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IPM
30
YEARS OLD

“School of Cognitive Sciences”

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Paper   IPM / Cognitive Sciences / 8791
School of Cognitive Sciences
  Title:   A novel numerical optimization algorithm inspired from weed colonization
  Author(s): 
1.  C. Lucas
2.  AR. Mehrabian
  Status:   Published
  Journal: Ecological Informatics
  Vol.:  1
  Year:  2006
  Pages:   355-366
  Supported by:  IPM
  Abstract:
This paper introduces a novel numerical stochastic optimization algorithm inspired from colonizing weeds. Weeds are plants whose vigorous, invasive habits of growth pose a serious threat to desirable, cultivated plants making them a threat for agriculture. Weeds have shown to be very robust and adaptive to change in environment. Thus, capturing their properties would lead to a powerful optimization algorithm. It is tried to mimic robustness, adaptation and randomness of colonizing weeds in a simple but effective optimizing algorithm designated as Invasive Weed Optimization (IWO). The feasibility, the efficiency and the effectiveness of IWO are tested in details through a set of benchmark multi-dimensional functions, of which global and local minima are known. The reported results are compared with other recent evolutionary-based algorithms: genetic algorithms, memetic algorithms, particle swarm optimization, and shuffled frog leaping. The results are also compared with different versions of simulated annealing - a generic probabilistic meta-algorithm for the global optimization problem - which are simplex simulated annealing, and direct search simulated annealing. Additionally, IWO is employed for finding a solution for an engineering problem, which is optimization and tuning of a robust controller. The experimental results suggest that results from IWO are better than results from other methods. In conclusion, the performance of IWO has a reasonable performance for all the test functions.

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