• DocumentCode
    3575397
  • Title

    Competition over Resources: A New Optimization Algorithm Based on Animals Behavioral Ecology

  • Author

    Mohseni, Sina ; Gholami, Reza ; Zarei, Niloofar ; Zadeh, Arash Roomi

  • Author_Institution
    Fac. of Electr. Eng., Noshirvani Univ. of Technol., Babol, Iran
  • fYear
    2014
  • Firstpage
    311
  • Lastpage
    315
  • Abstract
    In the recent years, many heuristic optimization algorithms have been developed. A majority of these heuristic algorithms have been derived from the behavior of biological or physical systems in nature. In this paper, we propose a new optimization algorithm based on competitive behavior of animal groups. In the proposed algorithm, the whole population is divided into a number of groups. In each group, the best searching agent spreads its children in its owned territory. Any group which is not able to find rich resources will be eliminated form competition. The competition gradually results in an increase in population of wealthy group which gives a fast convergence to proposed optimization algorithm. In the following, after a detailed explanation of the algorithm and pseudo code, we compare it to other existing algorithms, including genetics and particle swarm optimizations. Applying the proposed algorithm on various benchmark cost functions, shows faster and superior results compared to other optimization algorithms.
  • Keywords
    behavioural sciences computing; ecology; optimisation; software agents; animal groups; animals behavioral ecology; competitive behavior; cost functions; heuristic algorithms; heuristic optimization algorithms; particle swarm optimizations; physical systems; pseudo code; searching agent; Algorithm design and analysis; Animals; Benchmark testing; Heuristic algorithms; Optimization; Sociology; Statistics; animals behavioral ecology; nature inspired algorithm; optimization algorithm;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Networking and Collaborative Systems (INCoS), 2014 International Conference on
  • Print_ISBN
    978-1-4799-6386-7
  • Type

    conf

  • DOI
    10.1109/INCoS.2014.55
  • Filename
    7057107