Title †
Many-Objective Optimization
Motivation †
Evolutionary algorithms are particularly suited to solve multi-objective optimization problems since they can obtain a set of non-dominated solutions to approximate Pareto front 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.
- The convergence deterioration of solutions toward Pareto front
- The approximation of high dimensional entire Pareto front with a limited number of solutions in the population
- The presentation of obtained solutions in the high dimensional objective space and the decision making of a single final solution from them
- 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.
Scope †
- 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 WCCI 2016 submission website. When submitting choose the "Many-Objective Optimization" special session from the "Main Research Topic" list.
Organizers †
- Hiroyuki Sato, The University of Electro-Communications, Japan
- Antonio López Jaimes, Metropolitan Autonomous University (Cujimalpa Campus), Mexico
Contacts
E-mail: sato [at] hc.uec.ac.jp, alopez [at] correo.cua.uam.mx
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