Turning MATLAB's Simulated Annealing to Integer/Discrete Optimization

PLEASE USE THIS FILE ONLY IF YOU HAVE A GOOD GENERAL IDEA ABOUT YOUR OPTIMIZATION PROBLEM OTHERWISE THIS MAY NOT HELP YOUR PROBLEM.This code customizes simulated annealing into an integer

Novel optimization method to find global optimum of non-linear mixed integer objective functions

problems within the context of expensive optimization.A novel optimization algorithm called Hyper-Spherical Search (HSS) algorithm is proposed to solve the non-linear mixed integer optimization problems

Use the mixed-integer genetic algorithm to solve an engineering design problem.

circuit that meet our design criteria. The example uses optimization techniques to minimize the difference between a desired response curve and the curve generated from a simulation of the circuit

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

This Code is a modified versión of free available Tamilselvi Selvaraj NSGA II Matlab Code capable to solve mixed-integer non-linear programming with constraints. Several benchmarks problems are

BNB20 solves mixed integer nonlinear optimization problems

BNB20 solves mixed integer nonlinear optimization problems. It is a branch-and-bound type algorithm.

The optFUMOLA Package: A Simulation-Based Black-Box Optimization Library and Interface

The optFUMOLA Package: A Simulation-Based Black-Box Optimization Library and Interface

An optimization test suite involving 162 integer and 108 continuous variables

This submission can be used to evaluate the performance of optimization techniques on problems with integer and continuous variables. This optimization problem arises for maximization of profit in

Arithmetic with integers of fully arbitrary size. Arrays and vectors of vpi numbers are supported.

Every once in a while, I've wanted to do arithmetic with large integers with magnitude exceeding that which can fit into MATLAB's standard data types. Since I don't have the symbolic toolbox, the

List all partitions of an integer

is 292.)Its an example of a general problem, i.e., in how many unique ways can an integer be partitioned as a sum of smaller positive integers?http://en.wikipedia.org/wiki/Integer_partitionI wrote

This function solves the mixed integer linear programming problems.

This function solves the mixed integer linear programming problems. It uses the linprog.m function that comes with the optimization toolbox of MATLAB. It employs the branch and bound algorithm. It

GWO is a novel meta-heuristic algorithm for global optimization

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

All Pernutations of integers with sum criteria

This function provides all combinations of integer vector that must verify a criteria on the sum.Supported criteria issum(v) == L1sum(v) <= L1sum(v) < L1

使用AFO算法以及其他GA和PSO算法求解不确定多式联运路径优化问题。同时和MATLAB自带的全局优化搜索器进行对比。The AFO algorithm and other GA and PSO algorithms are used to solve the uncertain

Path-optimization

Mixed Integer Nonlinear Programming Solver with APM MATLAB

Solves the mixed integer nonlinear problem:min p(x,y)s.t. f(x,y) <= 0 s.t. g(x,y) == 0 s.t. lb <= x <= ub s.t. nlb <= y <= nub x(yidx) integer where yidx is a logical index vectory

Solve linear mixed integer problems with a branch and bound method.

Solves the mixed integer linear problem:min c'*x s.t. A*x <= b s.t. Aeq*x == beqs.t. lb <= x <= ubx(yidx) integerwhere yidx is a logical index vector.This program solves linear mixed integer

This app gives all the priliminary data, which helps in optimization of the UAV design

Generates a table of all integer partitions of integers from 0 to N.

Integer partitions are the different ways to express an integer say "4" as a sum of other positive integers, in this case we would have 4=4,3+1,2+2,2+1+1,1+1+1+1. This program calculates all the

A simple structured MATLAB implementation of PSO

For more information, see the following link:http://yarpiz.com/50/ypea102-particle-swarm-optimization

Online optimization of energy storage actions in a microgrid given system constraints and pricing

Energy management systems (EMS) help to optimize the usages of distributed energy resources (DERs) in microgrids, particularly when variable pricing and generation are involved. This example walks

Tips and tricks for use of the optimization toolbox, linear and nonlinear regression.

. Inclusive versus exclusive bound constraints21. Mixed integer/discrete problems22. Understanding how they work23. Wrapping an optimizer around quad24. Graphical tools for understanding sets of nonlinear

MATLAB implementation of ACO for Discrete and Combinatorial Optimization Problems

For more information see the following link:http://yarpiz.com/53/ypea103-ant-colony-optimization

Generate code optimized for C2000 MCU.

Surrogate model optimization algorithm for computationally expensive global optimization problems

Description: Surrogate model toolbox for- unconstrained continuous- constrained integer- constrained mixed-integerglobal optimization problems that are computationally expensive.The user can choose

Four-bar linkage lengths are optimized using MATLAB to follow a desired trajectory.

This example shows a four-bar linkage modeled in Simscape Multibody that is optimized using MATLAB so that the tip of the linkage follows a desired trajectory.Mechanical designers often wish to

Implementation of a PSO algorithm with the same syntax as the Genetic Algorithm Toolbox.

Previously titled "Another Particle Swarm Toolbox"IntroductionParticle swarm optimization (PSO) is a derivative-free global optimum solver. It is inspired by the surprisingly organized behaviour of

Finds least L1-norm solutions to linear equations C*x=d subject to linear and/or integer constraints.

similar to the Optimization Toolbox's lsqlin except that it minimizes with respect to the L1-norm, and also because options for integer constraints are available. The solution is achieved by reformulating

Lithium ion battery characterization, state estimation, cell balancing, and thermal management

Targethttp://www.mathworks.com/company/newsletters/articles/battery-pack-modeling-simulation-and-deployment-on-a-multicore-real-time-target.html?s_tid=srchtitleWebinar: Optimizing Vehicle Electrical Design through System-Level Simulationhttp://www.mathworks.com/videos/optimizing-vehicle-electrical-design-through-system-level-simulation-81919.htmlVideo: Real-Time

Demo files from the 2010 webinar "Global Optimization with MATLAB Products"

This submission contains the demo files used in the Global Optimization with MATLAB webinar: http://www.mathworks.com/videos/global-optimization-with-matlab-products-81716.htmlMultStart Demos *

Generate code optimized for STMicroelectronics STM32 Processor based boards

tuning and logging using external modePerform processor-in-the-loop (PIL) with execution profilingGenerate processor optimized code, including CMSIS-DSPDriver block libraries for on-chip and on-board

A function for multi-objective optimization using evolutionary algorithms

NSGA-II is a very famous multi-objective optimization algorithm. I submitted an example previously and wanted to make this submission useful to others by creating it as a function. Even though this

Download true (not pseudo-) random numbers from random.org's generator

The function TRUERAND returns truly random integers using random.org's Random Integer Generator. According to random.org, the numbers are generated based on atmospheric noise and skew-corrected to

The codes of a novel astrophysics-inspired meta-heuristic optimization algorithm, namely Transit Search (TS)

Welcome to the world of Transit Search (TS), a cutting-edge optimization algorithm that draws inspiration from the remarkable method of exoplanet detection known as transit. The TS presents a novel

Searching/Tuning/Optimizing by Particle Swarm Optimization (PSO) method

This is simple basic PSO function.This function is well illustrated and analogically programed to understand and visualize Particle Swarm Optimization theory in better way and how it implemented.To

A toolbox for the Grey Wolf Optimizer (GWO) algorithm

: http://www.mathworks.com.au/matlabcentral/fileexchange/44974-grey-wolf-optimizer--gwo-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

A simple implementation of the Chinese Remainder Theorem for integers.

A simple version of the Chinese Remainder Theorem for integers.A reference that was useful for this implementation was written by Vicky Neale, University of Cambridge and can be found here

Gbest PSO, Lbest PSO, RegPSO, GCPSO, MPSO, OPSO, Cauchy mutation, and hybrid combinations

The Particle Swarm Optimization Research Toolbox was written to assist with thesis research combating the premature convergence problem of particle swarm optimization (PSO). The control panel offers

This program quickly outputs n random integers in the specified range from a to b.

The program quickly outputs n random integers in the range from a to b. The integers are drawn from a uniform distribution to make selection of integers equally probable. This program is intended

Bound constrained optimization using fminsearch

Computes all partitions of a given nonnegative integer n.

A partition of a positive integer n, also called an integer partition, is a way of writing n as a sum of positive integers. Two sums that differ only in the order of their summands are considered the

An implementation of the classic algorithm with code optimized for Matlab

This code does not use any for loops and takes advantage of Matlabs internally optimized routines to produce a fast, optimized version of Bresenham's line drawing algorithm

An optimization test suite involving 162 integer and 108 continuous variables

This submission can be used to evaluate the performance of optimization techniques on problems with integer and continuous variables. This optimization problem arises for maximization of profit in

Multi-Objective Optimization of Aspen Plus Distillation Column using Stochastic Algorithm (NSGA II).

Many optimization problems in chemical engineering involve integer variables and trade-off objective. One approach to address this type of problem is using algorithms that handle continuous and

Numeric base conversion between any two numeric bases, for any size integer.

Files used in "An Introduction to Quadratic Programming" Webinar

hydroelectric dam and then optimize the operation schedule using FMINCON. We then show how improvements can be made to the optimization process and end up with a quadratic programming problem that can be solved

Generates restricted and unrestricted integer compositions

This is a Matlab implementation of a unique algorithm by J. D. Opdyke with very good properties for solving the Integer Composition problem of finding all permutations in the additive partitioning

An optimization test suite involving 162 integer and 108 continuous variables

This submission can be used to evaluate the performance of optimization techniques on problems with integer and continuous variables. This optimization problem arises for maximization of profit in

An optimization test suite involving 162 integer and 108 continuous variables

This submission can be used to evaluate the performance of optimization techniques on problems with integer and continuous variables. This optimization problem arises for maximization of profit in

MATLAB files from the webinar

optimization solver for mixed-integer linear programming in Release 2014a. This new solver enables you to solve optimization problems in which some or all of the variables are constrained to take on integer

Queries www.random.org to collect true random integer numbers.

Random integers drawn uniformly from a specified set.

RAND_INT(R,N) returns an n-by-n matrix containing pseudo-random integer values from range R.

Function that uses RAND to generate random integers in the specified linear range, as follows:result = floor(a + (b-a+1).* rand(N)),where specified range is [a b]-----Please note: There are lots of

The submission employs the recently proposed Grey Wolf Optimizer for training Multi-Layer Perceptron

Grey Wolf Optimizer (GWO) is employed as a trainer for Multi-Layer Perceptron (MLP). The current source codes are the demonstration of the GWO trainer for solving the "Iris" classification problem

MATLAB demonstrations discussed in: G A V Pai, "Metaheuristics for Portfolio Optimization", Wiley-ISTE, 2018.

The MATLAB demonstrations of the metaheuristic portfolio optimization models discussed in the book "Metaheuristics for Portfolio Optimization, An Introduction using MATLAB^®", authored by G A

With Trelea, Common, and Clerc types along with ...

MATLAB's Optimization Toolbox should feel right at home but even if you don't use that toolbox this will be easy to figure. Extensive help is included.Anyone from serious AI researchers to beginning

Generate all restricted integer compositions with fixed number of parts, each in the interval [a,b]

Matlab implementation of an algorithm that generates all restricted integer compositions of an integer n with k parts, each in the discrete interval [a,b]. The algorithm is based on Vincent

In this video tutorial, implementation of PSO in MATLAB is discussed in detail.

In this video tutorial, implementation of Particle Swarm Optimization (PSO) in MATLAB is discussed in detail. In the first part, theoretical foundations of PSO is briefly reviewed. In the next two

Various tools for working with integers and their factors, primes, congruences, etc.

Over the years, I've put together a few useful tools for working with integers, congruences, factors of numbers, divisors, etc. None of these tools requires the use of the symbolic toolbox, although

GIBBON: The Geometry and Image-Based Bioengineering add-ON for MATLAB

Sperm Swarm Optimization (SSO)

A new meta-heuristic optimization approach, called “Sperm Swarm Optimization (SSO)” is proposed. The underlying ideas and concepts behind the proposed method are inspired by sperm motility to

GODLIKE combines 4 global optimizers for both single/multi-objective optimizations

GODLIKE (Global Optimum Determination by Linking and Interchanging Kindred Evaluators) is a generization of various population-based global optimization schemes. Also, it handles both single- and