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The artificial immune system approach for self-nonself discrimination and its application to anomaly detection problems in engineering is showing great promise. A seminal contribution in this area is the V-detectors algorithm that can very effectively cover the nonself region of the feature space with a set of detectors. The detector set can be used to detect anomalous inputs. In this paper, a multistage approach to create an effective set of V-detectors is considered. The first stage of the algorithm generates an initial set of V-detectors. In subsequent stage, new detectors are grown from existing ones, by means of a mechanism called procreation. Procreating detectors can more effectively fill hard-to-reach interstices in the nonself region, resulting in better coverage. The effectiveness of the algorithm is first illustrated by applying it to a well-known fractal, the Koch curve. The algorithm is then applied to the problem of detecting anomalous behavior in power distribution systems, and can be of much use for maintenance-related decision-making in electrical utility companies.

Particle swarm optimization is a stochastic algorithm for optimizing continuous functions. It uses a population of particles that follow trajectories through the search space towards good optima. This paper proposes adding a local search component to PSO to improve its convergence speed. Two possible methods are discussed. The first adds a term containing estimated gradient information to the velocity of each particle. The second explicitly incorporates the Nelder-Mead algorithm, a known local search technique, within PSO. The suggested methods have been applied to the problem of estimating parameters of a gene network model. Results indicate the effectiveness of the proposed strategies.