Heuristics for cardinality constrained portfolio optimisation
Computers and Operations Research
Swarm intelligence
Local Search Techniques for Constrained Portfolio SelectionProblems
Computational Economics
A memetic model of evolutionary PSO for computational finance applications
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Constrained Portfolio Selection using Particle Swarm Optimization
Expert Systems with Applications: An International Journal
Particle Swarm Optimization (PSO) for the constrained portfolio optimization problem
Expert Systems with Applications: An International Journal
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
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In this paper we propose a heuristic approach based on bacterial foraging optimization (BFO) in order to find the efficient frontier associated with the portfolio optimization (PO) problem. The PO model with cardinality and bounding constraints is a mixed quadratic and integer programming problem for which no exact algorithms can solve in an efficient way. Consequently, various heuristic algorithms, such as genetic algorithms and particle swarm optimization, have been proposed in the past. This paper aims to examine the potential of a BFO algorithm in solving the PO problem. BFO is a new swarm intelligence technique that has been successfully applied to several real world problems. Through three operations, chemotaxis, reproduction, and elimination-dispersal, the proposed BFO algorithm can effectively solve a PO problem. The performance of the proposed approach was evaluated in computational tests on five benchmark data sets, and the results were compared to those obtained from existing heuristic algorithms. The proposed BFO algorithm is found to be superior to previous heuristic algorithms in terms of solution quality and time.