Regenerated a state-of-the-art meta-heuristic algorithm for the UAV path planning problem, proposed by Qu, Gai, and Zhong.

This is the repository of codes written in class.

Different meta-heuristic optimization techniques for feature selection

Customising optimisation metaheuristics via hyper-heuristic search (CUSTOMHyS). This framework provides tools for solving, but not limited to, continuous optimisation problems using a hyper-heuristic approach for customising metaheuristics. Such an approach is powered by a strategy based on Simulated Annealing. Also, several search operators ser…

Obtaining the best coefficients of Inverse Dynamics Controller, for a dynamical system, with Optimization Algorithms.

IntelELM: A Python Framework for Intelligent Metaheuristic-based Extreme Learning Machine

Metaheuristic Minimization Using Particle Swarm Optimization.

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 trad…

📈 Collect and archive the metaheuristic algorithms and their applications in geophysics.

C++ Optimization Library

Adaptive Multi-Population Optimization Algorithm

A new optimization method based on the dynamic of sliding motion along a frictionless inclined plane. In IPO, a collection of agents cooperate with each other and move toward better positions in the search space by employing Newton’s second law and equations of motion. The standard version of the IPO is presented by Mozafari et al. in 2016. Powe…

Python code for ECS-NL.

MetaPerceptron: Unleashing the Power of Metaheuristic-optimized Multi-Layer Perceptron - A Python Library

The traveling salesman problem (TSP) is a well-known problem in theoretical computer science and operations research. The standard version of the TSP is a hard problem and belongs to the NP-Hard class. In this project, I build an application to implement the TSP by the dynamic approach and the GVNS approach .

Implementation of three nature-inspired search algorithms: Bees Algorithm, Bat Algorithm, and Firefly Algorithm

The codes for metaheuristic optimization algorithms

EvoRBF: Evolving Radial Basis Function Network by Intelligent Nature-inspired Algorithms

Heterogeneous Improved Dynamic Multi-Swarm PSO (HIDMS-PSO) algorithm.