Search Results for "muti-objective optimization"

Optimization.jl provides the easiest way to create an optimization problem and solve it. It enables rapid prototyping and experimentation with minimal syntax overhead by providing a uniform interface to >25 optimization libraries, hence 100+ optimization solvers encompassing almost all classes of optimization algorithms such as global, mixed-integer, non-convex, second-order local, constrained, etc.

This package provides general guidelines to represent non-linear programming (NLP) problems in Julia and a standardized API to evaluate the functions and their derivatives. The main objective is to be able to rely on that API when designing optimization solvers in Julia.

A Julia package for non-smooth optimization algorithms. This package provides algorithms for the minimization of objective functions that include non-smooth terms, such as constraints or non-differentiable penalties.

This Java software includes algorithms of combinatorical optimization for the NP-hard offline ship lock scheduling problem. Solutions and performed computations can be displayed graphically. Besides, there is a framework for generating test instances and running these in parallel, as well as R/JGR code for statistical evaluation. Some tools for estimating the quality of calculated solutions will be further improved. Initially the software was developed within a project of TU Berlin regarding...

...Inheriting the use of Hoeffding bound in VFDT algorithm for node-splitting check, it contains four optional strategies of functional tree leaf, which improve the classifying accuracy. In addition, a multi-objective incremental optimization mechanism investigates a balance among accuracy, mode size and learning speed...

... ● LiPS gives sensitivity analysis procedures, which allow us to study the behaviour of the model when you change its parameters, including: analysis of changes in the right sides of constraints, analysis of changes in the coefficients of the objective function, analysis of changes in the column/row of the technology matrix. Such information may be extremely useful for the practical application of LP Models. ● LiPS provides methods of goal programming, including lexicographic and weighted GP methods, which are oriented on multi-objective optimisation.