Improved artificial bee colony algorithm for global optimization
The artificial bee colony algorithm is a relatively new optimization technique. This paper presents an improved artificial bee colony (IABC) algorithm for global optimization. Inspired by differential evolution (DE) and introducing a parameter M, we propose two improved solution search equations, na...
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| Veröffentlicht in: | Information processing letters 2011-09, Vol.111 (17), p.871-882 |
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| creator | Gao, Weifeng Liu, Sanyang |
| description | The artificial bee colony algorithm is a relatively new optimization technique. This paper presents an improved artificial bee colony (IABC) algorithm for global optimization. Inspired by differential evolution (DE) and introducing a parameter
M, we propose two improved solution search equations, namely “
ABC/best/1” and “
ABC/rand/1”. Then, in order to take advantage of them and avoid the shortages of them, we use a selective probability
p to control the frequency of introducing “
ABC/rand/1” and “
ABC/best/1” and get a new search mechanism. In addition, to enhance the global convergence speed, when producing the initial population, both the chaotic systems and the opposition-based learning method are employed. Experiments are conducted on a suite of unimodal/multimodal benchmark functions. The results demonstrate the good performance of the IABC algorithm in solving complex numerical optimization problems when compared with thirteen recent algorithms.
► “
ABC/best/1” and “
ABC/rand/1” are proposed. ► A new search mechanism is got by introducing a selective probability
p. ► Both opposition-based learning method and chaotic maps are employed. ► The experiment results demonstrate the good performance of the IABC algorithm. |
| doi_str_mv | 10.1016/j.ipl.2011.06.002 |
| format | Article |
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M, we propose two improved solution search equations, namely “
ABC/best/1” and “
ABC/rand/1”. Then, in order to take advantage of them and avoid the shortages of them, we use a selective probability
p to control the frequency of introducing “
ABC/rand/1” and “
ABC/best/1” and get a new search mechanism. In addition, to enhance the global convergence speed, when producing the initial population, both the chaotic systems and the opposition-based learning method are employed. Experiments are conducted on a suite of unimodal/multimodal benchmark functions. The results demonstrate the good performance of the IABC algorithm in solving complex numerical optimization problems when compared with thirteen recent algorithms.
► “
ABC/best/1” and “
ABC/rand/1” are proposed. ► A new search mechanism is got by introducing a selective probability
p. ► Both opposition-based learning method and chaotic maps are employed. ► The experiment results demonstrate the good performance of the IABC algorithm.</description><identifier>ISSN: 0020-0190</identifier><identifier>EISSN: 1872-6119</identifier><identifier>DOI: 10.1016/j.ipl.2011.06.002</identifier><identifier>CODEN: IFPLAT</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Algorithmics. Computability. Computer arithmetics ; Algorithms ; Applied sciences ; Artificial bee colony algorithm ; Calculus of variations and optimal control ; Colonies ; Computer science; control theory; systems ; Convergence ; Evolution ; Exact sciences and technology ; Initial population ; Learning ; Mathematical analysis ; Mathematical models ; Mathematics ; Miscellaneous ; Numerical analysis ; Numerical analysis. Scientific computation ; Numerical methods in mathematical programming, optimization and calculus of variations ; Numerical methods in optimization and calculus of variations ; Optimization ; Optimization algorithms ; Optimization techniques ; Probability distribution ; Randomized algorithms ; Sciences and techniques of general use ; Search mechanism ; Searching ; Solution search equation ; Studies ; Theoretical computing</subject><ispartof>Information processing letters, 2011-09, Vol.111 (17), p.871-882</ispartof><rights>2011 Elsevier B.V.</rights><rights>2015 INIST-CNRS</rights><rights>Copyright Elsevier Sequoia S.A. Sep 15, 2011</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c452t-916e6b7f5a764fbd675a5d86ace4eb24bf187a888209e0de224ee0dc50c47fba3</citedby><cites>FETCH-LOGICAL-c452t-916e6b7f5a764fbd675a5d86ace4eb24bf187a888209e0de224ee0dc50c47fba3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0020019011001670$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3536,27903,27904,65309</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=24576298$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Gao, Weifeng</creatorcontrib><creatorcontrib>Liu, Sanyang</creatorcontrib><title>Improved artificial bee colony algorithm for global optimization</title><title>Information processing letters</title><description>The artificial bee colony algorithm is a relatively new optimization technique. This paper presents an improved artificial bee colony (IABC) algorithm for global optimization. Inspired by differential evolution (DE) and introducing a parameter
M, we propose two improved solution search equations, namely “
ABC/best/1” and “
ABC/rand/1”. Then, in order to take advantage of them and avoid the shortages of them, we use a selective probability
p to control the frequency of introducing “
ABC/rand/1” and “
ABC/best/1” and get a new search mechanism. In addition, to enhance the global convergence speed, when producing the initial population, both the chaotic systems and the opposition-based learning method are employed. Experiments are conducted on a suite of unimodal/multimodal benchmark functions. The results demonstrate the good performance of the IABC algorithm in solving complex numerical optimization problems when compared with thirteen recent algorithms.
► “
ABC/best/1” and “
ABC/rand/1” are proposed. ► A new search mechanism is got by introducing a selective probability
p. ► Both opposition-based learning method and chaotic maps are employed. ► The experiment results demonstrate the good performance of the IABC algorithm.</description><subject>Algorithmics. Computability. Computer arithmetics</subject><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Artificial bee colony algorithm</subject><subject>Calculus of variations and optimal control</subject><subject>Colonies</subject><subject>Computer science; control theory; systems</subject><subject>Convergence</subject><subject>Evolution</subject><subject>Exact sciences and technology</subject><subject>Initial population</subject><subject>Learning</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>Mathematics</subject><subject>Miscellaneous</subject><subject>Numerical analysis</subject><subject>Numerical analysis. Scientific computation</subject><subject>Numerical methods in mathematical programming, optimization and calculus of variations</subject><subject>Numerical methods in optimization and calculus of variations</subject><subject>Optimization</subject><subject>Optimization algorithms</subject><subject>Optimization techniques</subject><subject>Probability distribution</subject><subject>Randomized algorithms</subject><subject>Sciences and techniques of general use</subject><subject>Search mechanism</subject><subject>Searching</subject><subject>Solution search equation</subject><subject>Studies</subject><subject>Theoretical computing</subject><issn>0020-0190</issn><issn>1872-6119</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LxDAQhoMouK7-AG9FEE-tk9gmLV4U8WNB8KLnkKaTNSVt1qQr6K83yy4ePHgamHlm5uUh5JRCQYHyy76wK1cwoLQAXgCwPTKjtWA5p7TZJ7PUgRxoA4fkKMYeAHh5JWbkZjGsgv_ELlNhssZqq1zWImbaOz9-ZcotfbDT-5AZH7Kl822a-9VkB_utJuvHY3JglIt4sqtz8vZw_3r3lD-_PC7ubp9zXVZsyhvKkbfCVErw0rQdF5WquporjSW2rGxNSqvqumbQIHTIWImp6gp0KUyrrubkYns3xf1YY5zkYKNG59SIfh1l3XBGmeB1Is_-kL1fhzGFk7UQiROMJohuIR18jAGNXAU7qPAlKciNUdnLZFRujErgMvlLO-e7wypq5UxQo7bxd5GVleCs2QS43nKYfHxaDDJqi6PGzgbUk-y8_efLDxV-i1k</recordid><startdate>20110915</startdate><enddate>20110915</enddate><creator>Gao, Weifeng</creator><creator>Liu, Sanyang</creator><general>Elsevier B.V</general><general>Elsevier</general><general>Elsevier Sequoia S.A</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20110915</creationdate><title>Improved artificial bee colony algorithm for global optimization</title><author>Gao, Weifeng ; Liu, Sanyang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c452t-916e6b7f5a764fbd675a5d86ace4eb24bf187a888209e0de224ee0dc50c47fba3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Algorithmics. Computability. Computer arithmetics</topic><topic>Algorithms</topic><topic>Applied sciences</topic><topic>Artificial bee colony algorithm</topic><topic>Calculus of variations and optimal control</topic><topic>Colonies</topic><topic>Computer science; control theory; systems</topic><topic>Convergence</topic><topic>Evolution</topic><topic>Exact sciences and technology</topic><topic>Initial population</topic><topic>Learning</topic><topic>Mathematical analysis</topic><topic>Mathematical models</topic><topic>Mathematics</topic><topic>Miscellaneous</topic><topic>Numerical analysis</topic><topic>Numerical analysis. Scientific computation</topic><topic>Numerical methods in mathematical programming, optimization and calculus of variations</topic><topic>Numerical methods in optimization and calculus of variations</topic><topic>Optimization</topic><topic>Optimization algorithms</topic><topic>Optimization techniques</topic><topic>Probability distribution</topic><topic>Randomized algorithms</topic><topic>Sciences and techniques of general use</topic><topic>Search mechanism</topic><topic>Searching</topic><topic>Solution search equation</topic><topic>Studies</topic><topic>Theoretical computing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gao, Weifeng</creatorcontrib><creatorcontrib>Liu, Sanyang</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Information processing letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gao, Weifeng</au><au>Liu, Sanyang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Improved artificial bee colony algorithm for global optimization</atitle><jtitle>Information processing letters</jtitle><date>2011-09-15</date><risdate>2011</risdate><volume>111</volume><issue>17</issue><spage>871</spage><epage>882</epage><pages>871-882</pages><issn>0020-0190</issn><eissn>1872-6119</eissn><coden>IFPLAT</coden><abstract>The artificial bee colony algorithm is a relatively new optimization technique. This paper presents an improved artificial bee colony (IABC) algorithm for global optimization. Inspired by differential evolution (DE) and introducing a parameter
M, we propose two improved solution search equations, namely “
ABC/best/1” and “
ABC/rand/1”. Then, in order to take advantage of them and avoid the shortages of them, we use a selective probability
p to control the frequency of introducing “
ABC/rand/1” and “
ABC/best/1” and get a new search mechanism. In addition, to enhance the global convergence speed, when producing the initial population, both the chaotic systems and the opposition-based learning method are employed. Experiments are conducted on a suite of unimodal/multimodal benchmark functions. The results demonstrate the good performance of the IABC algorithm in solving complex numerical optimization problems when compared with thirteen recent algorithms.
► “
ABC/best/1” and “
ABC/rand/1” are proposed. ► A new search mechanism is got by introducing a selective probability
p. ► Both opposition-based learning method and chaotic maps are employed. ► The experiment results demonstrate the good performance of the IABC algorithm.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.ipl.2011.06.002</doi><tpages>12</tpages></addata></record> |
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| subjects | Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Artificial bee colony algorithm Calculus of variations and optimal control Colonies Computer science control theory systems Convergence Evolution Exact sciences and technology Initial population Learning Mathematical analysis Mathematical models Mathematics Miscellaneous Numerical analysis Numerical analysis. Scientific computation Numerical methods in mathematical programming, optimization and calculus of variations Numerical methods in optimization and calculus of variations Optimization Optimization algorithms Optimization techniques Probability distribution Randomized algorithms Sciences and techniques of general use Search mechanism Searching Solution search equation Studies Theoretical computing |
| title | Improved artificial bee colony algorithm for global optimization |
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