Applying Particle Swarm Optimization to Solve Portfolio Selection Problems

Particle swarm optimization (PSO), introduced by Kennedy and Eberhart in 1995, is a social population-based search algorithm and is generally similar to the evolutionary computation techniques that have been successfully applied to solve various hard optimization problems. The standard Markowitz mean-variance approach to portfolio selection involves tracing out an efficient frontier, a continuous curve illustrating the tradeoff between return and risk. In this paper we applied the particle swarm approach to find an efficient frontier associated with the classical and general (unconstrained and constrained) mean-variance portfolio selection problem. The OR library data sets were tested in our paper and computational results showed that the PSO found better solutions when compared to genetic algorithm (GA), simulated annealing(SA), and tabu search(TS)

HAPSIMU: a genetic simulation platform for population-based association studies

<p>Abstract</p> <p>Background</p> <p>Population structure is an important cause leading to inconsistent results in population-based association studies (PBAS) of human diseases. Various statistical methods have been proposed to reduce the negative impact of population structure on PBAS. Due to lack of structural information in real populations, it is difficult to evaluate the impact of population structure on PBAS in real populations.</p> <p>Results</p> <p>We developed a genetic simulation platform, HAPSIMU, based on real haplotype data from the HapMap ENCODE project. This platform can simulate heterogeneous populations with various known and controllable structures under the continuous migration model or the discrete model. Moreover, both qualitative and quantitative traits can be simulated using additive genetic model with various genetic parameters designated by users.</p> <p>Conclusion</p> <p>HAPSIMU provides a common genetic simulation platform to evaluate the impact of population structure on PBAS, and compare the relative performance of various population structure identification and PBAS methods.</p