Global–local population memetic algorithm for solving the forward kinematics of parallel manipulators
Memetic algorithms (MA) are evolutionary computation methods that employ local search to selected individuals of the population. This work presents global–local population MA for solving the forward kinematics of parallel manipulators. A real-coded generation algorithm with features
of diversity is used in the global population and an evolutionary algorithm with parent-centric crossover operator which has local search features is used in the local population. The forward kinematics of the 3RPR and 6–6 leg manipulators are examined to test the performance of the
proposed method. The results show that the proposed method improves the performance of the real-coded genetic algorithm and can obtain high-quality solutions similar to the previous methods for the 6–6 leg manipulator. The accuracy of the solutions and the optimisation time achieved
by the methods in this work motivates for real-time implementation of the 3RPR parallel manipulator.
Keywords: forward kinematics of parallel manipulators; intensification and diversification; memetic algorithms; real-coded genetic algorithm
Document Type: Research Article
Affiliations: 1: School of Computing, Information and Mathematical Sciences, Faculty of Science, Technology and Environment, University of the South Pacific, Suva, Fiji 2: Memorial University, Faculty of Engineering, St-John's, Canada
Publication date: 02 January 2015
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