On the Convergence of Chemical Reaction Optimization for Combinatorial Optimization
A novel general-purpose optimization method, chemical reaction optimization (CRO), is a population-based metaheuristic inspired by the phenomenon of interactions between molecules in a chemical reaction process. CRO has demonstrated its competitive edge over existing methods in solving many real-wor...
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| Veröffentlicht in: | IEEE transactions on evolutionary computation 2013-10, Vol.17 (5), p.605-620 |
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| container_title | IEEE transactions on evolutionary computation |
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| creator | Lam, Albert Y. S. Li, Victor O. K. Jin Xu |
| description | A novel general-purpose optimization method, chemical reaction optimization (CRO), is a population-based metaheuristic inspired by the phenomenon of interactions between molecules in a chemical reaction process. CRO has demonstrated its competitive edge over existing methods in solving many real-world problems. However, all studies concerning CRO have been empirical in nature and no theoretical analysis has been conducted to study its convergence properties. In this paper, we present some convergence results for several generic versions of CRO, each of which adopts different combinations of elementary reactions. We investigate the limiting behavior of CRO. By modeling CRO as a finite absorbing Markov chain, we show that CRO converges to a global optimum solution with a probability arbitrarily close to one when time tends to infinity. Our results also show that the convergence of CRO is determined by both the elementary reactions and the total energy of the system. Moreover, we also study and discuss the finite time behavior of CRO. |
| doi_str_mv | 10.1109/TEVC.2012.2227973 |
| format | Article |
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Our results also show that the convergence of CRO is determined by both the elementary reactions and the total energy of the system. Moreover, we also study and discuss the finite time behavior of CRO.</description><identifier>ISSN: 1089-778X</identifier><identifier>EISSN: 1941-0026</identifier><identifier>DOI: 10.1109/TEVC.2012.2227973</identifier><identifier>CODEN: ITEVF5</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Algorithmics. Computability. Computer arithmetics ; Applied sciences ; Artificial intelligence ; Chemical reaction optimization (CRO) ; Chemicals ; Computer science; control theory; systems ; Convergence ; convergence rate ; Cost function ; Exact sciences and technology ; finite absorbing Markov chain ; first hitting time ; Flows in networks. Combinatorial problems ; Learning and adaptive systems ; Markov processes ; Operational research and scientific management ; Operational research. 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S.</creatorcontrib><creatorcontrib>Li, Victor O. K.</creatorcontrib><creatorcontrib>Jin Xu</creatorcontrib><title>On the Convergence of Chemical Reaction Optimization for Combinatorial Optimization</title><title>IEEE transactions on evolutionary computation</title><addtitle>TEVC</addtitle><description>A novel general-purpose optimization method, chemical reaction optimization (CRO), is a population-based metaheuristic inspired by the phenomenon of interactions between molecules in a chemical reaction process. CRO has demonstrated its competitive edge over existing methods in solving many real-world problems. However, all studies concerning CRO have been empirical in nature and no theoretical analysis has been conducted to study its convergence properties. In this paper, we present some convergence results for several generic versions of CRO, each of which adopts different combinations of elementary reactions. We investigate the limiting behavior of CRO. By modeling CRO as a finite absorbing Markov chain, we show that CRO converges to a global optimum solution with a probability arbitrarily close to one when time tends to infinity. Our results also show that the convergence of CRO is determined by both the elementary reactions and the total energy of the system. Moreover, we also study and discuss the finite time behavior of CRO.</description><subject>Algorithmics. Computability. Computer arithmetics</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Chemical reaction optimization (CRO)</subject><subject>Chemicals</subject><subject>Computer science; control theory; systems</subject><subject>Convergence</subject><subject>convergence rate</subject><subject>Cost function</subject><subject>Exact sciences and technology</subject><subject>finite absorbing Markov chain</subject><subject>first hitting time</subject><subject>Flows in networks. Combinatorial problems</subject><subject>Learning and adaptive systems</subject><subject>Markov processes</subject><subject>Operational research and scientific management</subject><subject>Operational research. Management science</subject><subject>Sociology</subject><subject>Statistics</subject><subject>Theoretical computing</subject><issn>1089-778X</issn><issn>1941-0026</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpNkE1Lw0AQhhdRsFZ_gHjJxWPqfn8cJdQqFApaxVvYbGbtSpOU3SDorzc1pXiaGeZ9BuZB6JrgGSHY3K3nb8WMYkJnlFJlFDtBE2I4yTGm8nTosTa5Uvr9HF2k9Ikx4YKYCXpZtVm_gazo2i-IH9A6yDqfFRtogrPb7Bms60PXZqtdH5rwY_8G38WBaKrQ2r6LYcj9X1-iM2-3Ca4OdYpeH-br4jFfrhZPxf0yd9SIPhfCcKMZ0VZiSoEpzmtTKcws8aIGYpSsZcW945IpZbVnoLzGNZNeWQDLpoiMd13sUorgy10MjY3fJcHl3kq5t1LurZQHKwNzOzI7m4b_fLStC-kIUqWModoMuZsxFwDguJZMCMk1-wWDdGvg</recordid><startdate>20131001</startdate><enddate>20131001</enddate><creator>Lam, Albert Y. S.</creator><creator>Li, Victor O. 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Computer arithmetics</topic><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Chemical reaction optimization (CRO)</topic><topic>Chemicals</topic><topic>Computer science; control theory; systems</topic><topic>Convergence</topic><topic>convergence rate</topic><topic>Cost function</topic><topic>Exact sciences and technology</topic><topic>finite absorbing Markov chain</topic><topic>first hitting time</topic><topic>Flows in networks. Combinatorial problems</topic><topic>Learning and adaptive systems</topic><topic>Markov processes</topic><topic>Operational research and scientific management</topic><topic>Operational research. Management science</topic><topic>Sociology</topic><topic>Statistics</topic><topic>Theoretical computing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lam, Albert Y. S.</creatorcontrib><creatorcontrib>Li, Victor O. K.</creatorcontrib><creatorcontrib>Jin Xu</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><jtitle>IEEE transactions on evolutionary computation</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Lam, Albert Y. S.</au><au>Li, Victor O. K.</au><au>Jin Xu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>On the Convergence of Chemical Reaction Optimization for Combinatorial Optimization</atitle><jtitle>IEEE transactions on evolutionary computation</jtitle><stitle>TEVC</stitle><date>2013-10-01</date><risdate>2013</risdate><volume>17</volume><issue>5</issue><spage>605</spage><epage>620</epage><pages>605-620</pages><issn>1089-778X</issn><eissn>1941-0026</eissn><coden>ITEVF5</coden><abstract>A novel general-purpose optimization method, chemical reaction optimization (CRO), is a population-based metaheuristic inspired by the phenomenon of interactions between molecules in a chemical reaction process. CRO has demonstrated its competitive edge over existing methods in solving many real-world problems. However, all studies concerning CRO have been empirical in nature and no theoretical analysis has been conducted to study its convergence properties. In this paper, we present some convergence results for several generic versions of CRO, each of which adopts different combinations of elementary reactions. We investigate the limiting behavior of CRO. By modeling CRO as a finite absorbing Markov chain, we show that CRO converges to a global optimum solution with a probability arbitrarily close to one when time tends to infinity. Our results also show that the convergence of CRO is determined by both the elementary reactions and the total energy of the system. Moreover, we also study and discuss the finite time behavior of CRO.</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/TEVC.2012.2227973</doi><tpages>16</tpages></addata></record> |
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| subjects | Algorithmics. Computability. Computer arithmetics Applied sciences Artificial intelligence Chemical reaction optimization (CRO) Chemicals Computer science control theory systems Convergence convergence rate Cost function Exact sciences and technology finite absorbing Markov chain first hitting time Flows in networks. Combinatorial problems Learning and adaptive systems Markov processes Operational research and scientific management Operational research. Management science Sociology Statistics Theoretical computing |
| title | On the Convergence of Chemical Reaction Optimization for Combinatorial Optimization |
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