Swarm-based chaotic gravitational search algorithm for solving mechanical engineering design problems
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 I...
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Veröffentlicht in: | World journal of engineering 2020-02, Vol.17 (1), p.97-114 |
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description | 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. |
doi_str_mv | 10.1108/WJE-09-2019-0254 |
format | Article |
fullrecord | <record><control><sourceid>proquest_emera</sourceid><recordid>TN_cdi_proquest_journals_2499033058</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2499033058</sourcerecordid><originalsourceid>FETCH-LOGICAL-c311t-41fc297e548c14831b5b2c40bac824eb6c569e1a318b48456a575a2fe007ba713</originalsourceid><addsrcrecordid>eNptkctLAzEQh4MoWGrvHgOeY_PczR6l1BcFDyoeQ5LOblP2UZNtxf_elHoRzGUg_L4Z5huErhm9ZYzq-cfzktCKcMoqQrmSZ2jCFVNEU83P0YSVVBPFtbxEs5S2ND9ZcFaKCYLXLxs74myCNfYbO4zB4ybaQxjtGIbetjiBjX6DbdsMMYybDtdDxGloD6FvcAcZ6oPPOeib0APE4_caUmh6vIuDa6FLV-iitm2C2W-dovf75dvikaxeHp4WdyviBWMjkaz2vCpBSe2Z1II55biX1FmvuQRXeFVUwKxg2kktVWFVqSyvgdLS2ZKJKbo59c2DP_eQRrMd9jEvkQyXVUWFoErnFD2lfBxSilCbXQydjd-GUXP0abJPQytz9GmOPjMyPyHQQbTt-j_izwXED9O_d0I</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2499033058</pqid></control><display><type>article</type><title>Swarm-based chaotic gravitational search algorithm for solving mechanical engineering design problems</title><source>Emerald A-Z Current Journals</source><source>Standard: Emerald eJournal Premier Collection</source><creator>Rather, Sajad Ahmad ; Bala, P. Shanthi</creator><creatorcontrib>Rather, Sajad Ahmad ; Bala, P. Shanthi</creatorcontrib><description>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.</description><identifier>ISSN: 1708-5284</identifier><identifier>EISSN: 2515-8082</identifier><identifier>DOI: 10.1108/WJE-09-2019-0254</identifier><language>eng</language><publisher>Brentwood: Emerald Publishing Limited</publisher><subject>Chaos theory ; Compression springs ; Computer engineering ; Computer science ; Convergence ; Cost function ; Design engineering ; Design optimization ; Evolutionary algorithms ; Exploitation ; Gravitational constant ; Heuristic ; Heuristic methods ; Mechanical engineering ; Mechanical engineering design ; Optimization algorithms ; Optimization techniques ; Power dispatch ; Pressure vessel design ; Pressure vessels ; Problem solving ; Researchers ; Search algorithms ; Statistical tests</subject><ispartof>World journal of engineering, 2020-02, Vol.17 (1), p.97-114</ispartof><rights>Emerald Publishing Limited</rights><rights>Emerald Publishing Limited 2020</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c311t-41fc297e548c14831b5b2c40bac824eb6c569e1a318b48456a575a2fe007ba713</citedby><cites>FETCH-LOGICAL-c311t-41fc297e548c14831b5b2c40bac824eb6c569e1a318b48456a575a2fe007ba713</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.emerald.com/insight/content/doi/10.1108/WJE-09-2019-0254/full/html$$EHTML$$P50$$Gemerald$$H</linktohtml><link.rule.ids>314,780,784,967,11635,21695,27924,27925,52689,53244</link.rule.ids></links><search><creatorcontrib>Rather, Sajad Ahmad</creatorcontrib><creatorcontrib>Bala, P. Shanthi</creatorcontrib><title>Swarm-based chaotic gravitational search algorithm for solving mechanical engineering design problems</title><title>World journal of engineering</title><description>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.</description><subject>Chaos theory</subject><subject>Compression springs</subject><subject>Computer engineering</subject><subject>Computer science</subject><subject>Convergence</subject><subject>Cost function</subject><subject>Design engineering</subject><subject>Design optimization</subject><subject>Evolutionary algorithms</subject><subject>Exploitation</subject><subject>Gravitational constant</subject><subject>Heuristic</subject><subject>Heuristic methods</subject><subject>Mechanical engineering</subject><subject>Mechanical engineering design</subject><subject>Optimization algorithms</subject><subject>Optimization techniques</subject><subject>Power dispatch</subject><subject>Pressure vessel design</subject><subject>Pressure vessels</subject><subject>Problem solving</subject><subject>Researchers</subject><subject>Search algorithms</subject><subject>Statistical tests</subject><issn>1708-5284</issn><issn>2515-8082</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNptkctLAzEQh4MoWGrvHgOeY_PczR6l1BcFDyoeQ5LOblP2UZNtxf_elHoRzGUg_L4Z5huErhm9ZYzq-cfzktCKcMoqQrmSZ2jCFVNEU83P0YSVVBPFtbxEs5S2ND9ZcFaKCYLXLxs74myCNfYbO4zB4ybaQxjtGIbetjiBjX6DbdsMMYybDtdDxGloD6FvcAcZ6oPPOeib0APE4_caUmh6vIuDa6FLV-iitm2C2W-dovf75dvikaxeHp4WdyviBWMjkaz2vCpBSe2Z1II55biX1FmvuQRXeFVUwKxg2kktVWFVqSyvgdLS2ZKJKbo59c2DP_eQRrMd9jEvkQyXVUWFoErnFD2lfBxSilCbXQydjd-GUXP0abJPQytz9GmOPjMyPyHQQbTt-j_izwXED9O_d0I</recordid><startdate>20200219</startdate><enddate>20200219</enddate><creator>Rather, Sajad Ahmad</creator><creator>Bala, P. Shanthi</creator><general>Emerald Publishing Limited</general><general>Emerald Group Publishing Limited</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20200219</creationdate><title>Swarm-based chaotic gravitational search algorithm for solving mechanical engineering design problems</title><author>Rather, Sajad Ahmad ; Bala, P. Shanthi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c311t-41fc297e548c14831b5b2c40bac824eb6c569e1a318b48456a575a2fe007ba713</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Chaos theory</topic><topic>Compression springs</topic><topic>Computer engineering</topic><topic>Computer science</topic><topic>Convergence</topic><topic>Cost function</topic><topic>Design engineering</topic><topic>Design optimization</topic><topic>Evolutionary algorithms</topic><topic>Exploitation</topic><topic>Gravitational constant</topic><topic>Heuristic</topic><topic>Heuristic methods</topic><topic>Mechanical engineering</topic><topic>Mechanical engineering design</topic><topic>Optimization algorithms</topic><topic>Optimization techniques</topic><topic>Power dispatch</topic><topic>Pressure vessel design</topic><topic>Pressure vessels</topic><topic>Problem solving</topic><topic>Researchers</topic><topic>Search algorithms</topic><topic>Statistical tests</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Rather, Sajad Ahmad</creatorcontrib><creatorcontrib>Bala, P. Shanthi</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><jtitle>World journal of engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Rather, Sajad Ahmad</au><au>Bala, P. Shanthi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Swarm-based chaotic gravitational search algorithm for solving mechanical engineering design problems</atitle><jtitle>World journal of engineering</jtitle><date>2020-02-19</date><risdate>2020</risdate><volume>17</volume><issue>1</issue><spage>97</spage><epage>114</epage><pages>97-114</pages><issn>1708-5284</issn><eissn>2515-8082</eissn><abstract>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.</abstract><cop>Brentwood</cop><pub>Emerald Publishing Limited</pub><doi>10.1108/WJE-09-2019-0254</doi><tpages>18</tpages></addata></record> |
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subjects | Chaos theory Compression springs Computer engineering Computer science Convergence Cost function Design engineering Design optimization Evolutionary algorithms Exploitation Gravitational constant Heuristic Heuristic methods Mechanical engineering Mechanical engineering design Optimization algorithms Optimization techniques Power dispatch Pressure vessel design Pressure vessels Problem solving Researchers Search algorithms Statistical tests |
title | Swarm-based chaotic gravitational search algorithm for solving mechanical engineering design problems |
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