Journal of Northeastern University Natural Science ›› 2016, Vol. 37 ›› Issue (3): 314-318.DOI: 10.12068/j.issn.1005-3026.2016.03.003

• Information & Control • Previous Articles     Next Articles

Species-Based Genetic Algorithm for Multiobjective Optimization Problems

FU Ya-ping1,2, WANG Hong-feng1,2, HUANG Min1,2   

  1. 1. School of Information Science & Engineering, Northeastern University, Shenyang 110819, China; 2. State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China.
  • Received:2015-01-05 Revised:2015-01-05 Online:2016-03-15 Published:2016-03-07
  • Contact: WANG Hong-feng
  • About author:-
  • Supported by:
    -

Abstract: In order to achieve a set of nondominated solutions for multiobjective optimization problems quickly and accurately, a Species-based genetic algorithm for multiobjecitve optimization problems was proposed. Firstly, a certain number of subproblems were developed with the Tchebycheff approach. Then multiple subpopulations were constructed based on the Species mechanism to solve all the subproblems simultaneously, which can improve the exploration and exploitation ability by using multiple individuals to search one optimal solution. Finally, a set of benchmark multiobjective functions were examined, and the experimental results showed that the proposed algorithm can obtain a certain number of nondominated solutions quickly and accurately.

Key words: multiobjective optimization problem, genetic algorithm, multiobjective optimization algorithm, Species mechanism, Tchebycheff approach

CLC Number: