Showing 7 open source projects for "differential evolution"

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  • 1
    Evolutionary.jl

    Evolutionary.jl

    Evolutionary & genetic algorithms for Julia

    A Julia package for evolutionary & genetic algorithms. The package can be installed with the Julia package manager.
    Downloads: 0 This Week
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  • 2
    PlatEMO

    PlatEMO

    Evolutionary multi-objective optimization platform

    ...Any machines able to run MATLAB can use PlatEMO regardless of the operating system. PlatEMO includes more than ninety existing popular MOEAs, including genetic algorithm, differential evolution, particle swarm optimization, memetic algorithm, estimation of distribution algorithm, and surrogate model-based algorithm. Most of them are representative algorithms published in top journals after 2010. Users can select various figures to be displayed, including the Pareto front of the result, the Pareto set of the result, the true Pareto front, and the evolutionary trajectories of any performance indicator values. ...
    Downloads: 2 This Week
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  • 3
    The ASCO project aims to bring circuit optimization capabilities to existing SPICE simulators using a high-performance parallel differential evolution (DE) optimization algorithm. It supports Eldo, HSPICE, LTspice, Spectre, and Qucs.
    Downloads: 15 This Week
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  • 4
    scikit-opt

    scikit-opt

    Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing

    scikit-opt is a Python library for solving optimization problems with evolutionary and swarm-intelligence algorithms. It includes genetic algorithms, particle swarm optimization, differential evolution, simulated annealing, ant colony optimization, immune algorithms, and artificial fish swarms. The package can address continuous objectives, constrained problems, and combinatorial tasks such as the traveling salesman problem. A consistent workflow lets users define an objective, configure an optimizer, run iterations, and inspect the best solution. ...
    Downloads: 0 This Week
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  • 5

    OpenDino

    Open Source Java platform for Optimization, DoE, and Learning.

    ...It provides a graphical user interface (GUI) and a platform which simplifies integration of new algorithms as "Modules". Implemented Modules Evolutionary Algorithms: - CMA-ES - (1+1)-ES - Differential Evolution Deterministic optimization algorithm: - SIMPLEX Learning: - a simple Artificial Neural Net Optimization problems: - test functions - interface for executing other programs (solvers) - parallel execution of problems - distributed execution of problems via socket connection between computers Others: - data storage - data analyser and viewer
    Downloads: 0 This Week
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  • 6

    Point Symmetry Clustering

    Point Symmetry Clustering Approach Using Differential Evolution

    Implementation of Point Symmetry-based Automatic Clustering Approach Using Differential Evolution Using bug fixed KD tree nearest neighbor search from https://github.com/CristianDallos/kmeansclustering. (It is also modified to search for multiple nearest points instead only for one.) Used those academic works for algorithm implementation: http://cs.cug.edu.cn/teacherweb/gwy/Publication/ISICA-09.pdf http://www.isical.ac.in/~sanghami/ieeetkde_cameraready.pdf http://www.softcomputing.net/smca-paper1.pdf 1. ...
    Downloads: 0 This Week
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  • 7
    Opt4J

    Opt4J

    Modular Java framework for meta-heuristic optimization

    Opt4J is an open source Java-based framework for evolutionary computation. It contains a set of (multi-objective) optimization algorithms such as evolutionary algorithms (including SPEA2 and NSGA2), differential evolution, particle swarm optimization, and simulated annealing. The benchmarks that are included comprise ZDT, DTLZ, WFG, and the knapsack problem. The goal of Opt4J is to simplify the evolutionary optimization of user-defined problems as well as the implementation of arbitrary meta-heuristic optimization algorithms. For this purpose, Opt4J relies on a module-based implementation and offers a graphical user interface for the configuration as well as a visualization of the optimization process.
    Downloads: 0 This Week
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