loveJINforever's starred repositories
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.
DVRPTW_ACS
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)
GeneticAlgorithm_CrossMute
这些M文件只要是关于遗传算法中交叉和变异方式的总结
Multi-objective-Optimization
Application of Multi-objective Optimization Algorithm in Cluster Task Scheduling Problem
Constraint_NSGA-II_Algorithms
这是一个带约束条件的非支配排序遗传算法NSGA-II，解决了一个多目标优化问题
nsga2-matlab
A very fast, 90% vectorized, NSGA-II algorithm in matlab.
GA-for-MOO
Genetic Algorithms for Multi Objective Optimization