ParticleSwarmOptimization Class Reference

#include <ql/experimental/math/particleswarmoptimization.hpp>

Inheritance diagram for ParticleSwarmOptimization:

Classes
class	Inertia
	Base inertia class used to alter the PSO state. More...
class	Topology
	Base topology class used to determine the personal and global best. More...

Public Member Functions
	ParticleSwarmOptimization (Size M, const ext::shared_ptr< Topology > &topology, const ext::shared_ptr< Inertia > &inertia, Real c1=2.05, Real c2=2.05, unsigned long seed=SeedGenerator::instance().get())
	ParticleSwarmOptimization (Size M, const ext::shared_ptr< Topology > &topology, const ext::shared_ptr< Inertia > &inertia, Real omega, Real c1, Real c2, unsigned long seed=SeedGenerator::instance().get())
void	startState (Problem &P, const EndCriteria &endCriteria)
EndCriteria::Type	minimize (Problem &P, const EndCriteria &endCriteria)
	minimize the optimization problem P

Protected Attributes
std::vector< Array >	X_
std::vector< Array >	V_
std::vector< Array >	pBX_
std::vector< Array >	gBX_
Array	pBF_
Array	gBF_
Array	lX_
Array	uX_
Size	M_
Size	N_
Real	c0_
Real	c1_
Real	c2_
MersenneTwisterUniformRng	rng_
ext::shared_ptr< Topology >	topology_
ext::shared_ptr< Inertia >	inertia_

Friends
class	Inertia
class	Topology

Detailed Description

The process is as follows: M individuals are used to explore the N-dimensional parameter space: \( X_{i}^k = (X_{i, 1}^k, X_{i, 2}^k, \ldots, X_{i, N}^k) \) is the kth-iteration for the ith-individual.

X is updated via the rule

\[ X_{i, j}^{k+1} = X_{i, j}^k + V_{i, j}^{k+1} \]

with V being the "velocity" that updates the position:

\[ V_{i, j}^{k+1} = \chi\left(V_{i, j}^k + c_1 r_{i, j}^k (P_{i, j}^k - X_{i, j}^k) + c_2 R_{i, j}^k (G_{i, j}^k - X_{i, j}^k)\right) \]

where c are constants, r and R are uniformly distributed random numbers in the range [0, 1], and \( P_{i, j} \) is the personal best parameter set for individual i up to iteration k \( G_{i, j} \) is the global best parameter set for the swarm up to iteration k. \( c_1 \) is the self recognition coefficient \( c_2 \) is the social recognition coefficient

This version is known as the PSO with constriction factor (PSO-Co). PSO with inertia factor (PSO-In) updates the velocity according to:

\[ V_{i, j}^{k+1} = \omega V_{i, j}^k + \hat{c}_1 r_{i, j}^k (P_{i, j}^k - X_{i, j}^k) + \hat{c}_2 R_{i, j}^k (G_{i, j}^k - X_{i, j}^k) \]

and is accessible from PSO-Co by setting \( \omega = \chi \), and \( \hat{c}_{1,2} = \chi c_{1,2} \).

These two versions of PSO are normally referred to as canonical PSO.

Convergence of PSO-Co is improved if \( \chi \) is chosen as \( \chi = \frac{2}{\vert 2-\phi-\sqrt{\phi^2 - 4\phi}\vert} \), with \( \phi = c_1 + c_2 \). Stable convergence is achieved if \( \phi >= 4 \). Clerc and Kennedy recommend \( c_1 = c_2 = 2.05 \) and \( \phi = 4.1 \).

Different topologies can be chosen for G, e.g. instead of it being the best of the swarm, it is the best of the nearest neighbours, or some other form.

In the canonical PSO, the inertia function is trivial. It is simply a constant (the inertia) multiplying the previous iteration's velocity. The value of the inertia constant determines the weight of a global search over local search. Like in the case of the topology, other possibilities for the inertia function are also possible, e.g. a function that interpolates between a high inertia at the beginning of the optimization (hence prioritizing a global search) and a low inertia towards the end of the optimization (hence prioritizing a local search).

The optimization stops either because the number of iterations has been reached or because the stationary function value limit has been reached.

Examples

GlobalOptimizer.cpp.