Swarm-based chaotic gravitational search algorithm for solving mechanical engineering design problems

2020 ◽  
Vol 17 (1) ◽  
pp. 97-114
Author(s):  
Sajad Ahmad Rather ◽  
P. Shanthi Bala
Keyword(s):  
Engineering Design ◽  
Mechanical Engineering ◽  
Gravitational Constant ◽  
Chaotic Maps ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Experimental Results ◽  
Content Type ◽  
Efficient Performance ◽  
Gravitational Search

Purpose The purpose of this paper is to investigate the performance of chaotic gravitational search algorithm (CGSA) in solving mechanical engineering design frameworks including welded beam design (WBD), compression spring design (CSD) and pressure vessel design (PVD). Design/methodology/approach In this study, ten chaotic maps were combined with gravitational constant to increase the exploitation power of gravitational search algorithm (GSA). Also, CGSA has been used for maintaining the adaptive capability of gravitational constant. Furthermore, chaotic maps were used for overcoming premature convergence and stagnation in local minima problems of standard GSA. Findings The chaotic maps have shown efficient performance for WBD and PVD problems. Further, they have depicted competitive results for CSD framework. Moreover, the experimental results indicate that CGSA shows efficient performance in terms of convergence speed, cost function minimization, design variable optimization and successful constraint handling as compared to other participating algorithms. Research limitations/implications The use of chaotic maps in standard GSA is a new beginning for research in GSA particularly convergence and time complexity analysis. Moreover, CGSA can be used for solving the infinite impulsive response (IIR) parameter tuning and economic load dispatch problems in electrical sciences. Originality/value The hybridization of chaotic maps and evolutionary algorithms for solving practical engineering problems is an emerging topic in metaheuristics. In the literature, it can be seen that researchers have used some chaotic maps such as a logistic map, Gauss map and a sinusoidal map more rigorously than other maps. However, this work uses ten different chaotic maps for engineering design optimization. In addition, non-parametric statistical test, namely, Wilcoxon rank-sum test, was carried out at 5% significance level to statistically validate the simulation results. Besides, 11 state-of-the-art metaheuristic algorithms were used for comparative analysis of the experimental results to further raise the authenticity of the experimental setup.

scholarly journals Hybridization of Chaotic Maps and Gravitational Search Algorithm for Constrained Mechanical and Civil Engineering Design Frameworks

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Engineering Design ◽  
Chaotic Maps ◽  
Civil Engineering ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Gear Train ◽  
Significance Level ◽  
Beam Design ◽  
Gravitational Search

The Chaotic Gravitational Search Algorithm (CGSA) is a physics-based heuristic algorithm inspired by Newton's law of universal gravitation. It uses 10 chaotic maps for optimal global search and fast convergence rate. The advantages of CGSA has been incorporated in various Mechanical and Civil engineering design frameworks which include Speed Reducer Design (SRD), Gear Train Design (GTD), Three Bar Truss Design (TBTD), Stepped Cantilever Beam Design (SCBD), Multiple Disc Clutch Brake Design (MDCBD), and Hydrodynamic Thrust Bearing Design (HTBD). The CGSA has been compared with eleven state of the art stochastic algorithms. In addition, a non-parametric statistical test namely the Signed Wilcoxon Rank-Sum test has been carried out at a 5% significance level to statistically validate the results. The simulation results indicate that CGSA shows efficient performance in terms of high convergence speed and minimization of the design parameter values as compared to other heuristic algorithms. The source codes are publicly available on Github i.e. https://github.com/SajadAHMAD1.

Hybridization of Biogeography-Based Optimization and Gravitational Search Algorithm for Efficient Face Recognition

2019 ◽  
pp. 258-279
Author(s):  
Lavika Goel ◽  
Lavanya B. ◽  
Pallavi Panchal
Keyword(s):  
Face Recognition ◽  
Evolutionary Algorithm ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Recognition Rate ◽  
Experimental Results ◽  
Nature Inspired Algorithm ◽  
In The Wild ◽  
Gravitational Search

This chapter aims to apply a novel hybridized evolutionary algorithm to the application of face recognition. Biogeography-based optimization (BBO) has some element of randomness to it that apart from improving the feasibility of a solution could reduce it as well. In order to overcome this drawback, this chapter proposes a hybridization of BBO with gravitational search algorithm (GSA), another nature-inspired algorithm, by incorporating certain knowledge into BBO instead of the randomness. The migration procedure of BBO that migrates SIVs between solutions is done between solutions only if the migration would lead to the betterment of a solution. BBO-GSA algorithm is applied to face recognition with the LFW (labelled faces in the wild) and ORL datasets in order to test its efficiency. Experimental results show that the proposed BBO-GSA algorithm outperforms or is on par with some of the nature-inspired techniques that have been applied to face recognition so far by achieving a recognition rate of 80% with the LFW dataset and 99.75% with the ORL dataset.

Reduction of carbon emissions in VRP by gravitational search algorithm

2014 ◽  
Vol 25 (6) ◽  
pp. 766-782 ◽  
Author(s):  
S.A. MirHassani ◽  
S. Mohammadyari
Keyword(s):  
Air Pollution ◽  
Fuel Consumption ◽  
Carbon Emissions ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Influential Factors ◽  
Benchmark Problems ◽  
Minimum Length ◽  
Content Type ◽  
Gravitational Search

Purpose – Nowadays, global warming, due to large-scale emissions of greenhouse gasses, is among top environmental issues. The purpose of this paper is to present a problem involving the incorporation of environmental aspects into logistics, which provides a comparison between pollution reduction and distance-based approaches. Design/methodology/approach – In green vehicle routing problem (VRP), the aim is to model and solve an optimization problem in order to minimize the fuel consumption which results in reducing energy consumption as well as air pollution. The Gravitational Search Algorithm (GSA) is adapted and used as a powerful heuristic. Findings – Here, it is shown that a set of routes with minimum length is not an optimal solution for FCVRP model since the total distance is not the only effective factor for fuel consumption and vehicle's load plays an important role too. In many cases, a considerable reduction in emissions can be achieved by only an insignificant increase in costs. Research limitations/implications – Green transportation is a policy toward reducing carbon emissions. This research focussed on routes problem and introduce FCVRP model. GSA is used as a powerful heuristic to obtain high quality routes in a reasonable time. Considering other factors that affecting fuel consumption could make this study more realistic. Practical implications – When a distribution center receives all the information it needs about the demand from all the retail stores it supplies, a VRP is produced. So the models are valid for use by all goods producers and distributors. The preliminary assessment of the proposed model and method carried out on benchmark problems up to 200 nodes. Originality/value – Fuel consumption is one of the most influential factors in transportation costs. This paper introduces an innovative decision-making framework to obtain optimum routes in a vehicle routes problem considering air pollution. The results were compared from fuel consumption as well as total travel distance viewpoints.

Design technique for leakage current reduction in surge arrester using gravitational search algorithm and imperialist competitive algorithm

2018 ◽  
Vol 37 (1) ◽  
pp. 357-374 ◽  
Author(s):  
Nurul Ain Abdul Latiff ◽  
Hazlee Azil Illias ◽  
Ab Halim Abu Bakar ◽  
Syahirah Abd Halim ◽  
Sameh Ziad Dabbak
Keyword(s):  
Leakage Current ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Imperialist Competitive Algorithm ◽  
Original Design ◽  
Element Analysis ◽  
Content Type ◽  
Competitive Algorithm ◽  
Surge Arresters ◽  
Gravitational Search

Purpose Leakage current is one of the factors, which can contribute towards degradation of surge arresters. Thus, the purpose of this paper is to study on leakage current within surge arresters and improvement on their design. Design/methodology/approach In this work, a three-dimensional model geometry of 11 kV zinc oxide surge arrester was designed in finite element analysis and was applied to calculate the leakage current under normal operating condition and being verified with measurement results. The optimisation methods were used to improve the arrester design by minimising the leakage current across the arrester using imperialist competitive algorithm (ICA) and gravitational search algorithm (GSA). Findings The arrester design in reducing leakage current was successfully optimised by varying the glass permittivity, silicone rubber permittivity and the width of the ground terminal of the surge arrester. It was found that the surge arrester design obtained using ICA has lower leakage current than GSA and the original design of the surge arrester. Practical implications The comparison between measurement and simulation enables factors that affect the mechanism of leakage current in surge arresters to be identified and provides the ideal design of arrester. Originality/value Surge arrester design was optimised by ICA and GSA, which has never been applied in past works in designing surge arrester with minimum leakage current.

scholarly journals Levy Flight and Chaos Theory Based Gravitational Search Algorithm for Global Optimization

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Performance Metrics ◽  
Chaotic Maps ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Lévy Flight ◽  
Step Size ◽  
Levy Flight ◽  
Statistical Measures ◽  
Engineering Design Problems ◽  
Gravitational Search

The Gravitational Search Algorithm (GSA) is one of the highly regarded population-based algorithms. It has been reported that GSA has a powerful global exploration capability but suffers from the limitations of getting stuck in local optima and slow convergence speed. In order to resolve the aforementioned issues, a modified version of GSA has been proposed based on levy flight distribution and chaotic maps (LCGSA). In LCGSA, the diversification is performed by utilizing the high step size value of levy flight distribution while exploitation is carried out by chaotic maps. The LCGSA is tested on well-known 23 classical benchmark functions. Moreover, it is also applied to three constrained engineering design problems. Furthermore, the analysis of results is performed through various performance metrics like statistical measures, convergence rate, and so on. Also, a signed Wilcoxon rank-sum test has also been conducted. The simulation results indicate that LCGSA provides better results as compared to standard GSA and most of the competing algorithms.

A hybrid constriction coefficient-based particle swarm optimization and gravitational search algorithm for training multi-layer perceptron

2020 ◽  
Vol 13 (2) ◽  
pp. 129-165 ◽  
Author(s):  
Sajad Ahmad Rather ◽  
P. Shanthi Bala
Keyword(s):  
Particle Swarm Optimization ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Particle Swarm ◽  
Multi Layer Perceptron ◽  
Swarm Optimization ◽  
Local Optima ◽  
Content Type ◽  
Gravitational Search ◽  
Constriction Coefficient

PurposeIn this paper, a newly proposed hybridization algorithm namely constriction coefficient-based particle swarm optimization and gravitational search algorithm (CPSOGSA) has been employed for training MLP to overcome sensitivity to initialization, premature convergence, and stagnation in local optima problems of MLP.Design/methodology/approachIn this study, the exploration of the search space is carried out by gravitational search algorithm (GSA) and optimization of candidate solutions, i.e. exploitation is performed by particle swarm optimization (PSO). For training the multi-layer perceptron (MLP), CPSOGSA uses sigmoid fitness function for finding the proper combination of connection weights and neural biases to minimize the error. Secondly, a matrix encoding strategy is utilized for providing one to one correspondence between weights and biases of MLP and agents of CPSOGSA.FindingsThe experimental findings convey that CPSOGSA is a better MLP trainer as compared to other stochastic algorithms because it provides superior results in terms of resolving stagnation in local optima and convergence speed problems. Besides, it gives the best results for breast cancer, heart, sine function and sigmoid function datasets as compared to other participating algorithms. Moreover, CPSOGSA also provides very competitive results for other datasets.Originality/valueThe CPSOGSA performed effectively in overcoming stagnation in local optima problem and increasing the overall convergence speed of MLP. Basically, CPSOGSA is a hybrid optimization algorithm which has powerful characteristics of global exploration capability and high local exploitation power. In the research literature, a little work is available where CPSO and GSA have been utilized for training MLP. The only related research paper was given by Mirjalili et al., in 2012. They have used standard PSO and GSA for training simple FNNs. However, the work employed only three datasets and used the MSE performance metric for evaluating the efficiency of the algorithms. In this paper, eight different standard datasets and five performance metrics have been utilized for investigating the efficiency of CPSOGSA in training MLPs. In addition, a non-parametric pair-wise statistical test namely the Wilcoxon rank-sum test has been carried out at a 5% significance level to statistically validate the simulation results. Besides, eight state-of-the-art meta-heuristic algorithms were employed for comparative analysis of the experimental results to further raise the authenticity of the experimental setup.

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