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A New Approach on Many Objective Diversity Measurement Mostaghim, Sanaz Teich, Jürgen Multi-objective Optimization Particle Swarm Optimization 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). Schloss Dagstuhl – Leibniz-Zentrum für Informatik Sanaz Mostaghim and Jürgen Teich 2005 Is Part Of Dagstuhl Seminar Proceedings, Volume 4461, Practical Approaches to Multi-Objective Optimization (2005) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/DagSemProc.04461.4 urn:nbn:de:0030-drops-2543 https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.04461.4 eng https://creativecommons.org/licenses/by/4.0/legalcode