C# 클래스 SwarmOps.Optimizers.MOL

Many Optimizing Liaisons (MOL) optimization method devised as a simplification to the PSO method originally due to Eberhart et al. (1, 2). The MOL method does not have any attraction to the particle's own best known position, and the algorithm also makes use of random selection of which particle to update instead of iterating over the entire swarm. It is similar to the "Social Only" PSO suggested by Kennedy (3), and was studied more thoroguhly by Pedersen et al. (4) who found it to sometimes outperform PSO, and have more easily tunable control parameters.

References: (1) J. Kennedy and R. Eberhart. Particle swarm optimization. In Proceedings of IEEE International Conference on Neural Networks, volume IV, pages 1942-1948, Perth, Australia, 1995 (2) Y. Shi and R.C. Eberhart. A modified particle swarm optimizer. In Proceedings of the IEEE International Conference on Evolutionary Computation, pages 69-73, Anchorage, AK, USA, 1998. (3) J. Kennedy. The particle swarm: social adaptation of knowledge, In: Proceedings of the IEEE International Conference on Evolutionary Computation, Indianapolis, USA, 1997. (4) M.E.H. Pedersen and A.J. Chipperfield. Simplifying particle swarm optimization. Applied Soft Computing, 10, p. 618-628, 2010.

상속: Optimizer

파일 보기 프로젝트 열기: DanWBR/dwsim3

공개 메소드들

메소드	설명
EnforceConstraints ( double &parameters ) : bool	Enforce constraints and evaluate feasiblity of the wrapped problem.
Feasible ( double parameters ) : bool	Evaluate feasibility (constraint satisfaction) of the wrapped problem.
GetNumAgents ( double parameters ) : int	Get parameter, Number of agents, aka. swarm-size.
GetOmega ( double parameters ) : double	Get parameter, Omega.
GetPhi ( double parameters ) : double	Get parameter, Phi.
MOL ( ) : System.Diagnostics	Construct the object.
MOL ( Problem problem ) : System.Diagnostics	Construct the object.
Optimize ( double parameters ) : Result	Perform one optimization run and return the best found solution.

메소드 상세

EnforceConstraints() 공개 메소드

Enforce constraints and evaluate feasiblity of the wrapped problem.

public EnforceConstraints ( double &parameters ) : bool
parameters	double	Candidate solution.
리턴	bool

Feasible() 공개 메소드

Evaluate feasibility (constraint satisfaction) of the wrapped problem.

public Feasible ( double parameters ) : bool
parameters	double	Candidate solution.
리턴	bool

GetNumAgents() 공개 메소드

Get parameter, Number of agents, aka. swarm-size.

public GetNumAgents ( double parameters ) : int
parameters	double	Optimizer parameters.
리턴	int

GetOmega() 공개 메소드

Get parameter, Omega.

public GetOmega ( double parameters ) : double
parameters	double	Optimizer parameters.
리턴	double

GetPhi() 공개 메소드

Get parameter, Phi.

public GetPhi ( double parameters ) : double
parameters	double	Optimizer parameters.
리턴	double

MOL() 공개 메소드

Construct the object.

public MOL ( ) : System.Diagnostics
리턴	System.Diagnostics

MOL() 공개 메소드

Construct the object.

public MOL ( Problem problem ) : System.Diagnostics
problem	Problem	Problem to optimize.
리턴	System.Diagnostics

Optimize() 공개 메소드

Perform one optimization run and return the best found solution.

public Optimize ( double parameters ) : Result
parameters	double	Control parameters for the optimizer.
리턴	Result