Improved Cultural Immune Systems to solve the economic load dispatch problems

This work considers Artificial Immune Systems to solve the economic load dispatch problem. The Immune Systems are based on the clonal selection principle. Cultural Algorithms using normative, situational, historical and topographical knowledge sources are used to improve the global optimization property of immune systems. A new main influence function is proposed which improves the results obtained by the cultural version. All the proposed approaches have several points of self-adaptation and use a local search operator that is based on a quasi-simplex technique. The Immune System and its cultural versions are validated in test problems that consider 13 and 40 thermal generators and take into account valve-point loading effects. They are also validated in a test problem with 20 thermal generators, valve-point loading effects and transmission losses. The proposed cultural method including the new influence function outperforms other modern metaheuristics reported in recent literature, finding the minimum fuel cost value for all test systems.

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