DOPGA: a new fitness assignment scheme for multi-objective evolutionary algorithms

ERGÜL E. U., Eminoğlu İ.

INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE, vol.45, no.3, pp.407-426, 2014 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 45 Issue: 3
  • Publication Date: 2014
  • Doi Number: 10.1080/00207721.2012.724095
  • Journal Name: INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.407-426
  • Keywords: fitness assignment, domination power, DOPGA, evolutionary algorithms, SPEA and SPEA2, test functions, NSGA-II, OPTIMIZATION
  • Ondokuz Mayıs University Affiliated: Yes

Abstract

In this article, a new fitness assignment scheme to evaluate the Pareto-optimal solutions for multi-objective evolutionary algorithms is proposed. The proposed DOmination Power of an individual Genetic Algorithm (DOPGA) method can order the individuals in a form in which each individual (the so-called solution) could have a unique rank. With this new method, a multi-objective problem can be treated as if it were a single-objective problem without drastically deviating from the Pareto definition. In DOPGA, relative position of a solution is embedded into the fitness assignment procedures. We compare the performance of the algorithm with two benchmark evolutionary algorithms (Strength Pareto Evolutionary Algorithm (SPEA) and Strength Pareto Evolutionary Algorithm 2 (SPEA2)) on 12 unconstrained bi-objective and one tri-objective test problems. DOPGA significantly outperforms SPEA on all test problems. DOPGA performs better than SPEA2 in terms of convergence metric on all test problems. Also, Pareto-optimal solutions found by DOPGA spread better than SPEA2 on eight of 13 test problems.