Applications of immune and clonal selection-based techniques to distribution system optimal operational planning
Dipt. di Ingegneria Elettrica, Politecnico di Torino, TurinDOI: 10.1109/MELCON.2006.1653273 Conference: Electrotechnical Conference, 2006. MELECON 2006. IEEE Mediterranean
Source: IEEE Xplore
This paper presents a set of results obtained by using immune and clonal selection techniques to enhance the performance of genetic algorithms in solving the optimal operational planning of distribution systems. Various strategies and variants are illustrated and discussed, and the most suitable strategies, able to provide better results with respect to the ones obtained so far for the same problem, are identified. Significant results are presented for a large real urban MV distribution system
Article: Clonal Selection Algorithms[Show abstract] [Hide abstract]
ABSTRACT: Inspired by Darwin's theory of natural selection to explain the diversity and adaptability of life, Burnet's clonal selection theory explains the diversity and learning properties of the acquired immune system of vertebrates. In a similar mirroring manner to the field of evolutionary computation that attempts to use the principles of the Darwinian theory and genetics to address practical engineering problems, a new field of study called 'Clonal Selection Algorithms' has emerged that attempts the same task by abstracting and applying the principles of Burnet's foundational immunological theory. This paper provides a summary of this new field of clonal selection algorithms and proposes an algorithm taxonomy, a standardized nomenclature, and a general model of such algorithms. Finally, the field is compared and contrasted to the field of evolutionary computation, and general research trends are discussed. Artificial Immune Systems (AIS) is the investigation of models and abstractions of the vertebrate (typically mammalian) immune system and the application of these models and algorithms to practical endeavours such computation problem domains in the fields of science, engineering, and information technology (88). Although the source of inspiration for computational models in the immune system is near limitless, four main sub fields of research have emerged in AIS cantered on prominent immunological theories; negative selection algorithms (NSA), immune network algorithms (INA), danger theory algorithms (DTA), and clonal selection algorithms (CSA).
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