scholarly journals ON THE USE OF HEURISTIC ALGORITHMS IN THE PROBLEMS OF OPTIMAL PARAMET-RIC SYNTHESIS

2021 ◽  
pp. 34-46
Author(s):  
O.V. Abramov ◽  
◽  
A.D. Lagunova ◽  
Keyword(s):  
Heuristic Algorithms ◽  
Bat Algorithm ◽  
Optimal Synthesis ◽  
Engineering Systems ◽  
Operational Parameters ◽  
Multidimensional Search ◽  
Search Optimization ◽  
The Creation

The problem of optimal synthesis of engineering systems is considered, with due account for the ran-dom changes in their production and operational parameters. A number of ways and means of creating effective heuristic algorithms for multidimensional search optimization are proposed to solve the emerging problems. The paper focuses on the bat algorithm and the creation of its parallel modifica-tion.

scholarly journals FLC control for tuning exploration phase in bio-inspired metaheuristic

2017 ◽  
Vol 16 (2) ◽  
pp. 32
Author(s):  
Kazimierz Kiełkowicz ◽  
Damian Grela
Keyword(s):  
Fuzzy Logic Controller ◽  
Expert Knowledge ◽  
Bat Algorithm ◽  
Search Space ◽  
Search Process ◽  
Control Parameters ◽  
Local Search Methods ◽  
Multidimensional Search ◽  
Exploration Phase

<p>Growing popularity of the Bat Algorithm has encouraged researchers to focus their work on its further improvements. Most work has been done within the area of hybridization of Bat Algorithm with other metaheuristics or local search methods. Unfortunately, most of these modifications not only improves the quality of obtained solutions, but also increases the number of control parameters that are needed to be set in order to obtain solutions of expected quality. This makes such solutions quite impractical. What more, there is no clear indication what these parameters do in term of a search process. In this paper authors are trying to incorporate Mamdani type Fuzzy Logic Controller (FLC) to tackle some of these mentioned shortcomings by using the FLC to control the exploration phase of a bio-inspired metaheuristic. FLC also allows us to incorporate expert knowledge about the problem at hand and define expected behaviors of system – here process of searching in multidimensional search space by modeling the process of bats hunting for their prey.</p>

scholarly journals New Cepstrum Based Image Restoration Algorithm for Grayscale Images

2019 ◽  
Vol 8 (11) ◽  
pp. 1920-1928
Keyword(s):  
Image Restoration ◽  
Heuristic Algorithms ◽  
Bat Algorithm ◽  
Motion Blur ◽  
Hybrid Technique ◽  
Restoration Algorithm ◽  
Blur Estimation ◽  
Blurred Images ◽  
Selection Of ◽  
Grayscale Images

Optimization is the process that relates to finding the most excellent ways for all possible solutions. From last 2-3 decades, natural algorithms play an important role in improving solutions of various problems. By comparing various meta-heuristic algorithms, researchers can make a choice to the best selection of the meta-heuristic algorithms for the proposed problem. In this particular research, we have applied New Cepstrum based technique of image restoration to find out PSF parameters of motion blurred images as a primary technique. In addition, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), BAT Algorithm and GA-BAT hybrid technique etc. are also applied to optimize the blur parameters for calculated by new cepstrum based technique for blur estimation. This aids in analyzing the performance of each algorithm on the same primary technique. The performance analysis of all four algorithms aid in making the decision on the best meta-heuristic algorithm of the cepstrum based technique and to identify the preciseness of the motion blur. All four methods are applied to the same set of images. The algorithm is tested and compared using grayscale images and the benchmarking freely available online datasets, respectivel

Feature selection in classification using self-adaptive owl search optimization algorithm with elitism and mutation strategies

2021 ◽  
Vol 40 (1) ◽  
pp. 535-550
Author(s):  
Ashis Kumar Mandal ◽  
Rikta Sen ◽  
Basabi Chakraborty
Keyword(s):  
Feature Selection ◽  
Optimization Algorithm ◽  
Classification Accuracy ◽  
Heuristic Algorithms ◽  
Support Vector ◽  
Svm Classifier ◽  
Binary Particle Swarm Optimization ◽  
Feature Subset ◽  
Search Optimization ◽  
Self Adaptive

The fundamental aim of feature selection is to reduce the dimensionality of data by removing irrelevant and redundant features. As finding out the best subset of features from all possible subsets is computationally expensive, especially for high dimensional data sets, meta-heuristic algorithms are often used as a promising method for addressing the task. In this paper, a variant of recent meta-heuristic approach Owl Search Optimization algorithm (OSA) has been proposed for solving the feature selection problem within a wrapper-based framework. Several strategies are incorporated with an aim to strengthen BOSA (binary version of OSA) in searching the global best solution. The meta-parameter of BOSA is initialized dynamically and then adjusted using a self-adaptive mechanism during the search process. Besides, elitism and mutation operations are combined with BOSA to control the exploitation and exploration better. This improved BOSA is named in this paper as Modified Binary Owl Search Algorithm (MBOSA). Decision Tree (DT) classifier is used for wrapper based fitness function, and the final classification performance of the selected feature subset is evaluated by Support Vector Machine (SVM) classifier. Simulation experiments are conducted on twenty well-known benchmark datasets from UCI for the evaluation of the proposed algorithm, and the results are reported based on classification accuracy, the number of selected features, and execution time. In addition, BOSA along with three common meta-heuristic algorithms Binary Bat Algorithm (BBA), Binary Particle Swarm Optimization (BPSO), and Binary Genetic Algorithm (BGA) are used for comparison. Simulation results show that the proposed approach outperforms similar methods by reducing the number of features significantly while maintaining a comparable level of classification accuracy.

scholarly journals An Optimization of Feature Selection for Classification using Bat Algorithm

2021 ◽  
Vol 9 (6) ◽  
pp. 39-43
Author(s):  
Yasaswini V. ◽  
Santhi Baskaran
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Heuristic Algorithms ◽  
Harmony Search ◽  
Medical Engineering ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Vital Role ◽  
Target Class ◽  
Data Set

Data mining is the action of searching the large existing database in order to get new and best information. It plays a major and vital role now-a-days in all sorts of fields like Medical, Engineering, Banking, Education and Fraud detection. In this paper Feature selection which is a part of Data mining is performed to do classification. The role of feature selection is in the context of deep learning and how it is related to feature engineering. Feature selection is a preprocessing technique which selects the appropriate features from the data set to get the accurate result and outcome for the classification. Natureinspired Optimization algorithms like Ant colony, Firefly, Cuckoo Search and Harmony Search showed better performance by giving the best accuracy rate with less number of features selected and also fine f-Measure value is noted. These algorithms are used to perform classification that accurately predicts the target class for each case in the data set. We propose a technique to get the optimized feature selection to perform classification using Meta Heuristic algorithms. We applied new and recent advanced optimized algorithm named Bat algorithm on UCI datasets that showed comparatively equal results with best performed existing firefly but with less number of features selected. The work is implemented using JAVA and the Medical dataset (UCI) has been used. These datasets were chosen due to nominal class features. The number of attributes, instances and classes varies from chosen dataset to represent different combinations. Classification is done using J48 classifier in WEKA tool. We demonstrate the comparative results of the presently used algorithms with the existing algorithms thoroughly.

scholarly journals A new K-means grey wolf algorithm for engineering problems

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hardi M. Mohammed ◽  
Zrar Kh. Abdul ◽  
Tarik A. Rashid ◽  
Abeer Alsadoon ◽  
Nebojsa Bacanin
Keyword(s):  
Clustering Algorithm ◽  
Heuristic Algorithms ◽  
Bat Algorithm ◽  
Engineering Problem ◽  
Gray Wolf ◽  
Grey Wolf ◽  
Content Type ◽  
Gray Wolves ◽  
Whale Optimization ◽  
Benchmark Test Functions

Purpose This paper aims at studying meta-heuristic algorithms. One of the common meta-heuristic optimization algorithms is called grey wolf optimization (GWO). The key aim is to enhance the limitations of the wolves’ searching process of attacking gray wolves. Design/methodology/approach The development of meta-heuristic algorithms has increased by researchers to use them extensively in the field of business, science and engineering. In this paper, the K-means clustering algorithm is used to enhance the performance of the original GWO; the new algorithm is called K-means clustering gray wolf optimization (KMGWO). Findings Results illustrate the efficiency of KMGWO against to the GWO. To evaluate the performance of the KMGWO, KMGWO applied to solve CEC2019 benchmark test functions. Originality/value Results prove that KMGWO is superior to GWO. KMGWO is also compared to cat swarm optimization (CSO), whale optimization algorithm-bat algorithm (WOA-BAT), WOA and GWO so KMGWO achieved the first rank in terms of performance. In addition, the KMGWO is used to solve a classical engineering problem and it is superior.

scholarly journals Soil Fatigue from Induced Seismicity

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Merissa L. Zuzulock ◽  
Oliver-Denzil S. Taylor ◽  
Norbert H. Maerz.
Keyword(s):  
Short Duration ◽  
Seismic Activity ◽  
Induced Seismicity ◽  
Quantitative Data ◽  
Civil Engineering ◽  
Specific Area ◽  
Seismic Load ◽  
Engineering Systems ◽  
Operational Parameters ◽  
Natural Conditions

Induced seismicity and the effects on civil engineering systems are not completely understood and infrequently studied. One specific area that is not well known is soil fatigue which includes factors such as understanding the natural conditions of the subsurface as well as operational parameters under short duration impulse loads. With the increase of geoinduced seismic activity, soil fatigue becomes of greater concern to structures in the vicinity of this seismic load. The foundations of these structures can be affected by impulse loads which can ultimately cause failure. The lack of quantitative data puts the reliability of these civil engineering systems at risk as they are not fully evaluated to determine if they are functioning as they are intended in the environments they are designed to support.

An Improved Chaotic Binary Bat Algorithm for QoS Multicast Routing

2016 ◽  
Vol 25 (04) ◽  
pp. 1650025 ◽  
Author(s):  
Yassine Meraihi ◽  
Dalila Acheli ◽  
Amar Ramdane-Cherif
Keyword(s):  
Particle Swarm Optimization ◽  
Communication Networks ◽  
Heuristic Algorithms ◽  
Multicast Routing ◽  
Logistic Map ◽  
Particle Swarm ◽  
Bat Algorithm ◽  
Multicast Tree ◽  
Swarm Optimization ◽  
Routing Problem

The quality of service (QoS) multicast routing problem is one of the main issues for transmission in communication networks. It is known to be an NP-hard problem, so many heuristic algorithms have been employed to solve the multicast routing problem and find the optimal multicast tree which satisfies the requirements of multiple QoS constraints such as delay, delay jitter, bandwidth and packet loss rate. In this paper, we propose an improved chaotic binary bat algorithm to solve the QoS multicast routing problem. We introduce two modification methods into the binary bat algorithm. First, we use the two most representative chaotic maps, namely the logistic map and the tent map, to determine the parameter [Formula: see text] of the pulse frequency [Formula: see text]. Second, we use a dynamic formulation to update the parameter α of the loudness [Formula: see text]. The aim of these modifications is to enhance the performance and the robustness of the binary bat algorithm and ensure the diversity of the solutions. The simulation results reveal the superiority, effectiveness and efficiency of our proposed algorithms compared with some well-known algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Jumping Particle Swarm Optimization (JPSO), and Binary Bat Algorithm (BBA).

scholarly journals Nano- and Micro-Porous Chitosan Membranes for Human Epidermal Stratification and Differentiation

Membranes ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 394
Author(s):  
Simona Salerno ◽  
Maria Penelope De Santo ◽  
Enrico Drioli ◽  
Loredana De Bartolo
Keyword(s):  
Tissue Engineering ◽  
Surface Properties ◽  
Membrane Surface ◽  
Human Keratinocytes ◽  
Operational Parameters ◽  
Proliferating Cells ◽  
Phase Inversion Technique ◽  
Porous Chitosan ◽  
The Creation ◽  
Toxicity Of Drugs

The creation of partial or complete human epidermis represents a critical aspect and the major challenge of skin tissue engineering. This work was aimed at investigating the effect of nano- and micro-structured CHT membranes on human keratinocyte stratification and differentiation. To this end, nanoporous and microporous membranes of chitosan (CHT) were prepared by phase inversion technique tailoring the operational parameters in order to obtain nano- and micro-structured flat membranes with specific surface properties. Microporous structures with different mean pore diameters were created by adding and dissolving, in the polymeric solution, polyethylene glycol (PEG Mw 10,000 Da) as porogen, with a different CHT/PEG ratio. The developed membranes were characterized and assessed for epidermal construction by culturing human keratinocytes on them for up to 21 days. The overall results demonstrated that the membrane surface properties strongly affect the stratification and terminal differentiation of human keratinocytes. In particular, human keratinocytes adhered on nanoporous CHT membranes, developing the structure of the corneum epidermal top layer, characterized by low thickness and low cell proliferation. On the microporous CHT membrane, keratinocytes formed an epidermal basal lamina, with high proliferating cells that stratified and differentiated over time, migrating along the z axis and forming a multilayered epidermis. This strategy represents an attractive tissue engineering approach for the creation of specific human epidermal strata for testing the effects and toxicity of drugs, cosmetics and pollutants.

scholarly journals An Optimization of Feature Selection for Classification using Modified Bat Algorithm

2021 ◽  
Vol 13 (4) ◽  
pp. 38-46
Author(s):  
V. Yasaswini ◽  
◽  
Santhi Baskaran
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Heuristic Algorithms ◽  
Predictive Accuracy ◽  
Harmony Search ◽  
Medical Engineering ◽  
Bat Algorithm ◽  
Target Class ◽  
Data Set ◽  
Modified Bat Algorithm

Data mining is the action of searching the large existing database in order to get new and best information. It plays a major and vital role now-a-days in all sorts of fields like Medical, Engineering, Banking, Education and Fraud detection. In this paper Feature selection which is a part of Data mining is performed to do classification. The role of feature selection is in the context of deep learning and how it is related to feature engineering. Feature selection is a preprocessing technique which selects the appropriate features from the data set to get the accurate result and outcome for the classification. Nature-inspired Optimization algorithms like Ant colony, Firefly, Cuckoo Search and Harmony Search showed better performance by giving the best accuracy rate with less number of features selected and also fine fMeasure value is noted. These algorithms are used to perform classification that accurately predicts the target class for each case in the data set. We propose a technique to get the optimized feature selection to perform classification using Meta Heuristic algorithms. We applied new and recent advanced optimized algorithm named Modified Bat algorithm on University of California Irvine datasets that showed comparatively equal results with best performed existing firefly but with less number of features selected. The work is implemented using JAVA and the Medical dataset has been used. These datasets were chosen due to nominal class features. The number of attributes, instances and classes varies from chosen dataset to represent different combinations. Classification is done using J48 classifier in WEKA tool. We demonstrate the comparative results of the presently used algorithms with the existing algorithms thoroughly. The significance of this research is it will show a great impact in selecting the best features out of all the existing features which gives best accuracy rates which helps in extracting the information from raw data in Data Mining Domain. The Value of this research is it will manage main fields like medical and banking which gives exact and proper results in their respective field. The best quality of the research is to optimize the selection of features to achieve maximum predictive accuracy of the data sets which solves both single variable and multi-variable functions through the generation of binary structuring of features in the dataset and to increase the performance of classification by using nature inspired and Meta Heuristic algorithms.

Sign in / Sign up

Export Citation Format

Share Document