Multiobjective Optimization Software

Abstract

Abstract. This chapter provides a description of multiobjective optimization software with a general overview of selected few available tools developed in the last decade. This chapter can be considered a revision of previous valid papers and chapters on nonlinear multiobjective optimization software such as the ones written by Weistroffer et al. (2005) and Miettinen (1999) that lists existing software packages up to the year 1999. More precisely, this chapter is focused on the tools and features that advisable multiobjective optimization software should contain.

Coupling dynamic simulation and interactive multiobjective optimization for complex problems: An APROS-NIMBUS case study

“…[47,55,67] and references therein), but t not that many implementations of them exist (see e.g. [75,101]). This can be considered to be somewhat surprising, as these interactive methods have been successfully used to solve multiobjective optimization problems for various topics, including reservoir management [3], operating a wastewater treatment plant [35,36], the construction of bridges [69] and analyzing air pollution [92], among others.…”

Optimal preliminary design of electromechanical actuators

“…in the MS Excels environment) (Task 5, section 3.6). The generation of a meta-model follows the process shown Figure 11 and is performed here with the Optimus software [45]. The meta-models representing multi-body simulation link the effect of actuator geometric integration (anchorage x a , y a and transmission x t , y t points parameters, Figure 12) and the displacement curve form (α, β displacement curve parameters) to useful sizing criteria as stroke, maximum speed, maximum load, RMS and RMC load.…”

A software framework based on a conceptual unified model for evolutionary multiobjective optimization: ParadisEO-MOEO

“…Table 2 gives a non-exhaustive comparison between a number of existing software frameworks for EMO, including jMetal (Durillo et al, 2006), the MOEA toolbox for Matlab (Tan et al, 2001), MOMHLib++ (http://home.gna.org/momh/), PISA (Bleuler et al, 2003) and Shark (Igel et al, 2008). Note that other software packages exist for multiobjective optimization (Poles et al, 2008), but some cannot be considered as frameworks and others do not deal with EMO. The frameworks presented in Table 2 are distinguished according to the following criteria: the kind of MOPs they are able to tackle (continuous and/or combinatorial problems), the availability of statistical tools (including performance metrics), the availability of hybridization or parallel features, the framework type (black-box or white-box), the programming language and the license type (free or commercial).…”