Acoustic scattering reduction in elastic materials with Bat optimization algorithm

2021 ◽  
Vol 15 (1) ◽  
pp. 7907-7917
Author(s):  
Zeyad Khalaf Algburi ◽  
Cihan Karataş
Keyword(s):  
Optimization Algorithm ◽  
Poisson Ratio ◽  
Acoustic Scattering ◽  
Bat Algorithm ◽  
Algorithm Optimization ◽  
Elastic Materials ◽  
Rigid Cylinder ◽  
Scattering Effect ◽  
Directivity Patterns

A scattering-cancelation method is followed in this analysis. The opportunity to reduce the scattering effect in rigid cylinder with elastic cloaking material is investigated in the paper. The rigid cylinder with elastic cloaking and without elastic cloaking is analyzed for regular planes waves to test the scattering effect of the rigid cylinder. The energy due to reflected and travelled waves is presented mathematically. The parameter of the elastic cloaking such as thickness, density and Poisson ratio are optimized using BAT algorithm. Pressure filed of the rigid cylinder and directivity patterns scatter pressures in the cylinder are analyzed. The results revealed that, bat algorithm optimization provide the better results for elastic cloaking in the cylinder to reduce scattering effect.

scholarly journals Binary Spring Search Algorithm for Solving Various Optimization Problems

2021 ◽  
Vol 11 (3) ◽  
pp. 1286 ◽  
Author(s):  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Ali Dehghani ◽  
Om P. Malik ◽  
Ruben Morales-Menendez ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
High Performance ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Bat Algorithm ◽  
Search Space ◽  
Binary Particle Swarm Optimization ◽  
Dragonfly Algorithm ◽  
Grasshopper Optimization Algorithm

One of the most powerful tools for solving optimization problems is optimization algorithms (inspired by nature) based on populations. These algorithms provide a solution to a problem by randomly searching in the search space. The design’s central idea is derived from various natural phenomena, the behavior and living conditions of living organisms, laws of physics, etc. A new population-based optimization algorithm called the Binary Spring Search Algorithm (BSSA) is introduced to solve optimization problems. BSSA is an algorithm based on a simulation of the famous Hooke’s law (physics) for the traditional weights and springs system. In this proposal, the population comprises weights that are connected by unique springs. The mathematical modeling of the proposed algorithm is presented to be used to achieve solutions to optimization problems. The results were thoroughly validated in different unimodal and multimodal functions; additionally, the BSSA was compared with high-performance algorithms: binary grasshopper optimization algorithm, binary dragonfly algorithm, binary bat algorithm, binary gravitational search algorithm, binary particle swarm optimization, and binary genetic algorithm. The results show the superiority of the BSSA. The results of the Friedman test corroborate that the BSSA is more competitive.

scholarly journals A bat optimization algorithm with moderate orientation and perturbation of trend

2021 ◽  
Vol 15 ◽  
pp. 174830262110084
Author(s):  
Jingsen Liu ◽  
Hongyuan Ji ◽  
Qingqing Liu ◽  
Yu Li
Keyword(s):  
Local Search ◽  
Optimization Algorithm ◽  
Random Perturbation ◽  
Bat Algorithm ◽  
Local Extremum ◽  
Global Search ◽  
Deep Mining ◽  
Nonlinear Variation ◽  
Optimal Value ◽  
Search Stage

In order to improve the convergence speed and optimization accuracy of the bat algorithm, a bat optimization algorithm with moderate optimal orientation and random perturbation of trend is proposed. The algorithm introduces the nonlinear variation factor into the velocity update formula of the global search stage to maintain a high diversity of bat populations, thereby enhanced the global exploration ability of the algorithm. At the same time, in the local search stage, the position update equation is changed, and a strategy that towards optimal value modestly is used to improve the ability of the algorithm to local search for deep mining. Finally, the adaptive decreasing random perturbation is performed on each bat individual that have been updated in position at each generation, which can improve the ability of the algorithm to jump out of the local extremum, and to balance the early global search extensiveness and the later local search accuracy. The simulating results show that the improved algorithm has a faster optimization speed and higher optimization accuracy.

Moth Flame Optimization Algorithm Range-Based for Node Localization Challenge in Decentralized Wireless Sensor Network

2019 ◽  
Vol 10 (1) ◽  
pp. 82-109 ◽  
Author(s):  
Mihoubi Miloud ◽  
Rahmoun Abdellatif ◽  
Pascal Lorenz
Keyword(s):  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Dimensional Space ◽  
Fitness Function ◽  
Low Cost ◽  
Bat Algorithm ◽  
Wireless Sensor ◽  
Flower Pollination Algorithm ◽  
Node Localization ◽  
Moth Flame Optimization Algorithm

Recently developments in wireless sensor networks (WSNs) have raised numerous challenges, node localization is one of these issues. The main goal in of node localization is to find accurate position of sensors with low cost. Moreover, very few works in the literature addressed this issue. Recent approaches for localization issues rely on swarm intelligence techniques for optimization in a multi-dimensional space. In this article, we propose an algorithm for node localization, namely Moth Flame Optimization Algorithm (MFOA). Nodes are located using Euclidean distance, thus set as a fitness function in the optimization algorithm. Deploying this algorithm on a large WSN with hundreds of sensors shows pretty good performance in terms of node localization. Computer simulations show that MFOA converge rapidly to an optimal node position. Moreover, compared to other swarm intelligence techniques such as Bat algorithm (BAT), particle swarm optimization (PSO), Differential Evolution (DE) and Flower Pollination Algorithm (FPA), MFOA is shown to perform much better in node localization task.

scholarly journals Bat Algorithm Based an Adaptive PID Controller Design for Buck Converter Model

2020 ◽  
Vol 26 (7) ◽  
pp. 62-82
Author(s):  
Luay Thamir Rasheed
Keyword(s):  
Control System ◽  
Optimization Algorithm ◽  
Pid Controller ◽  
Tracking Error ◽  
Bat Algorithm ◽  
Input Voltage ◽  
Buck Converter ◽  
Control Action ◽  
On Line ◽  
Simulation Results

The aim of this paper is to design a PID controller based on an on-line tuning bat optimization algorithm for the step-down DC/DC buck converter system which is used in the battery operation of the mobile applications. In this paper, the bat optimization algorithm has been utilized to obtain the optimal parameters of the PID controller as a simple and fast on-line tuning technique to get the best control action for the system. The simulation results using (Matlab Package) show the robustness and the effectiveness of the proposed control system in terms of obtaining a suitable voltage control action as a smooth and unsaturated state of the buck converter input voltage of ( ) volt that will stabilize the buck converter system performance. The simulation results show also that the proposed control system when compared with the other controllers results has the capability of minimizing the rising time to (  sec) and the settling time to (  sec) in the transient response and minimizing the voltage tracking error of the system output to ( ) volt at the steady state response. Furthermore, the number of fitness evaluations is decreased.

Improved Chaotic Bacteria Foraging Optimization Algorithm Particle

2014 ◽  
Vol 651-653 ◽  
pp. 2322-2325
Author(s):  
Ying Ai ◽  
Yi Xin Su ◽  
Dan Hong Zhang ◽  
Yao Peng
Keyword(s):  
Optimization Algorithm ◽  
Chaotic Map ◽  
Standard Test ◽  
Convergence Speed ◽  
Test Functions ◽  
Algorithm Optimization ◽  
Slow Convergence ◽  
And Migration ◽  
Logistic Chaotic Map ◽  
Bacteria Foraging Optimization Algorithm

. Aiming at the defects of weak global search ability and slow convergence speed in bacteria foraging algorithm optimization, this paper proposed an improved chaotic bacteria foraging optimization algorithm which has introduced the chaotic thoughts, improved the update operation of fitness and migration operation in optimization process. Using Logistic chaotic map initializes the bacteria population, so as to improve the convergence speed of the algorithm; Then adjust quorum sensing mechanism to optimize the chemotactic direction of the bacteria, and operate on perished bacteria with chaos disturbance in migration operation, so as to improve the global optimization ability of the algorithm. Simulation of two standard test functions show that the proposed algorithm has higher convergence speed and precision.

scholarly journals Adaptive Bat Algorithm Optimization Strategy for Observation Matrix

2019 ◽  
Vol 9 (15) ◽  
pp. 3008 ◽  
Author(s):  
Zhihua Cui ◽  
Chunmei Zhang ◽  
Yaru Zhao ◽  
Zhentao Shi
Keyword(s):  
Bat Algorithm ◽  
Reconstruction Error ◽  
Optimization Strategy ◽  
Algorithm Optimization ◽  
Dynamic Adjustment ◽  
Memory Space ◽  
Adjustment Strategy ◽  
Reconstructed Signal ◽  
Observation Matrix ◽  
Signal Accuracy

Bat algorithm, as an optimization strategy of the observation matrix, has been widely used. Observation matrix has a direct impact on the reconstructed signal accuracy as a projection transformation matrix, and it has been widely used in various algorithms. However, for the traditional experimental process, randomly generated observation matrices often result in a larger reconstruction error and unstable reconstruction results. Therefore, it is a challenge to retain more feature information of the original signal and reduce reconstruction error. To obtain a more accurate reconstruction signal and less memory space, it is important to select an effective compression and reconstruction strategy. To solve this problem, an adaptive bat algorithm is proposed to optimize the observation matrix in this paper. For the adaptive bat algorithm, we design a dynamic adjustment strategy of the optimal radius to improve its global convergence ability. The results of our simulation experiments verify that, compared with other algorithms, it can effectively reduce the reconstruction error and has stronger robustness.

A Nature-Inspired Metaheuristic Cuckoo Search Algorithm for Community Detection in Social Networks

2017 ◽  
Vol 8 (1) ◽  
pp. 50-62 ◽  
Author(s):  
Ramadan Babers ◽  
Aboul Ella Hassanien
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Optimization Algorithm ◽  
Search Algorithm ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Krill Herd ◽  
Artificial Fish Swarm ◽  
Lion Optimization Algorithm

In last few years many approaches have been proposed to detect communities in social networks using diverse ways. Community detection is one of the important researches in social networks and graph analysis. This paper presents a cuckoo search optimization algorithm with Lévy flight for community detection in social networks. Experimental on well-known benchmark data sets demonstrates that the proposed algorithm can define the structure and detect communities of complex networks with high accuracy and quality. In addition, the proposed algorithm is compared with some swarms algorithms including discrete bat algorithm, artificial fish swarm, discrete Krill Herd, ant lion algorithm and lion optimization algorithm and the results show that the proposed algorithm is competitive with these algorithms.

scholarly journals Feature selection algorithm for usability engineering: a nature inspired approach

2021 ◽  
Author(s):  
Rajat Jain ◽  
Tania Joseph ◽  
Anvita Saxena ◽  
Deepak Gupta ◽  
Ashish Khanna ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Evaluation Method ◽  
Bat Algorithm ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
User Friendliness ◽  
Software Usability ◽  
Software Model ◽  
Whale Optimization ◽  
Selection Of

AbstractSoftware usability is usually used in reference to the hierarchical software usability model by researchers and is an important aspect of user experience and software quality. Thus, evaluation of software usability is an essential parameter for managing and regulating a software. However, it has been difficult to establish a precise evaluation method for this problem. A large number of usability factors have been suggested by many researchers, each covering a set of different factors to increase the degree of user friendliness of a software. Therefore, the selection of the correct determining features is of paramount importance. This paper proposes an innovative metaheuristic algorithm for the selection of most important features in a hierarchical software model. A hierarchy-based usability model is an exhaustive interpretation of the factors, attributes, and its characteristics in a software at different levels. This paper proposes a modified version of grey wolf optimisation algorithm (GWO) termed as modified grey wolf optimization (MGWO) algorithm. The mechanism of this algorithm is based on the hunting mechanism of wolves in nature. The algorithm chooses a number of features which are then applied to software development life cycle models for finding out the best among them. The outcome of this application is also compared with the conventional grey wolf optimization algorithm (GWO), modified binary bat algorithm (MBBAT), modified whale optimization algorithm (MWOA), and modified moth flame optimization (MMFO). The results show that MGWO surpasses all the other relevant optimizers in terms of accuracy and produces a lesser number of attributes equal to 8 as compared to 9 in MMFO and 12 in MBBAT and 19 in MWOA.

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