An new evolutionary multi-objective optimization algorithm

@article{Shengjing2003AnNE,
  title={An new evolutionary multi-objective optimization algorithm},
  author={Mu Sheng-jing and Su Hong-ye and Chu Jian and Wang Yue-xuan},
  journal={The 2003 Congress on Evolutionary Computation, 2003. CEC '03.},
  year={2003},
  volume={2},
  pages={914-920 Vol.2}
}
We introduce a new, simple and efficient evolutionary algorithm to multiobjective optimization problem, which based on neighborhood and archived operation (NAGA). The innovations contain two main parts: neighborhood identify procedure to obtain Pareto optimal solutions from the population and neighborhood crowding procedure to maintain the diversity of Pareto optimal solutions previously found. The neighborhood identify procedure is composed of two steps, first to identify the locally… CONTINUE READING

Citations

Publications citing this paper.
SHOWING 1-2 OF 2 CITATIONS

References

Publications referenced by this paper.
SHOWING 1-10 OF 11 REFERENCES

Multiple Objective Optimization with Vector Evaluated Genetic Algorithms

  • J. D. Schaffer
  • Genetic Algorithms and their Applications. Proceedings of the First International Conference on Genetic Algorithms, pages 93-100, Lawrence Erlbaum.
  • 1985
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

A new Genetic Algorithm to handle the constrained optimization problem

  • S. Mu, H Su
  • Proceedings of the 41st IEEE Conference of Decision and Control,
  • 2002

Scalable multi-objective optimization test problems

Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach

VIEW 2 EXCERPTS

An Overview of Evolutionary Algorithms in Multiobjective Optimization

  • C. M. Fonseca
  • Technical report, Department of Automatic Control and Systems Engineering,
  • 1994