Artificial Immune Systems (AIS) are algorithms inspired by the human immune system. The human immune system is a robust, decentralized, error tolerant and adaptive system. Such properties are highly desirable for the development of novel computer systems. Unlike some other bio-inspired techniques, such as genetic algorithms and neural networks, the field of AIS encompasses a spectrum of algorit...

This paper proposes a hybrid optimization method based on the fusion of the Simulated Annealing (SA) and Clonal Selection Algorithm (CSA), in which the SA is embedded in the CSA to enhance its search capability. The novel optimization algorithm is also employed to deal with several nonlinear benchmark functions as well as a practical engineering design problem. Simulation results demonstrate th...

In this paper, based on the fusion of the clonal selection algorithm (CSA) and differential evolution (DE) method, we propose a novel optimization scheme: CSA–DE. The DE is employed here to improve the affinities of the clones of the antibodies (Abs) in the CSA. Several nonlinear functions are used to verify and demonstrate the effectiveness of our hybrid optimization approach. It is further ap...

In this research work a large set of the classical numerical functions were taken into account in order to understand both the search capability and the ability to escape from a local optimal of a clonal selection algorithm, called i-CSA. The algorithm was extensively compared against several variants of Differential Evolution (DE) algorithm, and with some typical swarm intelligence algorithms....

In this paper, an Evolutionary Qualitative Model Learning Framework (EQML) is proposed and tested by learning the qualitative metabolic models under the condition of incomplete knowledge. JMorven, a fuzzy qualitative reasoning engine, is slightly modified and integrated into the framework as a sub module to represent and verify the learnt models. Three metabolic compartment models are tested by...

The interest of hybridizing different nature inspired algorithms has been growing in recent years. As a relatively new algorithm in this field, Biogeography Based Optimization(BBO) shows great potential in solving numerical optimization problems and some practical problems like TSP. In this paper, we proposed an algorithm which combines Biogeography Based Optimization (BBO) and Clonal Selection...

This paper presents a mathematical proof of convergence of a multiobjective artificial immune system algorithm (based on clonal selection theory). An specific algorithm (previously reported in the specialized literature) is adopted as a basis for the mathematical model presented herein. The proof is based on the use of Markov chains.