Clone Selection Algorithm with Niching Strategy for Computer Immune System

A clone selection algorithm for computer immune system is presented. Clone selection principles in biological immune system are applied to the domain of computer virus detection. Based on the negative selection algorithm proposed by Stephanie Forrest, combining mutation operator in genetic algorithms and niching strategy in biology is adopted, the number of detectors is decreased effectively and the ability on self-nonself discrimination is improved. Simulation experiment shows that the algorithm is simple, practical and is adapted to the discrimination for long files.

An Improved Immune Clone Selection Algorithm for Parameters Optimization of Marine Electric Power System Stabilizer

In themarine electric power system,the marine generators will be disturbed by the large change of loads or the fault of the power system.The marine generators usually installed power system stabilizers to damp power system oscillations through the excitation control.This paper proposes a novel method to obtain optimal parameter values for Power System Stabilizer(PSS)to suppress low-frequency oscillations in the marine electric power system.In this paper,a newly developed immune clone selection algorithm was improved from the three aspects of the adaptive incentive degree,vaccination,and adaptive mutation strategies.Firstly,the typical PSS implementation type of leader-lag structure was adopted and the objective function was set in the optimization process.The performance of PSS tuned by improved immune clone selection algorithm was compared with PSS tuned by basic immune clone selection algorithm(ICSA)under various operating conditions and disturbances.Then,an improved immune clone selection algorithm(IICSA)optimization technique was implemented on two test systems for test purposes.Based on the simulations,it is found that an improved immune clone selection algorithm demonstrates superiority over the basic immune clone selection algorithm in getting a smaller number of iterations and fast convergence rates to achieve the optimal parameters of the power system stabilizers.Moreover,the proposed approach improves the stability and dynamic performance under various loads conditions and disturbances of the marine electric power system.

A new improved Alopex-based evolutionary algorithm and its application to parameter estimation

In this work, focusing on the demerit of AEA (Alopex-based evolutionary algorithm) algorithm, an improved AEA algorithm (AEA-C) which was fused AEA with clonal selection algorithm was proposed. Considering the irrationality of the method that generated candidate solutions at each iteration of AEA, clonal selection algorithm could be applied to improve the method. The performance of the proposed new algorithm was studied by using 22 benchmark functions and was compared with original AEA given the same conditions. The experimental results show that the AEA-C clearly outperforms the original AEA for almost all the 22 benchmark functions with 10, 30, 50 dimensions in success rates, solution quality and stability. Furthermore, AEA-C was applied to estimate 6 kinetics parameters of the fermentation dynamics models. The standard deviation of the objective function calculated by the AEA-C is 41.46 and is far less than that of other literatures' results, and the fitting curves obtained by AEA-C are more in line with the actual fermentation process curves.

Image segmentation based on differential immune clone clustering algorithm

Purpose–The purpose of this paper is to present a Differential Immune Clone Clustering Algorithm(DICCA)to solve image segmentation.Design/methodology/approach–DICCA combines immune clone selection and differential evolution,and two populations are used in the evolutionary process.Clone reproduction and selection,differential mutation,crossover and selection are adopted to evolve two populations,which can increase population diversity and avoid local optimum.After extracting the texture features of an image and encoding them with real numbers,DICCA is used to partition these features,and the final segmentation result is obtained.Findings–This approach is applied to segment all sorts of images into homogeneous regions,including artificial synthetic texture images,natural images and remote sensing images,and the experimental results show the effectiveness of the proposed algorithm.Originality/value–The method presented in this paper represents a new approach to solving clustering problems.The novel method applies the idea two populations are used in the evolutionary process.The proposed clustering algorithm is shown to be effective in solving image segmentation.