Metaheuristics

A Julia package for metaheuristic optimization algorithms. Evolutionary are considered.

Installation

Julia 0.7 or Later

Open the Julia REPL and press ] to open the Pkg prompt. To add this package, use the add command:

pkg> add https://github.com/jmejia8/Metaheuristics.jl.git

Algorithms

• ECA algorithm
• Differential Evolution (DE) algorithm
• Particle swarm optimization (PSO) algorithm

Optimize

optimize function is used to optimize a D-dimensional function: optimize(f::Function, bounds::Array, method::AbstractAlgorithm )

• f objective function
• bounds a 2 times D matrix that contains the lower and upper bounds by rows.
• method optimization method: ECA, DE.

ECA

ECA() is a new metaheuristic optimization algorithm based on center of mass. ECA minimizes an objective function read more..

Parameters

• η_max: stepsize.
• K: number of neighbors for generating the center of mass.
• N: population size.

Example

using Metaheuristics

# Objective function
sphere(x) = sum(x.^2)

bounds = [-10 -10 -10 -10;
             10  10  10  10
]

eca = ECA()

result = optimize(sphere, bounds, eca)

DE

Differential Evolution DE is a method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. Read more...

Parameters

• F: DE-stepsize F_weight from interval [0, 2].
• N: Number of population members.
• CR: Crossover probability constant from interval [0, 1].
• strategy: DE strategy
  • :rand1 DE/rand/1
  • :rand2 DE/rand/2
  • :best1 DE/best/1
  • :best2 DE/best/2
  • :randToBest1 DE/rand-to-best/1

Example

using Metaheuristics

# Objective function
sphere(x) = sum(x.^2)

bounds = [-10 -10 -10 -10;
             10  10  10  10
]

de = DE()

result = optimize(sphere, bounds, de)

PSO

Particle swarm optimization is a population based stochastic optimization technique developed by Dr. Eberhart and Dr. Kennedy in 1995, inspired by social behavior of bird flocking or fish schooling. Read more...

Parameters

• N: Number of population members.
• C1, C2 learning factors (C1 = C2 = 2).
• ω: Inertia weight used for balancing the global search.

Example

using Metaheuristics

# Objective function
sphere(x) = sum(x.^2)

bounds = [-10 -10 -10 -10;
             10  10  10  10
]

pso = PSO()

result = optimize(sphere, bounds, pso)