Dang, JingJingDangEdelman, DavidDavidEdelmanHochreiter, RonaldRonaldHochreiterBrabazon, AnthonyAnthonyBrabazon2011-01-202011-01-202010 IEEE2010-07978-1-4244-6909-3http://hdl.handle.net/10197/2734Congress on Evolutionary Computation, IEEE World Congress on Computational Intelligence, Barcelona, Spain, 18-23 JulyAsset allocation is critical for the portfolio management process. In this paper, we solve a dynamic asset allocation problem through a multiperiod stochastic programming model. The objective is to maximise the expected utility of wealth at the end of the planning periods. To improve the optimisation result of the model, we employ swarm intelligent optimisers, the Bacterial Foraging Optimisation (BFO) algorithm and the Particle Swarm Optimisation (PSO) algorithm. A hybrid optimiser using the Bacterial Foraging Optimisation algorithm for initialisation and the Sequential Quadratic Programming (SQP) for local search is also suggested. The results are compared with the standard-alone SQP and the canonical Genetic Algorithm. The numerical results suggest the hybrid method provides better result, with improved accuracy, stability and computing speed than using BFO, PSO, GA, or SQP alone.412729 bytesapplication/pdfenPersonal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Swarm intelligenceDynamic asset allocationPortfolio management--Computer simulationSwarm intelligenceStochastic programmingQuadratic programmingConference Publication10.1109/CEC.2010.5586135https://creativecommons.org/licenses/by-nc-sa/1.0/