Combination of Intermittent Search Strategy and an Improve Particle Swarm Optimization algorithm (IPSO) for damage detection of steel frame

Modality and intermittent search strategy in combination with an Improve Particle Swarm Optimization algorithm (IPSO) to detect damage structure via using vibration analysis basic principle of a decline stiffness matrix a structure is presented in the study as a new technique. Unlike an optimization problem using a simplistic algorithm application, the combination leads to promising results. Interestingly, the PSO algorithm solves the optimal problem around the location determined previously. In contrast, Eagle Strategy (ES) is the charging of locating the position in intermittent space for the PSO algorithm to search locally. ES is easy to deal with its problem via drastic support of Levy flight. As known, the PSO algorithm has a fast search speed, yet the accuracy of the PSO algorithm is not as good as expected in many problems. Meanwhile, the combination is powerful to solve two problems: 1) avoiding local optimization, and 2) obtaining more accurate results. The paper compares the results obtained from the PSO algorithm with the combination of IPSO and ES for some problems and between experiment and FEM to demonstrate its effectiveness. Natural frequencies are used in the objective function to solve this optimization problem. The results show that the combination of IPSO and ES is quite effective.

Parameter Estimation of Static/Dynamic Photovoltaic Models Using a Developed Version of Eagle Strategy Gradient-Based Optimizer

The global trend towards renewable energy sources, especially solar energy, has had a significant impact on the development of scientific research to manufacture high-performance solar cells. The issue of creating a model that simulates a solar module and extracting its parameter is essential in designing an improved and high performance photovoltaic system. However, the nonlinear nature of the photovoltaic cell increases the challenge in creating this model. The application of optimization algorithms to solve this issue is increased and developed rapidly. In this paper, a developed version of eagle strategy GBO with chaotic (ESCGBO) is proposed to enhance the original GBO performance and its search efficiency in solving difficult optimization problems such as this. In the literature, different PV models are presented, including static and dynamic PV models. Firstly, in order to evaluate the effectiveness of the proposed ESCGBO algorithm, it is executed on the 23 benchmark functions and the obtained results using the proposed algorithm are compared with that obtained using three well-known algorithms, including the original GBO algorithm, the equilibrium optimizer (EO) algorithm, and wild horse optimizer (WHO) algorithm. Furthermore, both of original GBO and developed ESCGBO are applied to estimate the parameters of single and double diode as static models, and integral and fractional models as examples for dynamic models. The results in all applications are evaluated and compared with different recent algorithms. The results analysis confirmed the efficiency, accuracy, and robustness of the proposed algorithm compared with the original one or the recent optimization algorithms.

Micro Grid Operation Cost Reduction Using Particle Swarm Optimizer and Eagle Strategy

As a result of rapid financial development and natural disasters, energy efficiency research, and high-quality electricity alternative energy options, as well as efficient electricity sources. In particular, the use of green energy sources has become a hot issue; As a result, distributed electricity supply in the micro grid is the basis for the achievement of the vital objectives of successfully providing the customer with currency and stability. The article proposes a hybrid metaheuristic approach based on the Eagle strategy Technique (ES) and Particular Swarm Optimizing (PSO) Technology, which will minimize low-voltage running costs from a renewable energy source such as an electricity generator, solar panels, wind generators, micro turbines and fuel cells. The cost optimization problem is set up as a nonlinearly constrained problem. In order to maximize distributed generation, a mathematical problem must be solved. The proposed hybrid solution is evaluated on low-voltage micro grids, and its optimal performance is compared to that of other hybrid approaches and variety of other metaheuristic techniques

Analysis of Hybrid Technique for Motion Planning of Humanoid NAO

The navigation of a humanoid robot is essential because it is the basic requirement of any assigned task. Singly used motion planning techniques may take a long path to reach the target and increase the computational cost. Therefore, in this article, a hybrid controller is employed in the humanoid NAO for motion planning assignment. The Eagle strategy (ES) with Ant colony optimization (ACO) is introduced in this article for evaluating precise steering angles for humanoid robots as they traverse a route from a reference point to a target point. This enables the robot to achieve its specific target more quickly by avoiding barriers and obtaining the minimal global direction. The hybridized ES-ACO approach is critical in determining precise steering angles to escape obstacles. The details of terrain are obtained using vision and ultrasonic sensors, which also include the barriers ranges to the ES as an input variable. The ES's input parameters are the barrier ranges from the NAO in front, left, and right directions, and the technique's output variable is the precise steering angle. The designed controller is tested in both a simulation and an experimental setting with a variety of obstacles. The outcomes of both simulation and experimental conditions are compared, and a strong correlation is identified in those with the fewest deviations.

Eagle Strategy with Cauchy Mutation Particle Swarm Optimization for Energy Management in Cloud Computing

Energy management of the cloud datacentre is a challenging task, especially when the cloud server receives a number of the user’s request simultaneously. This requires an efficient method to optimally allocate the resources to the users. Resource allocation in cloud data centers need to be done in optimized manner for conserving energy keeping in view of Service Level Agreement (SLA). We propose, Eagle Strategy (ES) based Modified Particle Swarm Optimization (ES-MPSO) to minimize the energy consumption and SLA violation. The Eagle Strategy method is applied due to its efficient local optimization technique. The Cauchy Mutation method which schedules the task effectively and minimize energy consumption, is applied to the proposed ES-MPSO method for improving the convergence performance. The simulation result shows that the energy consumption of ES-MPSO is 42J and Particle Swarm Optimization (PSO) is 51J. The proposed method ES-MPSO achieves higher efficiency compared to the PSO method in terms of energy management and SLA.