Abstract
This paper addresses the problem of finding several different solutions with the same optimum performance in single objective real-world engineering problems. In this paper a parallel robot design is proposed. Thereby, this paper presents a genetic algorithm to optimize uni-objective problems with an infinite number of optimal solutions. The algorithm uses the maximin concept and ε-dominance to promote diversity over the admissible space. The performance of the proposed algorithm is analyzed with three well-known test functions and a function obtained from practical real-world engineering optimization problems. A spreading analysis is performed showing that the solutions drawn by the algorithm are well dispersed.
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Pires, E.J.S., Mendes, L., Lopes, A.M., de Moura Oliveira, P.B., Machado, J.A.T. (2013). Single-Objective Spreading Algorithm. In: Madureira, A., Reis, C., Marques, V. (eds) Computational Intelligence and Decision Making. Intelligent Systems, Control and Automation: Science and Engineering, vol 61. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4722-7_13
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DOI: https://doi.org/10.1007/978-94-007-4722-7_13
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