A New Approach on Many Objective Diversity Measurement
Sanaz Mostaghim, Jürgen Teich · Repository for Publications and Research Data (ETH Zurich) · 2005
In multi-objective particle swarm optimization (MOPSO) methods, selecting the best {it local guide} (the global best particle) for each particle of the population from a set of Pareto-optimal solutions has a great impact on the convergence and diversity of solutions, especially when optimizing problems with high number of objectives. here, we introduce the Sigma method as a new method for finding best local guides for each particle of the population. The Sigma method is implemented and is compared with another method, which uses the strategy of an existing MOPSO method for finding the local guides. These methods are examined for different test functions and the results are compared with the results of a multi-objective evolutionary algorithm (MOEA).