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cca16

Matlab code for our CCA 2016 paper "Fast Algorithms for UAV Tasking and Routing"

Language:MATLAB1700

Distance-vector

An implementation of the Distance-Vector Routing Protocol using the Bellman Ford Algorithm

Language:Python1000

My_Distance_Vector_Routing

a simple implementation of the Distance vector routing algorithm

Language:Python400

libara

A framework for the implementation and evaluation of routing algorithms based on the Ant Colony Optimization (ACO) metaheuristic.

Language:C++GPL-3.01900

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

Language:MATLAB67500

Chaotic-GSA-for-Engineering-Design-Problems

All 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.

Language:MATLAB8800