Heuristics for cardinality constrained portfolio optimisation
Computers and Operations Research
Swarm intelligence
A Study of Global Optimization Using Particle Swarms
Journal of Global Optimization
Fundamentals of Computational Swarm Intelligence
Fundamentals of Computational Swarm Intelligence
Differential evolution and particle swarm optimisation in partitional clustering
Computational Statistics & Data Analysis
A study of particle swarm optimization particle trajectories
Information Sciences: an International Journal
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
Multiobjective Evolutionary Algorithms for Portfolio Management: A comprehensive literature review
Expert Systems with Applications: An International Journal
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This work presents Particle Swarm Optimization (PSO), a collaborative population-based swarm intelligent algorithm for solving the cardinality constraints portfolio optimization problem (CCPO problem). To solve the CCPO problem, the proposed improved PSO increases exploration in the initial search steps and improves convergence speed in the final search steps. Numerical solutions are obtained for five analyses of weekly price data for the following indices for the period March, 1992 to September, 1997: Hang Seng 31 in Hong Kong, DAX 100 in Germany, FTSE 100 in UK, S&P 100 in USA and Nikkei 225 in Japan. The computational test results indicate that the proposed PSO outperformed basic PSO algorithm, genetic algorithm (GA), simulated annealing (SA), and tabu search (TS) in most cases.