Novel insights for multi-objective optimisation in engineering using Normal Boundary Intersection and (Enhanced) Normalised Normal Constraint

Normal Boundary Intersection (NBI) and (Enhanced) Normalised Normal Constraint (E)NNC are attractive and popular approaches to generate an approximation of the Pareto set in nonlinear multi-objective optimisation problems. All three methods are based on similar ideas, but do not always yield identical results, which may confuse practitioners. Hence, the current paper provides theoretical insights in the conditions under which identical results are obtained. Typically, NBI and ENNC are able to generate the same candidate Pareto points, if all additional inequalities in the ENNC subproblem are active. In general, NBI and NNC do not return the same points when three or more objectives are considered. Equivalence relations between the resulting lagrange multipliers for the additional NBI and ENNC (in)equality constraints have been derived. Moreover, the obtained relations have lead to novel criteria for detecting non-Pareto optimal points that in adverse situations maybe generated by these methods. The major advantage is that the removal criteria do not rely on a time-consuming pairwise comparison but only need matrix multiplications. A Matlab implementation has been added for completeness. The insights are illustrated for a general nonlinear bi-objective and three-objective optimisation problem, and a dynamic three-objective tubular reactor optimisation problem from chemical engineering. Finally, practical guidelines are added.

[1]  Kaisa Miettinen,et al.  Nonlinear multiobjective optimization , 1998, International series in operations research and management science.

[2]  Filip Logist,et al.  Derivation of generic optimal reference temperature profiles for steady-state exothermic jacketed tubular reactors , 2008 .

[3]  Christian Kirches,et al.  Efficient multiple objective optimal control of dynamic systems with integer controls , 2010 .

[4]  J. Dennis,et al.  A closer look at drawbacks of minimizing weighted sums of objectives for Pareto set generation in multicriteria optimization problems , 1997 .

[5]  A. Messac,et al.  Normal Constraint Method with Guarantee of Even Representation of Complete Pareto Frontier , 2004 .

[6]  J. V. Salcedo,et al.  A new perspective on multiobjective optimization by enhanced normalized normal constraint method , 2008 .

[7]  Moritz Diehl,et al.  ACADO toolkit—An open‐source framework for automatic control and dynamic optimization , 2011 .

[8]  John E. Dennis,et al.  Normal-Boundary Intersection: A New Method for Generating the Pareto Surface in Nonlinear Multicriteria Optimization Problems , 1998, SIAM J. Optim..

[9]  A. Messac,et al.  The normalized normal constraint method for generating the Pareto frontier , 2003 .

[10]  I. Y. Kim,et al.  Adaptive weighted-sum method for bi-objective optimization: Pareto front generation , 2005 .

[11]  Jasbir S. Arora,et al.  Survey of multi-objective optimization methods for engineering , 2004 .

[12]  Filip Logist,et al.  Fast Pareto set generation for nonlinear optimal control problems with multiple objectives , 2010 .