The Bees Algorithm for the Vehicle Routing Problem
In this thesis we present a new algorithm for the Vehicle Routing Problem called the Enhanced Bees Algorithm. It is adapted from a fairly recent algorithm, the Bees Algorithm, which was developed for continuous optimisation problems. We show that the results obtained by the Enhanced Bees Algorithm a...
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| creator | Fenton, Aish |
| description | In this thesis we present a new algorithm for the Vehicle Routing Problem
called the Enhanced Bees Algorithm. It is adapted from a fairly recent
algorithm, the Bees Algorithm, which was developed for continuous optimisation
problems. We show that the results obtained by the Enhanced Bees Algorithm are
competitive with the best meta-heuristics available for the Vehicle Routing
Problem (within 0.5% of the optimal solution for common benchmark problems). We
show that the algorithm has good runtime performance, producing results within
2% of the optimal solution within 60 seconds, making it suitable for use within
real world dispatch scenarios. |
| doi_str_mv | 10.48550/arxiv.1605.05448 |
| format | Article |
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called the Enhanced Bees Algorithm. It is adapted from a fairly recent
algorithm, the Bees Algorithm, which was developed for continuous optimisation
problems. We show that the results obtained by the Enhanced Bees Algorithm are
competitive with the best meta-heuristics available for the Vehicle Routing
Problem (within 0.5% of the optimal solution for common benchmark problems). We
show that the algorithm has good runtime performance, producing results within
2% of the optimal solution within 60 seconds, making it suitable for use within
real world dispatch scenarios.</description><identifier>DOI: 10.48550/arxiv.1605.05448</identifier><language>eng</language><subject>Computer Science - Artificial Intelligence ; Computer Science - Neural and Evolutionary Computing</subject><creationdate>2016-05</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/1605.05448$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.1605.05448$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Fenton, Aish</creatorcontrib><title>The Bees Algorithm for the Vehicle Routing Problem</title><description>In this thesis we present a new algorithm for the Vehicle Routing Problem
called the Enhanced Bees Algorithm. It is adapted from a fairly recent
algorithm, the Bees Algorithm, which was developed for continuous optimisation
problems. We show that the results obtained by the Enhanced Bees Algorithm are
competitive with the best meta-heuristics available for the Vehicle Routing
Problem (within 0.5% of the optimal solution for common benchmark problems). We
show that the algorithm has good runtime performance, producing results within
2% of the optimal solution within 60 seconds, making it suitable for use within
real world dispatch scenarios.</description><subject>Computer Science - Artificial Intelligence</subject><subject>Computer Science - Neural and Evolutionary Computing</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotzr1uwjAYhWEvDBVwAUz4BhL8GzsjRdBWQmqFItbos_OZWEpIZQJq774tdDrSOxw9hCw4y5XVmq0gfcVbzgumc6aVsk9EVC3SZ8QLXXenIcWx7WkYEh1_8xHb6Dukh-E6xvOJfqTBddjPyCRAd8H5_05JtdtWm9ds__7ytlnvMyiMzYRnAoKULnBvfQmN8pLzBoJvUDstDAoJSihZGGVK43hZNsEr1AZsYbWTU7J83N7R9WeKPaTv-g9f3_HyB3iTPks</recordid><startdate>20160518</startdate><enddate>20160518</enddate><creator>Fenton, Aish</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20160518</creationdate><title>The Bees Algorithm for the Vehicle Routing Problem</title><author>Fenton, Aish</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a678-2c02af33bf1c8c9ad4c311dafcde5b527e23a4243674797b199dfc4e57a8685b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Computer Science - Artificial Intelligence</topic><topic>Computer Science - Neural and Evolutionary Computing</topic><toplevel>online_resources</toplevel><creatorcontrib>Fenton, Aish</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Fenton, Aish</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The Bees Algorithm for the Vehicle Routing Problem</atitle><date>2016-05-18</date><risdate>2016</risdate><abstract>In this thesis we present a new algorithm for the Vehicle Routing Problem
called the Enhanced Bees Algorithm. It is adapted from a fairly recent
algorithm, the Bees Algorithm, which was developed for continuous optimisation
problems. We show that the results obtained by the Enhanced Bees Algorithm are
competitive with the best meta-heuristics available for the Vehicle Routing
Problem (within 0.5% of the optimal solution for common benchmark problems). We
show that the algorithm has good runtime performance, producing results within
2% of the optimal solution within 60 seconds, making it suitable for use within
real world dispatch scenarios.</abstract><doi>10.48550/arxiv.1605.05448</doi><oa>free_for_read</oa></addata></record> |
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| source | arXiv.org |
| subjects | Computer Science - Artificial Intelligence Computer Science - Neural and Evolutionary Computing |
| title | The Bees Algorithm for the Vehicle Routing Problem |
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