This 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)

Collection of classes and functions to allow 2D/3D path generation with heuristic algorithms such as A*, Theta* and LazyTheta* and ROS Interfaces

pyHarmonySearch is a pure Python implementation of the harmony search (HS) global optimization algorithm.

A solution for Vehicle Routing Problem (VRP) in Java with heuristic algorithms and Tabu search

Meta-heuristic optimisation suite for python

Algorithms for flight scheduling optimization.

The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In addition, three main steps of hunting, searching for prey, encircling prey, and attacking prey, are implemented to perform optimization. This is the source codes of the paper: S. Mirjalili, S. M. Mirjalili, A. Lewis, Grey Wolf Optimizer, Advances in Engineering Software, Volume 69, March 2014, Pages 46-61, ISSN 0965-9978, http://dx.doi.org/10.1016/j.advengsoft.2013.12.007. More information can be found in: http://www.alimirjalili.com/GWO.html

A very simple Genetic Algorithm implementation for matlab, easy to use, easy to modify runs fast.

How to solve the traveling salesman problem with the 2-opt algorithm, a fast heuristic search algorithm.

My n-puzzle solver: A* and IDA* search, heuristics, different puzzle configurations and sizes

Highly efficient holistic 2D visibility solution for grid-based environments/maps (C++ and MATLAB). Extends to a planner.

⚡ A simulation of finding the shortest charging routes for electric vehicle fleets using ant colony optimization.

Single robot path planning algorithms implemented in MATLAB. Including heuristic search and incremental heuristic search methods. A*, LPA*, D*Lite

Informed FMT (Fast Marching Tree) Star Planning ROS Action Server

This repository implements various Search Based (Heuristic and Incremental) and Sampling Based (Multi Query and Single Query) motion planning algorithms using ROS and turtlebot

Using heuristic search Best-First and A* with BFS (with manhatan distance) methods to solve 8-puzzle

AI classic search algorithms with graph and program implementation

A PDDL Solver in C++.

Multiprocessing genetic algorithm implementation library

Shortest Path Algorithms on an Ocean Routing Graph extracted from Open Street Map data.

Heuristics for cardinality constrained portfolio optimisation

As alternative heuristic techniques; genetic algorithm, simulated annealing algorithm and city swap algorithm are implemented in Python for Travelling Salesman Problem. Details on implementation and test results can be found in this repository.

Implementations of Fundamental Algorithms & Data Structures in C++.

This paper presents an intelligent sizing method to improve the performance and efficiency of a CMOS Ring Oscillator (RO). The proposed approach is based on the simultaneous utilization of powerful and new multi-objective optimization techniques along with a circuit simulator under a data link. The proposed optimizing tool creates a perfect tradeoff between the contradictory objective functions in CMOS RO optimal design. This tool is applied for intelligent estimation of the circuit parameters (channel width of transistors), which have a decisive influence on RO specifications. Along the optimal RO design in an specified range of oscillaton frequency, the Power Consumption, Phase Noise, Figure of Merit (FoM), Integration Index, Design Cycle Time are considered as objective functions. Also, in generation of Pareto front some important issues, i.e. Overall Nondominated Vector Generation (ONVG), and Spacing (S) are considered for more effectiveness of the obtained feasible solutions in application. Four optimization algorithms called Multi-Objective Genetic Algorithm (MOGA), Multi-Objective Inclined Planes system Optimization (MOIPO), Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Modified Inclined Planes System Optimization (MOMIPO) are utilized for 0.18-mm CMOS technology with supply voltage of 1-V. Baesd on our extensive simulations and experimental results MOMIPO outperforms the best performance among other multi-objective algorithms in presented RO designing tool.

Eight Puzzle solver using BFS, DFS & A* search algorithms

A web-based GUI tool to showcase SLD and Manhattan Metrics (P5 JS)

This is the A Star algorithm Python implementation

Learning Discrete World Models for Heuristic Search

Learn a domain-specific heuristic function in a domain-independent fashion to solve pathfinding problems.

A C++ implementation of N Puzzle problem using A Star Search with heuristics of Manhattan Distance, Hamming Distance & Linear Conflicts