Multiobjective optimization approach

Morales-Valdez P, Flores-Tlacuahuac A, Zavala VM. Analyzing the effects of comfort relaxation on energy demand flexibility of buildings a multiobjective optimization approach. Energy Build 2014 85(0) 416-26. 

Martinez-Guido, S. L, Gonz ez-Campos, J. B., Del-Rfo, R. E., Ponce-Ortega, J. M., Nipoles-Rivera, F., Sema-Gonzdlez, M., El-Halwagi, M. M. A multiobjective optimization approach for the development of a sustainable supply chain of a new fixative in the perfumery industry. ACS Sustainable Chemistry Engineering. 2 (10), 2380-2390, 2014. 

Several groups have approached multiobjective library design by combining individual objectives into a single combined fitness function. This is a widely used approach to multiojective optimization and effectively reduces a multiobjective optimization problem to one of optimizing a single objective. 

There are basically two approaches for solving a multiobjective optimization problem. The first is to attempt to find the optimal or preferred solution directly, and the second is to generate the so-called non-inferior solutions (set) and then locate the preferred solution among them. The latter approach is widely adopted and will be used in this work. 

Since most of this book is devoted to evolutionary methods for multiobjective optimization, we here only wish to discuss some differences between EMO approaches and scalarization based approaches. As mentioned before, EMO approaches are a posteriori type of methods and they try to generate an approximation of the Pareto optimal set. In bi-objective optimization problems, it is easy to plot the objective vectors produced on a plane and ask the DM to select the most preferred one. While looking at the... 

Branke, J., Deb, K., Miettinen, K. and Slowinski, R. (eds.) (2008). Multiobjective Optimization Interactive and Evolutionary Approaches, Lecture Notes in Computer Science, State-of-the-Art Survey, Vol. 4907 (Springer-Verlag), to appear. 

Hamalainen, J., Miettinen, K., Tarvainen, P. and Toivanen, J. (2003). Interactive solution approach to a multiobjective optimization problem in paper machine headbox design. Journal of Optimization Theory and Applications 116, 2, pp. 265-281. 

Miettinen, K. and Makela, M. (2006). Synchronous approach in interactive multiobjective optimization, European Journal of Operations Research 170, 3, pp. 909-922. 

The remainder of the paper is organized as follows Section 2 contains an overview of the Performance functions. Section 3 presents a synthesis of multiobjective optimization while Section 4 shows the proposed approach on different systems. Finally, Section 5 presents the conclusions. 

At this point we have defined the superstructure of interest, developed three performance criteria for the optimization of the superstructure and provided some fundamental insight into the impact of design variables on the responsiveness criterion. In this section the approach to solve the multiobjective optimization problem is presented together with the reference design case to be compared with the optimized designs. 

A multiobjective optimization problem is formulated for the MTBE RD column with respect to economic performance and exergy efficiency. The formulation includes the balance equations 8.1-8.5 and 8.29, the criteria definitions 8.42 and the optimization formulation 8.62. Constraints imposed by operating conditions and product specifications are included. For the sake of controlled built-up of optimization complexity the first approximation of this approach omits the response time constant as objective function. 

Answer. Improved residue curve mapping technique, multilevel modeling approach, dynamic optimization of spatial and control structures, steady-state and dynamic behavior analysis, generic lumped reactive distillation volume element, multiobjective optimization criteria. 

The application of the Pareto concept, in search of the solution of the multiobjective optimization problem, allows to evaluate the optimal choice of the DV that represents a compromise solution which guarantees an acceptable level of relative displacement. An Evolutionary approach by means of a Genetic Algorithm has been used to solve the MOOP and search the population of non-inferior parallel solutions. Illustrated numerical examples show that all assessments and... 

Branke, J., Deb, K., Miettinen, K., Stowinski, R. 2002>).Multiobjective optimization interactive and evolutionary approaches. Springer. 

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