Self-adaptive obtaining water-supply reservoir operation rules: Co-evolution artificial immune system

We investigated the complexity of reservoir operation and management as a complex adaptive system in this paper. Based on similarities between the process of extracting reservoirs operating rules and the selfadaptive learning behavior of antibody to antigens in the human immune system, a novel reservoir operating rule extraction architecture is proposed. By using the established co-evolution artificial immune system model (Co-EAISM), a case study of a single water-supply reservoir to provide water consumption for municipals, industries and agricultural irrigation is also presented. Twenty four rules are obtained eventually via Co-EAISM after 500 generations. It is demonstrated that they can identify 92.5% of the training samples and 84.4% of the testing samples, while obtaining the shortage index 2.23(10 m) between the predicted and practical release during the testing, which are beyond those by using Radius Basis Function (RBF) as a data mining technology for extracting water-supply reservoir operating rules. Three aspects of operating rule diversity, generality and non-linear division are discussed, considering behaviors, performances and impact factors of the Co-EAISM over the evolution. Through themodeling data and the presented case study, the proposedmodel has some benefits: (a) it is feasible and effective for self-adaptively extracting the reservoir operating rules to provide a novel route for reservoir operation management; (b) it can self-adaptively track the rules, adjust the population of the rules in corresponding to complex operation environment, and make reasonable release decisions; (c) it drives the rules diversity emergence to capturemany niches composed of the operating samples with similar operating attributes, to achieve the non-linear division of the operating samples in the binary space, which helps to acquire the spatial distributions of samples and gain the reservoir operation experience; (d) it can also explore the binary space to deal with subsequent complex changes of the operation environment via the character ‘‘#’’ in the schemas of the rules, and furthermore provide sufficient decision-making information in view of the physical meanings of the gene schemas contained in the rules. 2013 Elsevier Ltd. All rights reserved.