Many-Objective Optimization


Evolutionary algorithms are particularly suited to solve multi-objective optimization problems because they can find a set of Pareto optimal solutions in a single run of the algorithm. So far, multi-objective EAs have been successfully applied mostly in two and three objectives problems. However, multi-objective EAs face several difficulties when we try to solve many-objective optimization problems, which optimize four or more objective functions simultaneously. At least, the following difficulties have been recognized in recent researches of evolutionary many-objective optimization.

  1. The convergence deterioration of solutions toward Pareto front
  2. The approximation of high dimensional entire Pareto front with a limited number of solutions in the population
  3. The presentation of obtained solutions in the high dimensional objective space and the decision making of a single final solution from them
  4. The search performance evaluation of search algorithms

This special session focus on evolutionary many-objective optimization to tackle problems in many-objective optimization including the above mentioned difficulties.


  • Algorithm design
  • Preference based search
  • Dimensionality reduction of the objective space
  • Benchmark problems
  • Visualization of high dimensional space and decision making
  • Search performance metrics

Paper Submission

Special session papers are treated the same as regular papers and must be submitted via the CEC 2015 submission website. When submitting choose the "Many-Objective Optimization" special session from the "Main Research Topic" list.


Hiroyuki Sato

E-mail: sato [at] hc.uec.ac.jp

Affiliation: The University of Electro-Communications

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