metaheuristic-designer 0.1.5

A python package to desing metaheuristic optimization algorithms from components.

Metaheuristic-designer

This is an object-oriented framework for the development, testing and analysis of metaheuristic optimization algorithms.

It defines the components of a general evolutionary algorithm and offers some implementations of algorithms along with components that can be used directly. Those components will be explained below.

It was inspired by the article Metaheuristics “in the large” that discusses some of the issues in the research on metaheuristic optimization, sugesting the development of libraries for the standarization of metaheuristic algorithms.

Most of the design decisions are based on the book Introduction to evolutionary computing by Eiben, Agoston E., and James E. Smith which is very well expained and is highly recomended to anyone willing to learn about the topic.

This framework doesn't claim to have a high performance, specially since the chosen language is Python and the code has not been designed for speed. This shouldn't really be an issue since the highest amount of time spent in these kind of algorithms tends to be in the evaluation of the objective function. If you want to compare an algorithm made with this tool with another one that is available by other means, it is recomended to use the number of evaluations of the objective function as a metric instead of execution time.

Instalation

The package is available in the PyPi repository (https://pypi.org/project/metaheuristic-designer/).

To install it, use the pip command as follows:

pip install metaheuristic-designer

Examples

• There are 2 scripts to test this repository:
  • "examples/exec_basic.py": Optimize a simple function, in this case, the "sphere" function that calculates the squared norm of a vector, we want a vector that minmizes this function. There are two possible flags that can be added:
    • "-a [Alg]" use one of the available algorithms, the choices are:
      • HillClimb: simple hill climbing algorithm.
      • LocalSearch: take the best of 20 randomly chosen neighbours.
      • ES: (100+150)-ES, basic evolutionary strategy.
      • HS: Harmony search algorithm.
      • GA: genetic algorithm.
      • SA: simulated annealing algorithm.
      • DE: DE/best/1, differential evolution algorithm.
      • PSO: simple particle swarm algorithm.
      • NoSearch: no search is done.
    • "-m" use a memetic search like structure, do local search after mutation.
  • "examples/exec_basic.py": Evolve an image so that it matches the one given as an input.
    • The same parameters as the previous script.
    • "-i [Image path]" read the image and evolve a random image into this one.

It is recomended that you create a virtual environment to test the examples.

This is done with the following commands:

python -m venv venv
source venv/bin/activate
pip install .[examples]

Once you have activate the virtual environment, you can execute one of the examples like this:

python examples/example_basic.py

or

python examples/image_evolution.py

To run the tests you need to install nox, to execute the tests use the command

nox test

Implemented components

This package comes with some already made components that can be used in any algorithm in this framework

Search strategies

The algorithms implemented are:

Class name	Strategy	Params	Other info
NoSearch	Do nothing		For debugging purposes
RandomSearch	Random Search
HillClimb	Hill climb
LocalSearch	Local seach	iters (number of neighbors to test each time)
SA	Simulated annealing	iter (iterations per temperature change), temp_init (initial temperature), alpha (exponent of the temperature change)
GA	Genetic algorithm	pmut (probability of mutation), pcross (probability of crossover)
ES	Evolution strategy	offspringSize (number of indiviuals to generate each generation)
HS	Harmony search	HSM, HMCR, BW, PAR
PSO	Particle Swarm optimization	w,c1,c2
DE	Differential evolution
CRO	Coral Reef Optimization	rho,Fb,Fd,Pd,attempts
CRO_SL	Coral Reef Optimization with substrate layers	rho,Fb,Fd,Pd,attempts
PCRO_SL	probabilistic Coral Reef Optimization with substrate layers	rho,Fb,Fd,Pd,attempts
DPCRO_SL	Dynamic probabilistic Coral Reef Optimization with substrate layers	rho,Fb,Fd,Pd,attempts,group_subs,dyn_method,dyn_steps,prob_amp
VND	Variable neighborhood descent
RVNS	Restricted variable neighborhood search		In progress
VNS	Variable neighborhood search		In progress
CMA_ES	Covariance matrix adaptation - Evolution strategy		Not implemented yet

Survivor selection methods

These are methods of selecting the individuals to use in future generations.

The methods implemented are:

Method name	Algorithm	Params	Other info
"Elitism"	Elitism	amount
"CondElitism"	Conditional Elitism	amount
"nothing" or "generational"	Replace all the parents with their children		Needs the offspring size to be equal to the population size
"One-to-one" or "HillClimb"	One to one (compare each parent with its child)		Needs the offspring size to be equal to the population size
"Prob-one-to-one" or "ProbHillClimb"	Probabilitisc One to one (with a chance to always choose the child)	p	Needs the offspring size to be equal to the population size
"(m+n)" or "keepbest"	(λ+μ), or choosing the λ best individuals taking parents and children
"(m,n)" or "keepoffspring"	(λ,μ), or taking the best λ children		λ must be smaller than μ
"CRO"	A 2 step survivor selection method used in the CRO algorithm. Each individual attempts to enter the population K times and then a percentage of the worse individuals will be eliminated from the population	Fd,Pd,attempts,maxPopSize	Can return a population with a variable number of individuals

Parent selection methods

These are methods of selecting the individuals that will be mutated/perturbed in each generation

The methods implemented are:

Method name	Algorithm	Params	Other info
"Torunament"	Choose parents by tournament	amount, p
"Best"	Select the n best individuals	amount
"Random"	Take n individuals at random	amount
"Roulette"	Perform a selection with the roullette method	amount, method, F
"SUS"	Stochastic universal sampling	amount, method, F
"Nothing"	Take all the individuals from the population

Operators

Class name	Domain	Other info
OperatorReal	Real valued vectors
OperatorInt	Integer valued vectors
OperatorBinary	Binary vectors
OperatorPerm	Permutations
OperatorList	Variable length lists
OperatorMeta	Other operators
OperatorLambda	Any	Lets you specify a function as an operator

The Operators functions available in the operator classes are:

Method name	Algorithm	Params	Domains
"1point"	1 point crossover		Real, Int, Bin
"2point"	2 point crossover		Real, Int, Bin
"Multipoint"	multipoint crossover		Real, Int, Bin
"WeightedAvg"	Weighted average crossover	F	Real, Int
"BLXalpha"	BLX-alpha crossover	Cr	Real
"Multicross"	multi-individual multipoint crossover	Nindiv	Real, Int, Bin
"XOR"	Bytewise XOR with a random vector	N	Int
"XORCross"	Bytewise XOR between 2 vectors component by component		Int
"sbx"	SBX crossover	Cr	Real
"Perm"	Permutate vector components	N	Real, Int, Bin, Perm
"Gauss"	Add Gaussian noise	F	Real, Int
"Laplace"	Add noise following a Laplace distribution	F	Real, Int
"Cauchy"	Add noise following a Cauchy distribution	F	Real, Int
"Poisson"	Add noise following a Cauchy distribution	F	Int
"Uniform"	Add Uniform noise	Low, Up	Real, Int
"MutRand" or "MutNoise"	Add random noise to a number of vector components	method, N, optionaly: Low, Up, F	Real, Int
"MutSample"	Take a sample from a probability distribution and put it on a number of vector components	method, N, optionaly: Low, Up, F	Real, Int
"RandNoise"	Add random noise	method, optionaly: Low, Up, F	Real, Int
"RandSample"	Sample from a probability distribution	method, optionaly: Low, Up, F	Real, Int
"DE/Rand/1"	Sample from a probability distribution	F, Cr	Real, Int
"DE/Best/1"	Sample from a probability distribution	F, Cr	Real, Int
"DE/Rand/2"	Sample from a probability distribution	F, Cr	Real, Int
"DE/Best/2"	Sample from a probability distribution	F, Cr	Real, Int
"DE/Current-to-rand/1"	Sample from a probability distribution	F, Cr	Real, Int
"DE/Current-to-best/1"	Sample from a probability distribution	F, Cr	Real, Int
"DE/Current-to-pbest/1"	Sample from a probability distribution	F, Cr, p	Real, Int
"PSO"	Sample from a probability distribution	w, c1, c2	Real, Int
"Firefly"	Sample from a probability distribution	a,b,c,g	Real, Int
"Random"	Sample from a probability distribution		Real, Int, Bin, Perm
"RandomMask"	Randomly sample a number of vector components	N	Real, Int
"Swap"	Swap two components		Perm
"Insert"	Insert a component and shift to the left		Perm
"Scramble"	Scramble permutation order	N	Perm
"Invert"	Reverse order of components		Perm
"Roll"	Roll components to the right	N	Perm
"PMX"	Partially mapped crossover		Perm
"OrderCross"	Ordered crossover		Perm
"branch"	Choose one of the provided operators randomly		Operators
"sequence"	Apply all the provided operators in order		Operators
"split"	Apply each operator to a subset of vector components following the mask provided		Operators
"pick"	Manually pick one of the operators provided (setting the chosen_idx attribute)		Operators
"Dummy"	Assing the vector to a predefined value		All
"Custom"	Provide a lambda function to apply as an operator	function	All
"Nothing"	Do nothing		All

Initializers

Initializers create the initial population that will be evolved in the optimization process.

Some of the implemente Initializers are:

Class name	Description	Other info
DirectInitializer	Initialize the population to a preset list of individuals
SeedProbInitializer	Initializes the population with another initializer and inserts user-specified individuals with a probability
SeedDetermInitializer	Initializes the population with another initializer and inserts a number of user-specified individuals into the population
GaussianVectorInitializer	Initialize individuals with normally distributed vectors
UniformVectorInitializer	Initialize individuals with uniformly random distributed vectors
PermInitializer	Initialize individuals with random permuations
LambdaInitializer	Initialize individuals with a user-defined function

Encodings

Specifying the Encoding is optional but can be very helpful for some types of problems.

An encoding will represent each solution differently in the optimization process and the evaluation of the fintess, since most algorithm work only with vectors, but we might need other types of datatypes for our optimization.

Some of the implemented Encodings are:

Class name	Encoding	Decoding	Other info
DefaultEncoding	Makes no changes to the input	Makes no changes to the input
TypeCastEncoding	Changes the datatype of the vector from T1 to T2	Changes the datatype of the vectorfrom T1 to T2
MatrixEncoding	Converts a vector into a matrix of size NxM	Converts a matrix to a vector with the .flatten() method
ImageEncoding	Converts a vector into a matrix of size NxMx1 or NxMx3, each component is an unsigned 8bit number	Converts a matrix to a vector with the .flatten() method
LambdaEncoding	Applies the user-defined encode function	Applies the user-defined decode function

Benchmark functions

The benchmark functions you can use to test the algorithms are:

Class name	Domain	Other info
MaxOnes		Integer
DiophantineEq		Integer
MaxOnesReal		Real
Sphere		Real
HighCondElliptic		Real
BentCigar		Real
Discus		Real
Rosenbrock		Real
Ackley		Real
Weistrass		Real
Griewank		Real
Rastrigin		Real
ModSchwefel		Real
Katsuura		Real
HappyCat		Real
HGBat		Real
SumPowell		Real
N4XinSheYang		Real
ThreeSAT		Real
BinKnapsack		Binary
MaxClique		Permutation
TSP		Permutation
ImgApprox		Integer
ImgStd		Integer
ImgEntropy		Integer