applied on the Pareto curve constructed by the non-dominated solutions to decide the most favorable solution on the frontier.

A meta-heuristic method for solving scheduling problem: crow search algorithm

Scheduling is one of the most important processes in an industry both in manufacturingand services. The scheduling process is the process of selecting resources to perform an operation on tasks. Resources can be machines, peoples, tasks, jobs or operations.. The selection of optimum sequence of jobs from a permutation is an essential issue in every research in scheduling problem. Optimum sequence becomes optimum solution to resolve scheduling problem. Scheduling problem becomes NP-hard problem since the number of job in the sequence is more than normal number can be processed by exact algorithm. In order to obtain optimum results, it needs a method with capability to solve complex scheduling problems in an acceptable time. Meta-heuristic is a method usually used to solve scheduling problem. The recently published method called Crow Search Algorithm (CSA) is adopted in this research to solve scheduling problem. CSA is an evolutionary meta-heuristic method which is based on the behavior in flocks of crow. The calculation result of CSA for solving scheduling problem is compared with other algorithms. From the comparison, it is found that CSA has better performance in term of optimum solution and time calculation than other algorithms.

Optimal allocation of capacitor devices on MV distribution networks using crow search algorithm

This study aims at enhancing the performance of medium-voltage distribution systems. Improving the performance of distribution networks is employed through optimally reducing the energy loss, minimising of the loading level for the transformer substation, and economical penetration of shunt capacitors. Moreover, the impacts of changing the switching tie-line in the distribution network on the optimal allocation of the capacitor devices are discussed. A novel recent powerful swarm intelligence optimiser called crow search algorithm (CSA) is developed for optimal allocations of capacitors into electrical distribution networks. Achieving the technical and economical objective functions is subjected to various operational limits. The proposed procedure is applied on East Delta Network (EDN) as a real distribution network in the Unified Egyptian Network. Significant technical and economical merits are satisfied which prove the capability of the proposed allocation procedure.