Abstract
Many algorithms for multiobjective optimization have been proposed in the last years. In the recent past a great importance have the MOEAs able to solve problems with more than two objectives and with a large number of decision vectors (space dimensions). The diffculties occur when problems with more than three objectives (higher dimensional problems) are considered. In this paper, a new algorithm for multiobjective optimization called Multiobjective Adaptive Representation Evolutionary Algorithm (MAREA) is proposed. MAREA combines an evolution strategy and an steady-state algorithm. The performance of the MAREA algorithm is assessed by using several well-known test functions having more than two objectives. MAREA is compared with the best present day algorithms: SPEA2, PESA and NSGA II. Results show that MAREA has a very good convergence.
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Groşan, C. (2006). Multiobjective Adaptive Representation Evolutionary Algorithm (MAREA) - a new evolutionary algorithm for multiobjective optimization. In: Abraham, A., de Baets, B., Köppen, M., Nickolay, B. (eds) Applied Soft Computing Technologies: The Challenge of Complexity. Advances in Soft Computing, vol 34. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31662-0_9
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DOI: https://doi.org/10.1007/3-540-31662-0_9
Publisher Name: Springer, Berlin, Heidelberg
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