Metaheuristics Collection

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⚡Thunder

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“AI constraint solver in Java to optimize the vehicle routing problem, employee rostering, task assignment, maintenance scheduling, conference scheduling and other planning problems.”

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🔮Psychic

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“Distributed GPU-Accelerated Framework for Evolutionary Computation. Comprehensive Library of Evolutionary Algorithms & Benchmark Problems.”

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🔮Psychic

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“A research toolkit for particle swarm optimization in Python ”

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🔮Psychic

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“A Collection Of The State-of-the-art Metaheuristic Algorithms In Python (Metaheuristic/Optimizer/Nature-inspired/Biology)”

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🔥Fire

★★

“A C++ platform to perform parallel computations of optimisation tasks (global and local) via the asynchronous generalized island model.”

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🌊Water

★★

“:four_leaf_clover: Evolutionary optimization library for Go (genetic algorithm, partical swarm optimization, differential evolution)”

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⚡Thunder

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“Jenetics - Genetic Algorithm, Genetic Programming, Grammatical Evolution, Evolutionary Algorithm, and Multi-objective Optimization”

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🔥Flame

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“A framework for single/multi-objective optimization with metaheuristics”

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🔮Psychic

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“🎯 A comprehensive gradient-free optimization framework written in Python”

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⚡Thunder

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“jMetal: a framework for multi-objective optimization with metaheuristics”

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🔮Psychic

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“EvoloPy toolbox provides classical and recent nature-inspired metaheuristic for the global optimization.”

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⚡Thunder

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“OptaPy is an AI constraint solver for Python to optimize planning and scheduling problems.”

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📦Normal

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“This toolbox offers more than 40 wrapper feature selection methods include PSO, GA, DE, ACO, GSA, and etc. They are simple and easy to implement.”

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🔥Fire

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“Derivative-Free Global Optimization Algorithm (C++, Python binding) - Continuous, Discrete, TSP, NLS, MINLP”

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🔮Psychic

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“A MSc's Dissertation Project which focuses on Vehicle Routing Problem with Time Windows (VRPTW), using both exact method and heuristic approach (General Variable Neighbourhood Search)”

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📦Normal

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“Toolbox for gradient-based and derivative-free non-convex constrained optimization with continuous and/or discrete variables. ”

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🔥Fire

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“An evolutionary computation framework to (automatically) build fast parallel stochastic optimization solvers”

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🔮Psychic

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“Artificial Bee Colony Algorithm in Python.”