Search Results for "multi-objective optimization" - Page 3

Generic toolbox of Particle Swarm Optimization developed in Scilab (PSOTS). It is developed for a variety of complex problems, including single and multi-objective optimization problems, continuous and discrete problems, and mixed integer problems.

The C++ library Geneva allows to run large scale parametric optimization problems. It can run in serial or multi-threaded mode or in a networked environment. The library currently covers Evolutionary Strategies, Genetic Algorithms and mixed scenarios.

This program generates customizable hyper-surfaces (multi-dimensional input and output) and samples data from them to be used further as benchmark for response surface modeling tasks or optimization algorithms.

A script(ing/ed/able) optimizing compiler for the C and Objective-C languages (and in the future other languages such as C++, D and Java). Written in a scripting language to allow for experimentation with code generation and optimization techniques.

The Parameter Tuning Unity (PTU) aims to adapt the parameters of ever connected multi-agents system, or expert system with a plugged optimization heuristic likes the descent of gradient for instance.

This project aims at providing an implementation of some state-of-the-art techniques for summarizing multi-dimensional data. Multi-dimensional data summarization can effectively support query optimization algorithms and OLAP applications.

PyOptFrame-LEGACY is a Python version of OptFrame v2, proposed in 2011, now superseeded in 2021 by v5 on GitHub and PIP. The main objective is to provide the same interface to OptFrame C++ optimization framework, including classic metaheuristics such as genetic algorithms, simulated annealing, variable neighborhood search, first/best/multi-improvement, hill climbing, and multi-objective methods such as nsga-ii. See NEWEST version v5 on GitHub and PIP. Please try Official pyoptframe on https://pypi.org/project/optframe/ for OptFrame v5 (last updated 2022).