Optimising mobile base station placement using an enhanced Multi-Objective Genetic Algorithm

Portfolio optimization by improved NSGA-II and SPEA 2 based on different risk measures

“…On one hand, based on the promising results of (Subbu et al 2005) and (Baixauli-Soler et al 2010), we include in the same portfolio optimization framework the loss-averse attitude of investors as well as the capital requirements imposed by the regulator, and we investigate the relationship between semivariance and CVaR in quantifying the downside risk. On the other hand, we extend the research of (Liu et al 2010) and investigate the effectiveness of using the uniform selection scheme, the extended intermediate crossover operator (Gen and Cheng 2000;Mühlenbein and Schlierkamp-Voosen 1993), and the Gaussian mutation (Hinterding 1995;Schwefel 1987) in the NSGA-II and SPEA 2 algorithms in order to generate the approximated Pareto fronts for the considered downside risk-based portfolio optimization problems. In addition, the performance of the proposed algorithms is compared to that of other variants of the NSGA-II and SPEA 2 algorithms that have already been shown to be highly competitive in portfolio optimization problems.…”