Hybridizing gravitational search algorithm with real coded genetic algorithms for structural engineering design problem

OPSEARCH ◽  
2017 ◽  
Vol 54 (3) ◽  
pp. 505-536 ◽  
Author(s):  
Amarjeet Singh ◽  
Kusum Deep
Keyword(s):  
Genetic Algorithms ◽  
Engineering Design ◽  
Design Problem ◽  
Structural Engineering ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Engineering Design Problem ◽  
Gravitational Search ◽  
Real Coded Genetic Algorithms

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.

scholarly journals Optimal Solution of Structural Engineering Design Problems using Crow Search Algorithm

2019 ◽  
Vol 4 (4) ◽  
pp. 968-981
Author(s):  
Madhur Agarwal
Keyword(s):  
Engineering Design ◽  
Design Problem ◽  
Structural Engineering ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Worst Case ◽  
Design Problems ◽  
Beam Design ◽  
Engineering Design Problems ◽  
Better Than

In real world, the structural engineering design problems are large scale non-linear constrained problems. In the present study, crow search algorithm (CSA) is applied to find the optimal solution of structural engineering design problems such as pressure vessel design problem, welded beam design problem and tension/ compression string design problem. The numerical results are compared with the existing results reported in the literature including metaheuristic algorithms and it is found that the results obtained by the crow search algorithm are better than other existing algorithms. Further, the effectiveness of the algorithm is verified to be better than the existing algorithms by statistical analysis using mean, median, best case, and worst case scenarios. The present study confirms that the crow search algorithm may be easily and effectively applied to various structural design problems.

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.

Analysis of Optimal Power Flow Using Gravitational Search Algorithm

2016 ◽  
Vol 3 (4) ◽  
pp. 1-11
Author(s):  
M. Lakshmikantha Reddy ◽  
◽  
M. Ramprasad Reddy ◽  
V.C. Veera Reddy ◽  
◽  
...  
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Optimal Power ◽  
Gravitational Search
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