SRaDE: an adaptive differential evolution based on stochastic ranking

ABSTRACT

In this paper, we propose a methodology to improve the performance of the standard Differential Evolution (DE) in constraint optimization applications, in terms of accelerating its search speed, and improving the success rate. One critical mechanism embedded in the approach is applying Stochastic Ranking (SR) to rank the whole population of individuals with both objective value and constraint violation to be compared. The ranked population is then in a better shape to provide useful information e.g. direction to guide the search process. The strength of utilizing the directional information can be further controlled by a parameter - population partitioning factor, which is adjusted according to the evolution stage and generations. Because the adaptive adjustment of the parameter is predefined and does not need user input, the resulting algorithm is free of definition of this extra parameter and easier to implement. The performance of the proposed approach, which we call SRaDE (Stochastic Ranking based Adaptive Differential Evolution) is investigated and compared with standard DE. The experimental results show that SRDE significantly outperforms, or at least is comparable with standard DE in all the tested benchmark functions. We also conducted an experiment to compare SRaDE with SRDE - a variant of Stochastic Ranking based Differential Evolution without adaptive adjustment of the population partitioning factor. Experimental results show that SRaDE can also achieve improved performance over SRDE.

REFERENCES

• 1: Z. Fan, J. Liu, T, Sørensen, P, Wang. "Improved Differential Evolution Based on Stochastic Ranking for Robust Layout Synthesis of MEMS Components". IEEE Trans. On Industrial Electronics, Vol 56, issue 4, pp937--948.
• 2: Jinchao Liu , Zhun Fan , Erik Goodman, SRDE: an improved differential evolution based on stochastic ranking, Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation, June 12-14, 2009, Shanghai, China [doi>10.1145/1543834.1543881]
• 3: Runarsson T.P., Yao X., 2000. Stochastic Ranking for Constrained Evolutionary Computation. IEEE Trans. Evol. Comput. 4, 3 (Sept. 2000), 284--294
• 4: J. J. Liang, Thomas Philip Runarsson, Efren Mezura-Montes, Maurice Clerc, P. N. Suganthan, Carlos A. Coello Coello&K. Deb, "Problem Definitions and Evaluation Criteria for the CEC 2006 Special Session on Constrained Real-Parameter Optimization", Technical Report, Nanyang Technological University, Singapore, March 2006

INDEX TERMS

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

General Terms:
Algorithms

Keywords:
constrained optimization, differential evolution, stochastic ranking