Abstract
Genetic algorithm (GA), evolutionary programming (EP) and evolutionary strategy (ES) are called the three kinds of evolutionary computation methods. They have been widely used in many engineering fields. However, selecting individuals directly and random search lead to produce premature problem, and requirement for high precision decreases the search efficiency, these become the obstructs of application in engineering practice. This paper proposes a new algorithm of evolutionary computation, it is called bio-simulated optimization algorithm (BSO). BSO reproduces new generation through asexual propagation and sexual propagation. Here, the evolutionary operators effectively solve the problem of premature convergence. Furthermore, performance of global search and convergence are proved theoretically. Finally, Compared BSO with GA and EP in searching the optimal solution of a continuous multi-peaks function, three kinds of computation procedures are run in Matlab, the result shows that performance of BSO is superior to GA and EP.
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© 2006 Springer-Verlag Berlin Heidelberg
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Wang, Y., Zhang, R., Pu, Q., Xiong, Q. (2006). A New Algorithm of Evolutionary Computation: Bio-Simulated Optimization. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing. ICIC 2006. Lecture Notes in Computer Science, vol 4113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816157_76
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DOI: https://doi.org/10.1007/11816157_76
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37271-4
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