Initial particles position for PSO, in Bound Constrained Optimization
AbstractWe consider the solution of bound constrained optimization problems, where we assume that the evaluation of the objective function is costly, its derivatives are unavailable and the use of exact derivativefree algorithms may imply a too large computational burden. There is plenty of real applications, e.g. several design optimization problems [1,2], belonging to the latter class, where the objective function must be treated as a Ôblack-boxÕ and automatic differentiation turns to be unsuitable. Since the objective function is often obtained as the result of a simulation, it might be affected also by noise, so that the use of finite differences may be definitely harmful. In this paper we consider the use of the evolutionary Particle Swarm Optimization (PSO) algorithm, where the choice of the parameters is inspired by , in order to avoid diverging trajectories of the particles, and help the exploration of the feasible set. Moreover, we extend the ideas in  and propose a specific set of initial particles position for the bound constrained problem.
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Bibliographic InfoPaper provided by Department of Management, Università Ca' Foscari Venezia in its series Working Papers with number 6.
Length: 11 pages
Date of creation: Jun 2013
Date of revision:
Bound Constrained Optimization; Discrete Dynamic Linear Systems; Free and Forced Responses; Particles Initial Position.;
Find related papers by JEL classification:
- C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
- C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
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