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Decomposition-Based Multiobjective Optimization with Invasive Weed Colonies

Author

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  • Yanyan Tan
  • Xue Lu
  • Yan Liu
  • Qiang Wang
  • Huaxiang Zhang

Abstract

In order to solve the multiobjective optimization problems efficiently, this paper presents a hybrid multiobjective optimization algorithm which originates from invasive weed optimization (IWO) and multiobjective evolutionary algorithm based on decomposition (MOEA/D), a popular framework for multiobjective optimization. IWO is a simple but powerful numerical stochastic optimization method inspired from colonizing weeds; it is very robust and well adapted to changes in the environment. Based on the smart and distinct features of IWO and MOEA/D, we introduce multiobjective invasive weed optimization algorithm based on decomposition, abbreviated as MOEA/D-IWO, and try to combine their excellent features in this hybrid algorithm. The efficiency of the algorithm both in convergence speed and optimality of results are compared with MOEA/D and some other popular multiobjective optimization algorithms through a big set of experiments on benchmark functions. Experimental results show the competitive performance of MOEA/D-IWO in solving these complicated multiobjective optimization problems.

Suggested Citation

  • Yanyan Tan & Xue Lu & Yan Liu & Qiang Wang & Huaxiang Zhang, 2019. "Decomposition-Based Multiobjective Optimization with Invasive Weed Colonies," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-18, August.
    • Handle: RePEc:hin:jnlmpe:6943921
    DOI: 10.1155/2019/6943921
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    Cited by:

    1. Saumweber, Andrea & Wederhake, Lars & Cardoso, Gonçalo & Fridgen, Gilbert & Heleno, Miguel, 2021. "Designing Pareto optimal electricity retail rates when utility customers are prosumers," Energy Policy, Elsevier, vol. 156(C).

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