A micro niche evolutionary algorithm with lower-dimensional-search crossover for optimisation problems with constraints

  • Authors:

  • Sanyou Zeng;Yang Yang;Yulong Shi;Xianqiang Yang;Bo Xiao;Song Gao;Danping Yu;Zu Yan

  • Affiliations:

  • School of Computer Science, Research Centre for Space Science and Technology, The State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan, 430074 ...;School of Computer Science, Research Centre for Space Science and Technology, The State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan, 430074 ...;School of Computer Science, Research Centre for Space Science and Technology, The State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan, 430074 ...;School of Computer Science, Research Centre for Space Science and Technology, The State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan, 430074 ...;School of Computer Science, Research Centre for Space Science and Technology, The State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan, 430074 ...;School of Computer Science, Research Centre for Space Science and Technology, The State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan, 430074 ...;School of Computer Science, Research Centre for Space Science and Technology, The State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan, 430074 ...;School of Computer Science, Research Centre for Space Science and Technology, The State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan, 430074 ...

  • Venue:
  • International Journal of Bio-Inspired Computation
  • Year:
  • 2009

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Abstract

This paper proposes a micro niche evolutionary algorithm (MNEA) with lower-dimensional-search crossover for optimisation problems with constraints. The best individual in each niche is picked out and all those picked individuals compose the breeding pool of the evolutionary algorithm. Crossover operator of the algorithm searches a lower dimensional space which is determined by the parent points. Both the niche technique and the crossover technique are favourable to enhance the performance of the algorithm. The new algorithm has been tested by the 24 constrained benchmark problems and the results show that it works better than or competitive to any known effective algorithm. Notably, using this new algorithm to solve a well-known engineering problem (pressure vessel problem), its result is much better than that of any other known algorithm.