Timefold Solver is an AI constraint solver for Python to optimize the Vehicle Routing Problem, Employee Rostering, Maintenance Scheduling, Task Assignment, School Timetabling, Cloud Optimization, Conference Scheduling, Job Shop Scheduling, Bin Packing and many more planning problems.

Different meta-heuristic optimization techniques for feature selection

Open-source framework for adaptive manufacturing processes scheduling

A list of optimization algorithms and their sample codes

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 serve as building blocks for tailoring metaheuristics. They were extracted from ten well-known metaheuristics in the literature.

Search for a model and corresponding hyperparameters that best model your data

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.

The Stochastic Optimisation Software (SOS) is a research-oriented software platform for Metaheuristic Optimisation (Stochastic Optimisation). If you are using SOS, please acknowledge the article "Caraffini, F.; Iacca, G. The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms. Mathematics 2020, 8, 785." (https://doi.org/10.3390/math8050785)

A collection of the most commonly used Optimisation Algorithms for Data Science & Machine Learning

Matlab code for Remora Optimization Algorithm (ROA)

Powerful and scalable black-box optimization algorithms for Python and C++.

A Python implementation of the paper "Duelist Algorithm: An Algorithm Inspired by How Duelist Improve Their Capabilities in a Duel" https://arxiv.org/abs/1512.00708

Heterogeneous Improved Dynamic Multi-Swarm PSO (HIDMS-PSO) algorithm. Source code for the paper: IEEE SSCI https://ieeexplore.ieee.org/document/9308313

C++ template classes of metaheuristic algorithms including GA, NSGA2, NSGA3, PSO,AOS, etc

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

Python code for African vulture 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. Powerful improved versions of it called MIPO and SIPO along with its multi-objective version of MOIPO were presented in 2016, 2017 and 2019 by Dr. Ali Mohammadi (myself) and colleagues at the University of Birjand, respectively. This powerful algorithm has also been used in many applications, which has provided very good outputs. In the following, the standard version of the IPO algorithm along with the benchmark functions reviewed in its reference article, and its improved versions are attached.

Routing problem in heterogeneous fleet is discussed.

Adaptive Heterogeneous Improved Dynamic Multi-Swarm PSO (A-HIDMS-PSO) Algorithm. Source code for the paper: IEEE SSCI https://ieeexplore.ieee.org/document/9660115

All courses assignements, exams and projects done during the year study at Data ScienceTech Institute (DSTI).

The codes for metaheuristic optimization algorithms

No description provided.

Simple Assembly Line Balancing Problem Type 2

The purpose of this repository is to present applications of different programming languages in scientific problems.

Genetic Algorithm as an approach to select image restoration parameters that provides the greatest improvement to a turbid underwater image.

Evolutionary Support Vector Machine

University assignment project for the Optimization Methods and Algorithms at PoliTO

Parallel Global Best-Worst Particle Swarm Optimization Algorithm for Solving Optimization Problems (Applied Soft Computing-2023)

This research proposes a novel order batching approach for warehouses to minimize total tardiness, considering category, weight, and fragility constraints. A Set-based Mayfly Algorithm (SBMA) is developed, adapting the Mayfly Algorithm to the discrete problem and leveraging swarming/mating behaviors to avoid local optima.

The code for Pontogammarus Maeoticus Swarm Optimization (PMSO) which is a metaheuristic optimization algorithm