An evolutionary algorithm for global optimization based on self-organizing maps. Issue 10 (2nd October 2016)

• Record Type: Journal Article
• Authors: Barmada, Sami; Raugi, Marco; Tucci, Mauro
• Is Part Of: Engineering optimization. Volume 48:Issue 10(2016)
• Page Start: 1740
• Page End: 1758
• Publication Date: 2016-10-02
• Subjects: self-organizing maps -- evolutionary algorithms -- global optimization -- particle-swarm optimization; Engineering design -- Periodicals; Mathematical optimization -- Periodicals; 620.0042
• DOI: 10.1080/0305215X.2015.1128424
• Languages: English
• ISSNs: 0305-215X

Abstract

Abstract : In this article, a new population-based algorithm for real-parameter global optimization is presented, which is denoted as self-organizing centroids optimization (SOC-opt). The proposed method uses a stochastic approach which is based on the sequential learning paradigm for self-organizing maps (SOMs). A modified version of the SOM is proposed where each cell contains an individual, which performs a search for a locally optimal solution and it is affected by the search for a global optimum. The movement of the individuals in the search space is based on a discrete-time dynamic filter, and various choices of this filter are possible to obtain different dynamics of the centroids. In this way, a general framework is defined where well-known algorithms represent a particular case. The proposed algorithm is validated through a set of problems, which include non-separable problems, and compared with state-of-the-art algorithms for global optimization.