Local dominance and controlling dominance area of solutions in multi and many objectives EAs

ABSTRACT

This work presents local dominance with alignment of principle search direction and control of dominance area of solutions to enhance selection of MOEAs, aiming to improve their performance on multi and many objectives combinatorial problems. We show that the methods used independently can substantially improve either diversity or convergence. Also, by including control of dominance area of solutions within the local dominance algorithm, we show that diversity and convergence can improve simultaneously while reducing the computational cost of the algorithm.

REFERENCES

• 1: Kalyanmoy Deb , Deb Kalyanmoy, Multi-Objective Optimization Using Evolutionary Algorithms, John Wiley & Sons, Inc., New York, NY, 2001
• 2: Carlos A. Coello Coello , Gary B. Lamont , David A. Van Veldhuizen, Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation), Springer-Verlag New York, Inc., Secaucus, NJ, 2006
• 3: H. Sato, H. Aguirre, and K. Tanaka, "Local Dominance and Local Recombination in MOEAs on 0/1 Multiple Knapsack Problems", European Journal of Operational Research, Vol. 181, No. 3, pp. 1708--1723, 2007.
• 4: H. Sato, H. Aguirre, and K. Tanaka, "Controlling Dominance Area of Solutions and its Impact on the Performance of MOEAs", Proc. Fourth Intl. Conf. on Evolutionary Multi-Criterion Optimization, Springer, LNCS, Vol. 4403, pp. 5--20, 2007.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.8 Problem Solving, Control Methods, and Search
          Subjects: Heuristic methods

Additional Classification:
  G. Mathematics of Computing
  G.1 NUMERICAL ANALYSIS
      G.1.6 Optimization

General Terms:
Algorithms, Design, Performance

Keywords:
control of dominance area of solutions, evolutionary multi and many objectives optimization, local dominance, selection