Hybridizing differential evolution and local search optimization for dimensional synthesis of linkages

This work deals with the optimal dimensional synthesis of planar linkages. The paper proposes a new search procedure to obtain the best linkage by means of hybridizing a Local Search (LS) approach and an Evolutionary Algorithm (EA), Hybrid Algorithms (HAs) combine the advantages of stochastic and deterministic optimization, enhancing their features in the search for the best solution. However, the way in which the integration of these two kinds of algorithms should be implemented is not a trivial issue and many options exist that have to be investigated. In this paper different schemes of hybrid algorithms are applied to the synthesis of planar linkages. The work discusses different approaches that use information and structures for achieving a balance between global and local searches. Differential Evolution (DE) is used together with the Generalized Reduced Gradient (GRG) approach for optimal search. Both Cluster and Elite Analyses are studied to select an effective hybrid optimization strategy. The different schemes of hybrid algorithms are presented and analyzed in different examples for the design of planar linkages and the best options are highlighted. (C) 2019 Elsevier Ltd. All rights reserved.