Abstract
TAn effective dynamical multi-objective evolutionary algorithm (DMOEA) based on the principle of the minimal free energy in thermodynamics was proposed in the paper. It provided a new fitness assignment strategy based on the principle of free energy minimization of thermodynamics for the convergence of solves, introduced a density-estimate technique for evaluating the crowding distance between individuals and a new criterion for selection of new individuals to maintain the diversity of the population. By using multi-crossover operator, it improved the search efficiency and the robustness. The test example results proves the validity of the algorithm in its rapidly convergence and maintaining diversity.
TThis work was supported by Chinese National Natural Science Foundation grant No. 60573168 and 50677046.
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LiuP, M., ZouP, X., KangP, L. (2007). An Effective Dynamical Multi-objective Evolutionary Algorithm for Solving Optimization Problems with High Dimensional Objective Space. In: Kang, L., Liu, Y., Zeng, S. (eds) Advances in Computation and Intelligence. ISICA 2007. Lecture Notes in Computer Science, vol 4683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74581-5_9
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