Муравьиный алгоритм поиска оптимального пути
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Updated
Sep 15, 2015 - Java
Муравьиный алгоритм поиска оптимального пути
A multi agent system, trying to figure out the shortest path between an anthill and a food source using an ant colony algorithm.
This repository contains source code for the four investigated ACO algoritms for the bi-objective Multiple Traveling Salesman Problem. For more details, see this paper "Necula, R., Breaban, M., Raschip, M.: Tackling the Bi-criteria Facet of Multiple Traveling Salesman Problem with Ant Colony Systems. ICTAI, (2015)" (https://ieeexplore.ieee.org/d…
Ant colony system (ACS) based algorithm for the dynamic vehicle routing problem with time windows (DVRPTW). For more details, see this paper "Necula, R., Breaban, M., & Raschip, M.: Tackling Dynamic Vehicle Routing Problem with Time Windows by means of ant colony system. CEC, (2017)" (https://ieeexplore.ieee.org/document/7969606)
Solving the next release problem using an hybrid ant colony optimization algorithm
A population based stochastic algorithm for solving the Traveling Salesman Problem.
Homeworks from the Solving Optimization Problems Using Evolutionary Computation Algorithms in Java course.
Homeworks for the college class: Solving Optimization Problems Using Evolutionary Computation Algorithms In Java
Nature Inspired Computation, Spring 2017
Optimization framework based on swarm intelligence
A metaheuristic solver for the Sudoku Problem.
A set of ant colony system and max-min ant system based algorithms for the single-objective MinMax Multiple Traveling Salesman Problem (mTSP) and for the bi-objective mTSP
Java Ant Colony Optimization Framework
Framework for Genetic-algorithm and Ant-Colony-Optimization in Java (bachelor project)
[university] Exploring various algorithms to approximately solve the single machine total weighted tardiness scheduling problem
Implementation of the Ant Colony Optimization algorithm
Solving the Unrelated Parallel Machine Scheduling Problem (UPMSP) with Ant Colony Algorithms, using the Isula Framework.
Solving Job Shop Scheduling Problem (JSSP) with two types of Swarm Intelligence: Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC)
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