mopsocd: MOPSOCD: Multi-objective Particle Swarm Optimization with...

In mopsocd: MOPSOCD: Multi-objective Particle Swarm Optimization with Crowding Distance

Description

Multi-objective optimization involves maximizing or minimizing multiple interacting and/or conflicting objective functions subject to a set of contraints. MOPSOCD is a multi-objective optimization solver based on particle swarm optimization that uses crowding distance computation to ensure an even spread of non-dominated solutions.

Usage

mopsocd(fn,
        gn,
        varcnt,
        fncnt,
        lowerbound,
        upperbound,
        opt,
        popsize,
        maxgen,
        archivesize,
        verbosity,
        pMut,
        w,
        c1,
        c2)

Arguments

fn	Objective functions to be optimized
gn	Constraints (optional)
varcnt	Number of Parameters
fncnt	Number of Objectives
lowerbound	Parameter Lower Bound
upperbound	Parameter Upper Bound
opt	Optimization type (0: minimization; 1: maximization)
popsize	Population Size (default: 100)
maxgen	Number of Generations (default: 100)
archivesize	Maximum size of archive containing non-dominated points (default: 250)
verbosity	Verbosity Levels : 0,1,2,3 (default: 1)
pMut	Mutation Probability (default: 0.5)
w	Inertia Weight (default: 0.4)
c1	Acceleration Coefficient 1 (default: 1.0)
c2	Acceleration Coefficient 2 (default: 1.0)

Value

The returned value is a pareto object with the following fields:

numsols	Number of Solutions Found
paramvalues	Estimated Parameter Values
objfnvalues	Values of the Objectives

Author(s)

Pros Naval

References

C. R. Raquel and P.C. Naval, "An Effective use of Crowding Distance in Multiobjective Particle Swarm Optimization", Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2005), Washington, D.C., June 25-29, 2005.

See Also

examples pareto

mopsocd documentation built on May 2, 2019, 2:38 p.m.