Stochastic ranking based differential evolution algorithm for constrained optimization problem

ABSTRACT

Based on differential evolution and stochastic ranking strategy, a new differential evolution algorithm for constrained optimization problem is proposed in this paper. The proposed algorithm reserves sub-optimal solutions in the process of population evolution, which effectively enhances the diversity of the population. The experiment results on 13 well-known benchmark problems show that the proposed algorithm is capable of improving the search performance significantly in convergent speed and precision with respect to four other algorithms such as Evolutionary Algorithm based on Homomorphous Maps (EAHM), Artificial Immune Response Constrained Evolutionary Strategy (AIRCES), Constraint Handling Differential Evolution (CHDE), and Evolutionary Strategies based on Stochastic Ranking (ESSR).

REFERENCES

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INDEX TERMS

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

General Terms:
Algorithms, Experimentation

Keywords:
constrained optimization, differential evolution algorithm, penalty function, stochastic ranking.