Abstract
In general, actual various decision making situations are formulated as large scale mathematical programming problems with many decision variables and constraints. For programming problems that decision variables take value 0 or 1 in mathematical programming problems, we can get strict solution by the application of dynamic programming fundamentally. However, a number of the solution that we should search becomes increases by leaps and bounds as the scale of the problem becomes large. In particular, for nonlinear 0-1 programming problems, there are not general strict solution method or approximate solution method, such as branch and bound method in case of linear 0-1 programming problems. In this research, focusing on nonlinear 0-1 programming problems, we propose an approximate solution method based on particle swarm optimization proposed by Kennedy et al. To be more specific, we develop a new particle swarm optimization method which is applicable to discrete optimization problems by incorporating the decoding algorithm.
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References
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Matsui, T., Sakawa, M., Kato, K. (2009). Particle Swarm Optimization Using the Decoding Algorithm for Nonlinear 0-1 Programming Problems. In: Håkansson, A., Nguyen, N.T., Hartung, R.L., Howlett, R.J., Jain, L.C. (eds) Agent and Multi-Agent Systems: Technologies and Applications. KES-AMSTA 2009. Lecture Notes in Computer Science(), vol 5559. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01665-3_67
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DOI: https://doi.org/10.1007/978-3-642-01665-3_67
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