zjeee,github

2e-vrp-abc

In the process of solving the 2E-VRP problem, the large-scale destruction and repair algorithm is used to ensure that the algorithm does not fall into the local optimal solution. The process of the initialization process uses the greedy strategy to cluster the customers. The large-scale destruction process is to randomly remove the customer nodes on the satellite into the customer pool. The repair process is based on the reciprocal of the customer's distance to each customer in the customer pool. Gambling Select the satellite to which the customer belongs and engage in greedy insertion. For the second layer of path planning, you need to use multiple search operators, such as random sequence reversal exchange operator, crossover operator, damage and repair operator, and crossover operator variants, etc. to improve the artificial bee group algorithm Of the local search ability. To ensure that the global optimal situation can be found, the neighborhood of large-scale search. The improved artificial bee colony algorithm incorporates the idea of simulated annealing and improves the global optimization ability of artificial bee colony algorithm. For the artificial bee colony algorithm, the combination of global optimization ability and local optimization ability improves the possibility that the algorithm can find a better solution than the existing method. Multi - operator artificial bee colony algorithm, which extends the search range of the food source 's neighborhood, and more possibilities to find the global optimal solution. The experimental results show that the algorithm can get better path planning results

alns

ALNS Algorithm which optimise MINLP railroad network models (applied to Madrid's network)

autorccar

OpenCV Python Neural Network Autonomous RC Car

center-cut-minlp

The Center-Cut Algorithm can be used to solve convex MINLP problems.

collection_of_optimization_algorithms

A collection of evolutionary optimization algorithms in MATLAB

coramin

A collection of tools (classes, functions, etc.) for developing MINLP algorithms

fjsp

分别用改进的粒子群优化算法和改进的差分进化算法求解柔性作业车间调度问题

gasmodels.jl

A Julia/JuMP Package for Gas Network Optimization

github-slideshow

A robot powered training repository :robot:

goga

Go evolutionary algorithm is a computer library for developing evolutionary and genetic algorithms to solve optimisation problems with (or not) many constraints and many objectives. Also, a goal is to handle mixed-type representations (reals and integers).

ieee_tec_2014_rtea

Code for the rolling-tide evolutionary algorithm described in the IEEE Transactions on Evolutionary Computation paper

masterthesis-nlp

NLP optimization of water pressure in water distribution networks - MINLP, Penalty method, Relaxation method

matlab_algorithm_with_cases

遗传算法、免疫算法、退火算法、粒子群算法、鱼群算法、蚁群算法和神经网络算法等常用智能算法的MATLAB实现

minlp

关于MINLP问题的一些资料（Resources of MINLP problem）

multi-unit-production-planning-with-integer-and-continuous-variables

An optimization test suite involving 162 integer and 108 continuous variables

new-milton

New Milton Compressor Station WonderWare to FactoryTalk conversion application.

pymetaheuristic

A python library for: Adaptive Random Search, Ant Lion Optimizer, Arithmetic Optimization Algorithm, Artificial Bee Colony Optimization, Artificial Fish Swarm Algorithm, Bat Algorithm, Biogeography Based Optimization, Cross-Entropy Method, Cuckoo Search, Differential Evolution, Dispersive Flies Optimization, Dragonfly Algorithm, Firefly Algorithm, Flow Direction Algorithm, Flower Pollination Algorithm, Genetic Algorithm, Grasshopper Optimization Algorithm, Gravitational Search Algorithm, Grey Wolf Optimizer, Harris Hawks Optimization, Improved Grey Wolf Optimizer, Improved Whale Optimization Algorithm, Jaya, Jellyfish Search Optimizer, Memetic Algorithm, Moth Flame Optimization, Multiverse Optimizer, Particle Swarm Optimization, Random Search, Salp Swarm Algorithm, Simulated Annealing, Sine Cosine Algorithm, Whale Optimization Algorithm

pysofc

A zero-dimensional (lumped parameters) model of a Solid Oxide Fuel Cell (SOFC), including graphical user interface.

real-coded-integer-handling-nsga-ii

MultiObjective Optimization Non-Sorting Genetic Algorithm capable to solve Mixed-Integer Non-Linear Problems.

salpoptimalcoordinationofdirectionalovercurrentrelays

Implementação da técnica híbrida Simulated Annealing - Linear Programming para coordenação ótima de relés de sobrecorrente direcionais. Esta técnica foi aplicada em 5 sistemas de teste. A aplicação é baseada na referência: Kida, Alexandre Akira & Labrador Rivas, Angel & Pareja, Luis. (2020). An improved simulated annealing–linear programming hy- brid algorithm applied to the optimal coordination of directional overcurrent relays. Electric Power Systems Research. 181. 10.1016/j.epsr.2020.106197.

scikit-opt

Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization Algorithm,Immune Algorithm, Artificial Fish Swarm Algorithm, Differential Evolution and TSP(Traveling salesman)

seeding-methods-for-general-minlp

Implementation of algorithms for solving MINLP problems

solid_oxide_fuel_cell_simulation_using_python

Mathematical Modelling and Simulation of Solid Oxide Fuel Cell using Python coding.

supplier_selection_and_order_allocation_optimization

We consider an integrated supplier selection and inventory control problem for a multi-echelon inventory system with an order-splitting policy. A buyer firm consisting of one warehouse and $N$ identical retailers procures a type of product from a group of potential suppliers; the acquisition of the warehouse takes place when the inventory level depletes to a reorder point $R$, and the order $Q$ is simultaneously split among $m$ selected suppliers. We develop an exact analytical model for the order-splitting problem in a multi-echelon system, and formulate the supplier selection problem in a Mixed Integer Nonlinear Programming (MINLP) model. This model determines the optimal inventory policy that coordinates stock levels between each echelon of the systems while properly allocating orders among selected suppliers to maximize the expected profit. For verification and validation of the proposed mathematical model, we conduct several numerical analyses and implement simulation models which helps us demonstrate the model's solvability and effectiveness.

zeroonelinearprogramming

元启发式算法求解0-1线性整数规划问题

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