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1 – 10 of 297
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. 18 no. 4
Type: Research Article
ISSN: 1708-5284

Keywords

Open Access
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

Keywords

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

Keywords

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…

4651

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.

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.

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

Keywords

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

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

Keywords

Open Access
Article
Publication date: 19 November 2021

Łukasz Knypiński

The purpose of this paper is to execute the efficiency analysis of the selected metaheuristic algorithms (MAs) based on the investigation of analytical functions and…

Abstract

Purpose

The purpose of this paper is to execute the efficiency analysis of the selected metaheuristic algorithms (MAs) based on the investigation of analytical functions and investigation optimization processes for permanent magnet motor.

Design/methodology/approach

A comparative performance analysis was conducted for selected MAs. Optimization calculations were performed for as follows: genetic algorithm (GA), particle swarm optimization algorithm (PSO), bat algorithm, cuckoo search algorithm (CS) and only best individual algorithm (OBI). All of the optimization algorithms were developed as computer scripts. Next, all optimization procedures were applied to search the optimal of the line-start permanent magnet synchronous by the use of the multi-objective objective function.

Findings

The research results show, that the best statistical efficiency (mean objective function and standard deviation [SD]) is obtained for PSO and CS algorithms. While the best results for several runs are obtained for PSO and GA. The type of the optimization algorithm should be selected taking into account the duration of the single optimization process. In the case of time-consuming processes, algorithms with low SD should be used.

Originality/value

The new proposed simple nondeterministic algorithm can be also applied for simple optimization calculations. On the basis of the presented simulation results, it is possible to determine the quality of the compared MAs.

Details

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

Keywords

Article
Publication date: 22 April 2022

Seyed Salar Sefati, Simona Halunga and Roya Zareh Farkhady

Flying ad hoc networks (FANETs) have a major effect in various areas such as civil projects and smart cities. The facilities of installation and low cost of unmanned…

Abstract

Purpose

Flying ad hoc networks (FANETs) have a major effect in various areas such as civil projects and smart cities. The facilities of installation and low cost of unmanned aerial vehicles (UAVs) have created a new challenge for researchers. Cluster head (CH) selection and load balancing between the CH are the most critical issues in the FANETs. For CH selection and load balancing in FANETs, this study used efficient clustering to address both problems and overcome these challenges. This paper aims to propose a novel CH selection and load balancing scheme to solve the low energy consumption and low latency in the FANET system.

Design/methodology/approach

This paper tried to select the CH and load balancing with the help of low-energy adaptive clustering hierarchy (LEACH) algorithm and bat algorithm (BA). Load balancing and CH selection are NP-hard problems, so the metaheuristic algorithms can be the best answer for these issues. In the LEACH algorithm, UAVs randomly generate numerical, and these numbers are sorted according to those values. To use the load balancing, the threshold of CH has to be considered; if the threshold is less than 0.7, the BA starts working and begins to find new CH according to the emitted pulses.

Findings

The proposed method compares with three algorithms, called bio-inspired clustering scheme FANETs, Grey wolf optimization and ant colony optimization in the NS3 simulator. The proposed algorithm has a good efficiency with respect to the network lifetime, energy consumption and cluster building time.

Originality/value

This study aims to extend the UAV group control concepts to include CH selection and load balancing to improve UAV energy consumption and low latency.

Details

Aircraft Engineering and Aerospace Technology, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1748-8842

Keywords

1 – 10 of 297