Optimization of the ratio of two functions is called fractional programming or ratio optimization problem. If one can optimize simultaneously collection of fractional objective functions then the problem is called multi-objective fractional programming. This paper presents a survey on fractional programming problems. In contrast, this survey excludes many of the technical details and provides a...

Planning under uncertainty poses a complex problem in which multiple objectives often need to be balanced. When dealing with multiple objectives, it is often assumed that the relative importance of the objectives is known a priori. However, in practice human decision makers often find it hard to specify such preferences, and would prefer a decision support system that presents a range of possib...

In multiple criteria linear programming (MOLP) any efficient solution can be found by the weighting approach with some positive weights allocated to several criteria. The weights settings represent preferences model thus involving impreciseness and uncertainties. The resulting weighted average performance may be lower than expected. Several approaches have been developed to deal with uncertain ...

Two multi-objective genetic algorithms, an elitist version of MOGA and NSGA-II, were applied to solve two linear control design problems. The first was a H2 problem with a PI controller structure, for a first order stable plant. The second was a mixed H2/H4 control problem. In both cases, three indicators were used to evaluate each algorithm performance: Set coverage, spread and hypervolume. It...

Non-linear microscopy has the potential to provide clinically useful information on the structure of biological tissue in vivo via an endomicroscope. The ability to use plastic as the optical material in a multiphoton objective was evaluated based on several criteria including autofluorescence, injection molding induced birefringence, and pulse broadening due to group velocity dispersion. An al...

This paper compares a family of methods for characterizing neural feature selectivity with natural stimuli in the framework of the linear-nonlinear model. In this model, the neural firing rate is a nonlinear function of a small number of relevant stimulus components. The relevant stimulus dimensions can be found by maximizing one of the family of objective functions, Rényi divergences of differ...

This work develops an interactive fuzzy multi-objective linear programming (i-FMOLP) method for solving the fuzzy multiobjective transportation problems with piecewise linear membership function. The proposed i-FMOLP method aims to simultaneously minimize the total distribution costs and the total delivery time with reference to fuzzy available supply and total budget at each source, and fuzzy ...

This paper shows how a formal modeling framework, Evolutionary Systems Design (ESD), for evolutionary problem definition and solution, can be used for problem adaptation and restructuring (i.e., reengineering) in optimization problems, as developed for Multiobjective Linear Programming (MOLP). R es t ructuring through a heuristic controls/goals/values referral process and adaptation are discuss...

This paper discusses the bookkeeping strategies for solving large multiple objective linear programs (MOLPs) on ADBASE, a well developed sequential software package, and on a parallel ADBASE algorithm. Three representative list creation schemes were rst analyzed and tested. The best of them, Binary Search with Insertion Sort (BSIS), was selected to be incorporated into ADBASE and the parallel A...