Non-dominated sorting genetic algorithm with decomposition to solve constrained optimisation problems

  • Authors:

  • Sanyou Zeng;Dong Zhou;Hui Li

  • Affiliations:

  • School of Computer Science, China University of GeoSciences, 430074 Wuhan, Hubei, China;School of Computer Science, China University of GeoSciences, 430074 Wuhan, Hubei, China;School of Computer Science, China University of GeoSciences, 430074 Wuhan, Hubei, China

  • Venue:
  • International Journal of Bio-Inspired Computation
  • Year:
  • 2013

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Abstract

Pareto-domination was adopted to handle not only trade-off between objective and constraints but also trade-off between convergence and diversity on solving a constrained optimisation problem COP in this paper like many other researchers. But there are some differences. This paper converts a COP into an equivalent dynamic constrained multi-objective optimisation problem DCMOP first, then dynamic version of non-dominated sorting genetic algorithm with decomposition NSGA/D is designed to solve the equivalent DCMOP, consequently solve the COP. A key issue for the NSGA/D working effectively is that the environmental change should not destroy the feasibility of the population. With a feasible population, the NSGA/D could solve well the DCMOP just as a MOEA usually can solve well an unconstrained MOP. Experimental results show that the NSGA/D outperforms or performs similarly to other state-of-the-art algorithms referred to in this paper, especially in global search.