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{ "item_title" : "Dynamic Network Formation Using Ant Colony Optimization", "item_author" : [" Steven C. Oimoen "], "item_description" : "This research presents three contributions for solving highly dynamic (i.e. drastic change within the network) Multi-commodity Capacitated Network Design Problems (MCNDPs) resulting in a distributed multi-agent network design algorithm. The first contribution incorporates an Ant Colony Optimization (ACO) algorithm Ant Colony System (ACS) to solve the static MCNDP with weak constraints. Second, a new algorithm is developed and has the capability to dynamically adjust its exploration parameter of the solution space. This enhanced algorithm converges quickly and automatically adjusts to the dynamically changing network environment. Third, a distributed approach is created replacing the previous centralized solver.", "item_img_path" : "https://covers1.booksamillion.com/covers/bam/1/28/839/746/1288397461_b.jpg", "price_data" : { "retail_price" : "23.95", "online_price" : "23.95", "our_price" : "23.95", "club_price" : "23.95", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Dynamic Network Formation Using Ant Colony Optimization|Steven C. Oimoen

Dynamic Network Formation Using Ant Colony Optimization

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Overview

This research presents three contributions for solving highly dynamic (i.e. drastic change within the network) Multi-commodity Capacitated Network Design Problems (MCNDPs) resulting in a distributed multi-agent network design algorithm. The first contribution incorporates an Ant Colony Optimization (ACO) algorithm Ant Colony System (ACS) to solve the static MCNDP with weak constraints. Second, a new algorithm is developed and has the capability to dynamically adjust its exploration parameter of the solution space. This enhanced algorithm converges quickly and automatically adjusts to the dynamically changing network environment. Third, a distributed approach is created replacing the previous centralized solver.

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Details

  • ISBN-13: 9781288397464
  • ISBN-10: 1288397461
  • Publisher: Biblioscholar
  • Publish Date: December 2012
  • Dimensions: 9.21 x 6.14 x 0.66 inches
  • Shipping Weight: 0.98 pounds
  • Page Count: 316

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