MSOPS-II: a general-purpose many-objective optimiser. Existing evolutionary methods capable of true many-objective optimisation have been limited in their application: for example either initial search directions need to be specified a-priori, or the use of hypervolume limits the search in practice to less than 10 objective dimensions. This paper describes two extensions to the multiple single objective pareto sampling (MSOPS) algorithm. The first provides automatic target vector generation, removing the requirement for initial a-priori designer intervention; and secondly redefines the fitness assignment method to simplify analysis and allow more comprehensive constraint handling. The significant enhancements allow the new MSOPS-II ranking process to be used as part of a general-purpose multi/many objective optimisation algorithm, requiring minimal initial configuration.

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  1. Ye Tian, Ran Cheng, Xingyi Zhang, Yaochu Jin: PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization (2017) arXiv
  2. Wang, Rui; Purshouse, Robin C.; Fleming, Peter J.: Preference-inspired co-evolutionary algorithms using weight vectors (2015)
  3. Denysiuk, Roman; Costa, Lino; Santo, Isabel Espírito: Generalized multiobjective evolutionary algorithm guided by descent directions (2014)
  4. Giagkiozis, I.; Purshouse, R.C.; Fleming, P.J.: Generalized decomposition and cross entropy methods for many-objective optimization (2014)
  5. López Jaimes, Antonio; Coello Coello, Carlos A.; Aguirre, Hernán; Tanaka, Kiyoshi: Objective space partitioning using conflict information for solving many-objective problems (2014)
  6. Shang, Ronghua; Wang, Yuying; Wang, Jia; Jiao, Licheng; Wang, Shuo; Qi, Liping: A multi-population cooperative coevolutionary algorithm for multi-objective capacitated arc routing problem (2014)
  7. von Lücken, Christian; Barán, Benjamín; Brizuela, Carlos: A survey on multi-objective evolutionary algorithms for many-objective problems (2014)
  8. Guo, Xiaofang; Wang, Yuping; Wang, Xiaoli: Using objective clustering for solving many-objective optimization problems (2013)
  9. Tan, Yan-yan; Jiao, Yong-chang; Li, Hong; Wang, Xin-kuan: MOEA/D + uniform design: a new version of MOEA/D for optimization problems with many objectives (2013)
  10. Weise, Thomas; Chiong, Raymond; Tang, Ke: Evolutionary optimization: pitfalls and booby traps (2012)