Volume 19, Issue 2 (International Journal of Engineering 2008)                   2008, 19(2): 99-112 | Back to browse issues page

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Bozorg Haddad O, Afshar A, Afshar H. Honey-Bee Mating Optimization (HBMO) Algorithm in Optimization Problems . Journal title 2008; 19 (2) :99-112
URL: http://ijiepm.iust.ac.ir/article-1-24-en.html
, a_afshar@iust.ac.ir
Abstract:   (18770 Views)

 Over the last decade, evolutionary and meta-heuristic algorithms have been extensively used as search and optimization tools in various problem domains, including science, commerce, and engineering. Ease of use and broad applicability may be considered as the primary reasons for their success. The honey-bee mating process has been considered as a typical swarm-based approach to optimization, in which the search algorithm is inspired by the process of real honey-bee mating. In this paper, the honey-bee mating optimization (HBMO) algorithm is applied to three well-known mathematical problems. To demonstrate the efficiency of the algorithm, these three problems are chosen from well-known constraint and unconstraint mathematical problems with continuous decision and state variables having all the complexities of optimization problems. The proposed HBMO algorithm results in a feasible, near-optimal solution in an acceptable number of mating flights. To emphasize the capability of the developed algorithm in handling these problems, results are compared with those of a well-developed genetic algorithm (GA). Although the HBMO algorithm is at the early stages of development the results obtained by the present algorithm are if not better but surly equally convenient to those employing genetic algorithms. A real-world water resources problem, in the field of water resources management is selected as a case study. The developed model is applied to a reservoir with 60 operational periods with the objective function of minimizing the root mean square error of release and demand. The results show that in this problem the algorithm arrives at the global optimum which is gained by non-linear programming.

  The results are promising and the algorithm arrives at the global optimum which is gained by non-linear programming.

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Type of Study: Research |
Received: 2009/04/25 | Published: 2008/06/15

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