Ant Colony Optimization
Ants communicate information by leaving pheromone tracks. A moving ant leaves, in varying quantities, some pheromone on the ground to mark its way. While an isolated ant moves essentially at random, an ant encountering a previously laid trail is able to detect it and decide with high probability to...
সংরক্ষণ করুন:
| বিন্যাস: | Online |
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| ভাষা: | ইংরেজি |
| প্রকাশিত: |
IntechOpen
2021
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| বিষয়গুলি: | |
| অনলাইন ব্যবহার করুন: | ONIX_20210420_9789533071572_280 |
| ট্যাগগুলো: |
কোনো ট্যাগ নেই, প্রথমজন হিসাবে ট্যাগ করুন!
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| _version_ | 1869521094832029696 |
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| collection | Directory of Open Access Books |
| description | Ants communicate information by leaving pheromone tracks. A moving ant leaves, in varying quantities, some pheromone on the ground to mark its way. While an isolated ant moves essentially at random, an ant encountering a previously laid trail is able to detect it and decide with high probability to follow it, thus reinforcing the track with its own pheromone. The collective behavior that emerges is thus a positive feedback: where the more the ants following a track, the more attractive that track becomes for being followed; thus the probability with which an ant chooses a path increases with the number of ants that previously chose the same path. This elementary ant's behavior inspired the development of ant colony optimization by Marco Dorigo in 1992, constructing a meta-heuristic stochastic combinatorial computational methodology belonging to a family of related meta-heuristic methods such as simulated annealing, Tabu search and genetic algorithms. This book covers in twenty chapters state of the art methods and applications of utilizing ant colony optimization algorithms. New methods and theory such as multi colony ant algorithm based upon a new pheromone arithmetic crossover and a repulsive operator, new findings on ant colony convergence, and a diversity of engineering and science applications from transportation, water resources, electrical and computer science disciplines are presented. |
| format | Online |
| id | doab-20.500.12854ir-64924 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | IntechOpen |
| publisherStr | IntechOpen |
| record_format | ojs |
| spelling | doab-20.500.12854ir-649242024-04-14T10:28:19Z Ant Colony Optimization Ostfeld, Avi Neural networks & fuzzy systems thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence Ants communicate information by leaving pheromone tracks. A moving ant leaves, in varying quantities, some pheromone on the ground to mark its way. While an isolated ant moves essentially at random, an ant encountering a previously laid trail is able to detect it and decide with high probability to follow it, thus reinforcing the track with its own pheromone. The collective behavior that emerges is thus a positive feedback: where the more the ants following a track, the more attractive that track becomes for being followed; thus the probability with which an ant chooses a path increases with the number of ants that previously chose the same path. This elementary ant's behavior inspired the development of ant colony optimization by Marco Dorigo in 1992, constructing a meta-heuristic stochastic combinatorial computational methodology belonging to a family of related meta-heuristic methods such as simulated annealing, Tabu search and genetic algorithms. This book covers in twenty chapters state of the art methods and applications of utilizing ant colony optimization algorithms. New methods and theory such as multi colony ant algorithm based upon a new pheromone arithmetic crossover and a repulsive operator, new findings on ant colony convergence, and a diversity of engineering and science applications from transportation, water resources, electrical and computer science disciplines are presented. 2021-04-20T15:05:07Z 2021-04-20T15:05:07Z 2011 book ONIX_20210420_9789533071572_280 9789533071572 9789535159803 https://directory.doabooks.org/handle/20.500.12854/64924 eng image/jpeg n/a https://www.intechopen.com/books https://mts.intechopen.com/storage/books/45/authors_book/authors_book.pdf IntechOpen IntechOpen 10.5772/577 10.5772/577 78a36484-2c0c-47cb-ad67-2b9f5cd4a8f6 9789533071572 9789535159803 IntechOpen 354 open access |
| spellingShingle | Neural networks & fuzzy systems thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence Ant Colony Optimization |
| title | Ant Colony Optimization |
| title_full | Ant Colony Optimization |
| title_fullStr | Ant Colony Optimization |
| title_full_unstemmed | Ant Colony Optimization |
| title_short | Ant Colony Optimization |
| title_sort | ant colony optimization |
| topic | Neural networks & fuzzy systems thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence |
| topic_facet | Neural networks & fuzzy systems thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence |
| url | ONIX_20210420_9789533071572_280 |