Abstract
By the penalty function method we transform zero-one nonlinear programming problems into unconstrained zero-one integer optimization problems. A particle swarm optimization algorithm with chaos and gene density mutation is given to solve unconstrained the zero-one nonlinear program problems. We use chaos to initialize populations and use the 0-1 integer operation in updating positions to produce 0-1 integer points. We use the fitness variance and gene density strategy to determine whether the population premature phenomenon or not. If it appears that we use the gene density mutation to increase the population diversity or restart and reset the population by chaos technique. Numerical simulations show that the proposed algorithm for most test functions is feasible, effective and has high precision.
The work is supported by the National Natural Science Foundation of China under Grant No.60962006 and the Higher School Research Projects of Ningxia under Grant No.2009JY008.
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Gao, Y., Lei, F., Li, H., Li, J. (2011). PSO Algorithm with Chaos and Gene Density Mutation for Solving Nonlinear Zero-One Integer Programming Problems. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds) Advances in Swarm Intelligence. ICSI 2011. Lecture Notes in Computer Science, vol 6728. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21515-5_13
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DOI: https://doi.org/10.1007/978-3-642-21515-5_13
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