Abstract
This paper presents a novel and efficient method for solving economic load dispatch problems with non-smooth cost functions, by combining an Artificial Immune Systems with Cultural Algorithms. The proposed method, called Cultural Immune System, uses a real coded AIS that is derived from the clonal selection principle with a pure aging operator and hypermutation operators based on Gaussian and Cauchy mutations that are guided by four knowledge sources stored in the belief space of a Cultural Algorithm. The Cultural Immune System has a local search stage that is based on a quasi-simplex technique and several points of self-adaptation. Three test systems with thermal units whose fuel cost function takes into account valve-point loading effects are used to validate the proposed method. These test systems constitute complex constrained optimization problems. Firstly, Cultural Immune System is compared with his non-cultural counterpart (the same AIS without knowledge sources guiding the hypermutation operators). After that both immune-based methods are compared with state-of-the-art algorithms. The results show that the Cultural Immune System is capable of outperforming other state-of-the-art algorithms in solving load dispatch problems with the valve-point effect.
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Gonçalves, R.A., de Almeida, C.P., Delgado, M.R., Goldbarg, E.F., Goldbarg, M.C. (2007). A Cultural Immune System for Economic Load Dispatch with Non-smooth Cost Functions. In: de Castro, L.N., Von Zuben, F.J., Knidel, H. (eds) Artificial Immune Systems. ICARIS 2007. Lecture Notes in Computer Science, vol 4628. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73922-7_33
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DOI: https://doi.org/10.1007/978-3-540-73922-7_33
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