Continuous Function Optimization Using Hybrid Ant Colony Approach with Orthogonal Design Scheme
A hybrid Orthogonal Scheme Ant Colony Optimization (OSACO) algorithm for continuous function optimization (CFO) is presented in this paper. The methodology integrates the advantages of Ant Colony Optimization (ACO) and Orthogonal Design Scheme (ODS). OSACO is based on the following principles: a) ea...
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| creator | Zhang, Jun Chen, Wei-neng Zhong, Jing-hui Tan, Xuan Li, Yun |
| description | A hybrid Orthogonal Scheme Ant Colony Optimization (OSACO) algorithm for continuous function optimization (CFO) is presented in this paper. The methodology integrates the advantages of Ant Colony Optimization (ACO) and Orthogonal Design Scheme (ODS). OSACO is based on the following principles: a) each independent variable space (IVS) of CFO is dispersed into a number of random and movable nodes; b) the carriers of pheromone of ACO are shifted to the nodes; c) solution path can be obtained by choosing one appropriate node from each IVS by ant; d) with the ODS, the best solved path is further improved. The proposed algorithm has been successfully applied to 10 benchmark test functions. The performance and a comparison with CACO and FEP have been studied. |
| doi_str_mv | 10.1007/11903697_17 |
| format | Book Chapter |
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The methodology integrates the advantages of Ant Colony Optimization (ACO) and Orthogonal Design Scheme (ODS). OSACO is based on the following principles: a) each independent variable space (IVS) of CFO is dispersed into a number of random and movable nodes; b) the carriers of pheromone of ACO are shifted to the nodes; c) solution path can be obtained by choosing one appropriate node from each IVS by ant; d) with the ODS, the best solved path is further improved. The proposed algorithm has been successfully applied to 10 benchmark test functions. 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The performance and a comparison with CACO and FEP have been studied.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Exact sciences and technology</subject><subject>Multimodal Function</subject><subject>Pheromone Information</subject><subject>Search Range</subject><subject>Solution Path</subject><subject>Unimodal Function</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>9783540473312</isbn><isbn>3540473319</isbn><isbn>3540473327</isbn><isbn>9783540473329</isbn><fulltext>true</fulltext><rsrctype>book_chapter</rsrctype><creationdate>2006</creationdate><recordtype>book_chapter</recordtype><recordid>eNpNUE1PwyAAxa_EOXfyD3Dx4KEKhUI5LtOpyZIdnGcCBVq0g6Z0MfPXW50mvsvL-8g7PACuMLrFCPE7jAUiTHCJ-RG4IAVFlBOS82MwwQzjjBAqTsBM8PIvw_kpmCCC8kxwSs7BLKU3NILkCFE6AXIRw-DDLu4SXO5CNfgY4Lob_NZ_qh_xmnyo4dNe997AeRjgIrYx7OG86_qoqgZ--KGB635oYh2DauG9Tb4O8KVq7NZegjOn2mRnvzwFm-XDZvGUrdaPz4v5Kuvygg0ZL7nWiFfOOmsc00ZbUhpTaGaEU8ZpbV2lUGkQs3b0lUVFIaxGtGSYaDIF14fZTqVKta5XofJJdr3fqn4vsRAiZ5iOvZtDL41RqG0vdYzvSWIkvw-W_w4mX5lGayE</recordid><startdate>2006</startdate><enddate>2006</enddate><creator>Zhang, Jun</creator><creator>Chen, Wei-neng</creator><creator>Zhong, Jing-hui</creator><creator>Tan, Xuan</creator><creator>Li, Yun</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>IQODW</scope></search><sort><creationdate>2006</creationdate><title>Continuous Function Optimization Using Hybrid Ant Colony Approach with Orthogonal Design Scheme</title><author>Zhang, Jun ; Chen, Wei-neng ; Zhong, Jing-hui ; Tan, Xuan ; Li, Yun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p256t-787bb07cfefedf6bdbe38dd5b6d9fadfbbefca08d06eedd5ae0559eb048613b3</frbrgroupid><rsrctype>book_chapters</rsrctype><prefilter>book_chapters</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Computer science; control theory; systems</topic><topic>Exact sciences and technology</topic><topic>Multimodal Function</topic><topic>Pheromone Information</topic><topic>Search Range</topic><topic>Solution Path</topic><topic>Unimodal Function</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Jun</creatorcontrib><creatorcontrib>Chen, Wei-neng</creatorcontrib><creatorcontrib>Zhong, Jing-hui</creatorcontrib><creatorcontrib>Tan, Xuan</creatorcontrib><creatorcontrib>Li, Yun</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Jun</au><au>Chen, Wei-neng</au><au>Zhong, Jing-hui</au><au>Tan, Xuan</au><au>Li, Yun</au><au>Iba, Hitoshi</au><au>Wang, Xufa</au><au>Chen, Guo-Liang</au><au>Li, Xiaodong</au><au>Chen, Shu-Heng</au><au>Wang, Tzai-Der</au><au>Yao, Xin</au><au>Abbass, Hussein</au><format>book</format><genre>bookitem</genre><ristype>CHAP</ristype><atitle>Continuous Function Optimization Using Hybrid Ant Colony Approach with Orthogonal Design Scheme</atitle><btitle>Lecture notes in computer science</btitle><seriestitle>Lecture Notes in Computer Science</seriestitle><date>2006</date><risdate>2006</risdate><spage>126</spage><epage>133</epage><pages>126-133</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>9783540473312</isbn><isbn>3540473319</isbn><eisbn>3540473327</eisbn><eisbn>9783540473329</eisbn><abstract>A hybrid Orthogonal Scheme Ant Colony Optimization (OSACO) algorithm for continuous function optimization (CFO) is presented in this paper. The methodology integrates the advantages of Ant Colony Optimization (ACO) and Orthogonal Design Scheme (ODS). OSACO is based on the following principles: a) each independent variable space (IVS) of CFO is dispersed into a number of random and movable nodes; b) the carriers of pheromone of ACO are shifted to the nodes; c) solution path can be obtained by choosing one appropriate node from each IVS by ant; d) with the ODS, the best solved path is further improved. The proposed algorithm has been successfully applied to 10 benchmark test functions. The performance and a comparison with CACO and FEP have been studied.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/11903697_17</doi><tpages>8</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Lecture notes in computer science, 2006, p.126-133 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_19992614 |
| source | Springer Books |
| subjects | Applied sciences Artificial intelligence Computer science control theory systems Exact sciences and technology Multimodal Function Pheromone Information Search Range Solution Path Unimodal Function |
| title | Continuous Function Optimization Using Hybrid Ant Colony Approach with Orthogonal Design Scheme |
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