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Article
Publication date: 18 January 2021

Shahla U. Umar and Tarik A. Rashid

The purpose of this study is to provide the reader with a full study of the bat algorithm, including its limitations, the fields that the algorithm has been applied…

Abstract

Purpose

The purpose of this study is to provide the reader with a full study of the bat algorithm, including its limitations, the fields that the algorithm has been applied, versatile optimization problems in different domains and all the studies that assess its performance against other meta-heuristic algorithms.

Design/methodology/approach

Bat algorithm is given in-depth in terms of backgrounds, characteristics, limitations, it has also displayed the algorithms that hybridized with BA (K-Medoids, back-propagation neural network, harmony search algorithm, differential evaluation strategies, enhanced particle swarm optimization and Cuckoo search algorithm) and their theoretical results, as well as to the modifications that have been performed of the algorithm (modified bat algorithm, enhanced bat algorithm, bat algorithm with mutation (BAM), uninhabited combat aerial vehicle-BAM and non-linear optimization). It also provides a summary review that focuses on improved and new bat algorithm (directed artificial bat algorithm, complex-valued bat algorithm, principal component analyzes-BA, multiple strategies coupling bat algorithm and directional bat algorithm).

Findings

Shed light on the advantages and disadvantages of this algorithm through all the research studies that dealt with the algorithm in addition to the fields and applications it has addressed in the hope that it will help scientists understand and develop it.

Originality/value

As far as the research community knowledge, there is no comprehensive survey study conducted on this algorithm covering all its aspects.

Details

World Journal of Engineering, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1708-5284

Keywords

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Article
Publication date: 11 April 2018

Mohamed A. Tawhid and Kevin B. Dsouza

In this paper, we present a new hybrid binary version of bat and enhanced particle swarm optimization algorithm in order to solve feature selection problems. The proposed…

Abstract

In this paper, we present a new hybrid binary version of bat and enhanced particle swarm optimization algorithm in order to solve feature selection problems. The proposed algorithm is called Hybrid Binary Bat Enhanced Particle Swarm Optimization Algorithm (HBBEPSO). In the proposed HBBEPSO algorithm, we combine the bat algorithm with its capacity for echolocation helping explore the feature space and enhanced version of the particle swarm optimization with its ability to converge to the best global solution in the search space. In order to investigate the general performance of the proposed HBBEPSO algorithm, the proposed algorithm is compared with the original optimizers and other optimizers that have been used for feature selection in the past. A set of assessment indicators are used to evaluate and compare the different optimizers over 20 standard data sets obtained from the UCI repository. Results prove the ability of the proposed HBBEPSO algorithm to search the feature space for optimal feature combinations.

Details

Applied Computing and Informatics, vol. 16 no. 1/2
Type: Research Article
ISSN: 2634-1964

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Article
Publication date: 19 June 2019

Wensheng Xiao, Qi Liu, Linchuan Zhang, Kang Li and Lei Wu

Bat algorithm (BA) is a global optimization method, but has a worse performance on engineering optimization problems. The purpose of this study is to propose a novel…

Abstract

Purpose

Bat algorithm (BA) is a global optimization method, but has a worse performance on engineering optimization problems. The purpose of this study is to propose a novel chaotic bat algorithm based on catfish effect (CE-CBA), which can effectively deal with optimization problems in real-world applications.

Design/methodology/approach

Incorporating chaos strategy and catfish effect, the proposed algorithm can not only enhance the ability for local search but also improve the ability to escape from local optima traps. On the one hand, the performance of CE-CBA has been evaluated by a set of numerical experiment based on classical benchmark functions. On the other hand, five benchmark engineering design problems have been also used to test CE-CBA.

Findings

The statistical results of the numerical experiment show the significant improvement of CE-CBA compared with the standard algorithms and improved bat algorithms. Moreover, the feasibility and effectiveness of CE-CBA in solving engineering optimization problems are demonstrated.

Originality/value

This paper proposed a novel BA with two improvement strategies including chaos strategy and catfish effect for the first time. Meanwhile, the proposed algorithm can be used to solve many real-world engineering optimization problems with several decision variables and constraints.

Details

Engineering Computations, vol. 36 no. 5
Type: Research Article
ISSN: 0264-4401

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Article
Publication date: 13 July 2012

Xin‐She Yang and Amir Hossein Gandomi

Nature‐inspired algorithms are among the most powerful algorithms for optimization. The purpose of this paper is to introduce a new nature‐inspired metaheuristic…

Abstract

Purpose

Nature‐inspired algorithms are among the most powerful algorithms for optimization. The purpose of this paper is to introduce a new nature‐inspired metaheuristic optimization algorithm, called bat algorithm (BA), for solving engineering optimization tasks.

Design/methodology/approach

The proposed BA is based on the echolocation behavior of bats. After a detailed formulation and explanation of its implementation, BA is verified using eight nonlinear engineering optimization problems reported in the specialized literature.

Findings

BA has been carefully implemented and carried out optimization for eight well‐known optimization tasks; then a comparison has been made between the proposed algorithm and other existing algorithms.

Originality/value

The optimal solutions obtained by the proposed algorithm are better than the best solutions obtained by the existing methods. The unique search features used in BA are analyzed, and their implications for future research are also discussed in detail.

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Article
Publication date: 16 April 2018

Naser Safaeian Hamzehkolaei, Mahmoud Miri and Mohsen Rashki

Reliability-based design optimizations (RBDOs) of engineering structures involve complex non-linear/non-differentiable performance functions, including both continuous and…

Abstract

Purpose

Reliability-based design optimizations (RBDOs) of engineering structures involve complex non-linear/non-differentiable performance functions, including both continuous and discrete variables. The gradient-based RBDO algorithms are less than satisfactory for these cases. The simulation-based approaches could also be computationally inefficient, especially when the double-loop strategy is used. This paper aims to present a pseudo-double loop flexible RBDO, which is efficient for solving problems, including both discrete/continuous variables.

Design/methodology/approach

The method is based on the hybrid improved binary bat algorithm (BBA) and weighed simulation method (WSM). According to this method, each BBA’s movement generates proper candidate solutions, and subsequently, WSM evaluates the reliability levels for design candidates to conduct swarm in a low-cost safe-region.

Findings

The accuracy of the proposed enhanced BBA and also the hybrid WSM-BBA are examined for ten benchmark deterministic optimizations and also four RBDO problems of truss structures, respectively. The solved examples reveal computational efficiency and superiority of the method to conventional RBDO approaches for solving complex problems including discrete variables.

Originality/value

Unlike other RBDO approaches, the proposed method is such organized that only one simulation run suffices during the optimization process. The flexibility future of the proposed RBDO framework enables a designer to present multi-level design solutions for different arrangements of the problem by using the results of the only one simulation for WSM, which is very helpful to decrease computational burden of the RBDO. In addition, a new suitable transfer function that enhanced convergence rate and search ability of the original BBA is introduced.

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Article
Publication date: 7 January 2019

Balamurali Gunji, Deepak B.B.V.L., Saraswathi M.B.L. and Umamaheswara Rao Mogili

The purpose of this paper is to obtain an optimal mobile robot path planning by the hybrid algorithm, which is developed by two nature inspired meta-heuristic algorithms

Abstract

Purpose

The purpose of this paper is to obtain an optimal mobile robot path planning by the hybrid algorithm, which is developed by two nature inspired meta-heuristic algorithms, namely, cuckoo-search and bat algorithm (BA) in an unknown or partially known environment. The cuckoo-search algorithm is based on the parasitic behavior of the cuckoo, and the BA is based on the echolocation behavior of the bats.

Design/methodology/approach

The developed algorithm starts by sensing the obstacles in the environment using ultrasonic sensor. If there are any obstacles in the path, the authors apply the developed algorithm to find the optimal path otherwise reach the target point directly through diagonal distance.

Findings

The developed algorithm is implemented in MATLAB for the simulation to test the efficiency of the algorithm for different environments. The same path is considered to implement the experiment in the real-world environment. The ARDUINO microcontroller along with the ultrasonic sensor is considered to obtain the path length and time of travel of the robot to reach the goal point.

Originality/value

In this paper, a new hybrid algorithm has been developed to find the optimal path of the mobile robot using cuckoo search and BAs. The developed algorithm is tested with the real-world environment using the mobile robot.

Details

International Journal of Intelligent Unmanned Systems, vol. 7 no. 1
Type: Research Article
ISSN: 2049-6427

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Article
Publication date: 16 May 2019

Bourahla Kheireddine, Belli Zoubida and Hacib Tarik

This study aims to improve the bat algorithm (BA) performance for solving optimization problems in electrical engineering.

Abstract

Purpose

This study aims to improve the bat algorithm (BA) performance for solving optimization problems in electrical engineering.

Design/methodology/approach

For this task, two strategies were investigated. The first one is based on including the crossover technique into classical BA, in the same manner as in the genetic algorithm method. Therefore, the newly generated version of BA is called the crossover–bat algorithm (C-BA). In the second strategy, a hybridization of the BA with the Nelder–Mead (NM) simplex method was performed; it gives the NM-BA algorithm.

Findings

First, the proposed strategies were applied to solve a set of two standard benchmark problems; then, they were applied to solve the TEAM workshop problem 25, where an electromagnetic field was computed by use of the 2D non-linear finite element method. Both optimization algorithms and finite element computation tool were implemented under MATLAB.

Originality/value

The two proposed optimization strategies, C-BA and NM-BA, have allowed good improvements of classical BA, generally known for its poor solution quality and slow convergence rate.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering , vol. 38 no. 3
Type: Research Article
ISSN: 0332-1649

Keywords

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Article
Publication date: 3 June 2019

Bourahla Kheireddine, Belli Zoubida and Hacib Tarik

This paper aims to deal with the development of a newly improved version of teaching learning based optimization (TLBO) algorithm.

Abstract

Purpose

This paper aims to deal with the development of a newly improved version of teaching learning based optimization (TLBO) algorithm.

Design/methodology/approach

Random local search part was added to the classic optimization process with TLBO. The new version is called TLBO algorithm with random local search (TLBO-RLS).

Findings

At first step and to validate the effectiveness of the new proposed version of the TLBO algorithm, it was applied to a set of two standard benchmark problems. After, it was used jointly with two-dimensional non-linear finite element method to solve the TEAM workshop problem 25, where the results were compared with those resulting from classical TLBO, bat algorithm, hybrid TLBO, Nelder–Mead simplex method and other referenced work.

Originality value

New TLBO-RLS proposed algorithm contains a part of random local search, which allows good exploitation of the solution space. Therefore, TLBO-RLS provides better solution quality than classic TLBO.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering , vol. 38 no. 3
Type: Research Article
ISSN: 0332-1649

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Article
Publication date: 1 March 2021

Hardi M. Mohammed, Zrar Kh. Abdul, Tarik A. Rashid, Abeer Alsadoon and Nebojsa Bacanin

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…

Abstract

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.

Details

World Journal of Engineering, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1708-5284

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Book part
Publication date: 30 September 2020

B. G. Deepa and S. Senthil

Breast cancer (BC) is one of the leading cancer in the world, BC risk has been there for women of the middle age also, it is the malignant tumor. However, identifying BC…

Abstract

Breast cancer (BC) is one of the leading cancer in the world, BC risk has been there for women of the middle age also, it is the malignant tumor. However, identifying BC in the early stage will save most of the women’s life. As there is an advancement in the technology research used Machine Learning (ML) algorithm Random Forest for ranking the feature, Support Vector Machine (SVM), and Naïve Bayes (NB) supervised classifiers for selection of best optimized features and prediction of BC accuracy. The estimation of prediction accuracy has been done by using the dataset Wisconsin Breast Cancer Data from University of California Irvine (UCI) ML repository. To perform all these operation, Anaconda one of the open source distribution of Python has been used. The proposed work resulted in extemporize improvement in the NB and SVM classifier accuracy. The performance evaluation of the proposed model is estimated by using classification accuracy, confusion matrix, mean, standard deviation, variance, and root mean-squared error.

The experimental results shows that 70-30 data split will result in best accuracy. SVM acts as a feature optimizer of 12 best features with the result of 97.66% accuracy and improvement of 1.17% after feature reduction. NB results with feature optimizer 17 of best features with the result of 96.49% accuracy and improvement of 1.17% after feature reduction.

The study shows that proposal model works very effectively as compare to the existing models with respect to accuracy measures.

Details

Big Data Analytics and Intelligence: A Perspective for Health Care
Type: Book
ISBN: 978-1-83909-099-8

Keywords

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