Existing artificial immune optimization algorithms reflect a number of shortcomings, such as premature convergence and poor local search ability. This paper proposes a danger-theory-based immune network optimization algorithm, named dt-aiNet. The danger theory emphasizes that danger signals generated from changes of environments will guide different levels of immune responses, and the areas aro...

Artificial bee colony (ABC) algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA), artificial colony optimization (ACO), and particle swarm optimization (PSO). However, the ABC still has some limitations. For example, ABC can easily get trapped ...

Artificial Immune System is applied to determine the optimal hourly schedule of power generation in a hydrothermal system. A multi-reservoir cascaded hydroelectric system with a nonlinear relationship between water discharge rate, net head and power generation is considered. The water transport delay between connected reservoirs is taken into account. The transmission losses are also taken into...

Artificial Immune System is applied to determine the optimal hourly schedule of power generation in a hydrothermal system. A multi-reservoir cascaded hydroelectric system with a nonlinear relationship between water discharge rate, net head and power generation is considered. The water transport delay between connected reservoirs is taken into account. The transmission losses are also taken into...

no unique method has been so far specified for determining the number of neurons in hidden layers of multi-layer perceptron (mlp) neural networks used for prediction. the present research is intended to optimize the number of neurons using two meta-heuristic procedures namely genetic and hill climbing algorithms. the data used in the present research for prediction are consumption data of water...

The paper introduces a speed boosting extension to a novel induction of fuzzy rules from raw data using Artificial Immune System methods. The modified algorithm was experimentally proved to be several times faster than

This paper presents an agent-based artificial immune system approach for adaptive damage detection in distributed monitoring networks. The presented approach establishes a new monitoring paradigm by embodying desirable immune attributes, such as adaptation, immune pattern recognition, and selforganization, into monitoring networks. In the artificial immune system-based paradigm, a group of auto...

In this paper we present an Abstract Immune System Algorithm, based on the model introduced by Farmer et al, inspired on the theory of Clonal Selection and Idiotypic Network due to Niels Jerne. The proposed algorithm can be used in order to solve problems much in the way that Evolutionary Algorithms or Artificial Neural Networks do. Besides presenting the Algorithm itself, we briefly discuss it...