134 skills found · Page 1 of 5
guofei9987 / Scikit OptGenetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization Algorithm,Immune Algorithm, Artificial Fish Swarm Algorithm, Differential Evolution and TSP(Traveling salesman)
LiYangSir / Smart Algorithm智能算法-遗传算法、蚁群算法、粒子群算法实现。实现版本Java,Python,MatLab多版本实现
HaaLeo / SwarmlibThis repository implements several swarm optimization algorithms and visualizes them. Implemented algorithms: Particle Swarm Optimization (PSO), Firefly Algorithm (FA), Cuckoo Search (CS), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), Grey Wolf Optimizer (GWO) and Whale Optimization Algorithm (WOA)
doFighter / Computational Intelligence记录计算智能优化算法的学习笔记，通过阅读论文并复现的形式加深对相关的启发式智能优化的理解。
pjmattingly / Ant Colony OptimizationImplementation of the Ant Colony Optimization algorithm (python)
bz51 / AntColonyAlgorithm蚁群算法的JS实现
Akavall / AntColonyOptimizationAnt Colony Optimization Algorithm using Python.
cuntou0906 / Route Planninguse some algorithm to solve the Route Planning. Including Genetic Algorithm(GA),Particle Swarm Optimization(PSO),ant colony optimization(ACO).
kyegomez / Swarms PytorchSwarming algorithms like PSO, Ant Colony, Sakana, and more in PyTorch 😊
SajadAHMAD1 / Chaotic GSA For Engineering Design ProblemsAll nature-inspired algorithms involve two processes namely exploration and exploitation. For getting optimal performance, there should be a proper balance between these processes. Further, the majority of the optimization algorithms suffer from local minima entrapment problem and slow convergence speed. To alleviate these problems, researchers are now using chaotic maps. The Chaotic Gravitational Search Algorithm (CGSA) is a physics-based heuristic algorithm inspired by Newton's gravity principle and laws of motion. It uses 10 chaotic maps for global search and fast convergence speed. Basically, in GSA gravitational constant (G) is utilized for adaptive learning of the agents. For increasing the learning speed of the agents, chaotic maps are added to gravitational constant. The practical applicability of CGSA has been accessed through by applying it to nine Mechanical and Civil engineering design problems which include Welded Beam Design (WBD), Compression Spring Design (CSD), Pressure Vessel Design (PVD), Speed Reducer Design (SRD), Gear Train Design (GTD), Three Bar Truss (TBT), Stepped Cantilever Beam design (SCBD), Multiple Disc Clutch Brake Design (MDCBD), and Hydrodynamic Thrust Bearing Design (HTBD). The CGSA has been compared with seven state of the art stochastic algorithms particularly Constriction Coefficient based Particle Swarm Optimization and Gravitational Search Algorithm (CPSOGSA), Standard Gravitational Search Algorithm (GSA), Classical Particle Swarm Optimization (PSO), Biogeography Based Optimization (BBO), Continuous Genetic Algorithm (GA), Differential Evolution (DE), and Ant Colony Optimization (ACO). The experimental results indicate that CGSA shows efficient performance as compared to other seven participating algorithms.
afurculita / VehicleRoutingProblemVehicle Routing Problem solved using Ant Colony System, Greedy and Tabu Search algorithms
eric20123101 / Improved Path Planning For Ant Colony AlgorithmNo description available
jullyjelly / Intelligent AlgorithmOptimization problem solving: genetic algorithm, ant colony algorithm, tabu search algorithm, simulated annealing algorithm, particle swarm optimization
diogo-fernan / AcoA C++ Ant Colony Optimization (ACO) algorithm for the traveling salesman problem.
Haghrah / ACO Robot Path PlanningSimulation of a paper which has used Ant Colony Optimization algorithm for robot path planning ...
ccssmnn / HegoMetaheuristics / Blackbox Optimization Algorithms for Go: Simulated Annealing, Genetic Algorithm, Ant Colony Optimization, Tabu Search, Particle Swarm Optimization ...
yinchuandong / GA AS AlgorithmGenetic Algorithm (GA) and Ant Colony Optimization (ACO) for travel itinerary planning
EvanOman / AntColonyOptimization TSPAn Ant Colony Optimization algorithm for the Traveling Salesman Problem
ralucanecula / DVRPTW ACSAnt 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)
cptanalatriste / IsulaA Java Framework for Ant Colony Optimization algorithms.
