An Extension of the MOON 2 /MOON 2R Approach to Many-Objective Optimization Problems

A multi-objective optimization (MUOP) method that supports agile and flexible decision making to be able to handle complex and diverse decision environments has been in high demand. This study proposes a general idea for solving many-objective optimization (MAOP) problems by using the MOON2 or MOON2R method. These MUOP methods rely on prior articulation in trade-off analysis among conflicting objectives. Despite requiring only simple and relative responses, the decision maker’s trade-off analysis becomes rather difficult in the case of MAOP problems, in which the number of objective functions to be considered is larger than in MUOP. To overcome this difficulty, we present a stepwise procedure that is extensively used in the analytic hierarchy process. After that, the effectiveness of the proposed method is verified by applying it to an actual problem. Finally, a general discussion is presented to outline the direction of future work in this area.

[1]  Sergei Utyuzhnikov,et al.  Control of robust design in multiobjective optimization under uncertainties , 2012 .

[2]  Kalyanmoy Deb,et al.  A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II , 2000, PPSN.

[3]  Yoshiaki Shimizu,et al.  Prototype Development for Supporting Multiobjective Decision Making in an Ill-Posed Environment , 2010 .

[4]  Yoshiaki Shimizu,et al.  A Practical Method for Multi-Objective Scheduling through Soft Computing Approach , 2003 .

[5]  Yoshiaki Shimizu,et al.  Integrated Product Design through Multi-Objective Optimization Incorporated with Meta-Modeling Technique , 2008 .

[6]  Bogdan Filipic,et al.  DEMO: Differential Evolution for Multiobjective Optimization , 2005, EMO.

[7]  Yoshiaki Shimizu,et al.  A Design Support Through Multi-Objective Optimization Aware of Subjectivity of Value System , 2006 .

[8]  T. Saaty,et al.  The Analytic Hierarchy Process , 1985 .

[9]  Yoshiaki Shimizu,et al.  Multi-Objective Optimization in Terms of Soft Computing , 2002 .

[10]  P. John Clarkson,et al.  A Multi-objective Tabu Search Algorithm for Constrained Optimisation Problems , 2005, EMO.

[11]  Carlos A. Coello Coello,et al.  A Short Tutorial on Evolutionary Multiobjective Optimization , 2001, EMO.

[12]  Tatsuhiko Sakaguchi,et al.  Multi-Objective Sequencing Optimization for Mixed-Model Assembly Line Considering Due-Date Satisfaction , 2012 .

[13]  Evan J. Hughes,et al.  Evolutionary many-objective optimisation: many once or one many? , 2005, 2005 IEEE Congress on Evolutionary Computation.

[14]  Peter C. Fishburn,et al.  Utility theory for decision making , 1970 .

[15]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[16]  Hisao Ishibuchi,et al.  Evolutionary many-objective optimization , 2008, 2008 3rd International Workshop on Genetic and Evolving Systems.

[17]  Yasutsugu Tanaka,et al.  Multi-objective optimization system, MOON2 on the Internet , 2004, Comput. Chem. Eng..

[18]  Piotr Czyzżak,et al.  Pareto simulated annealing—a metaheuristic technique for multiple‐objective combinatorial optimization , 1998 .

[19]  Yoshiaki Shimizu,et al.  A Progressive Approach for Multi-objective Design through Inter-related Modeling of Value System and Meta-model , 2005 .