Chaotic Multi-Objective Particle Swarm Optimization Algorithm Incorporating Clone Immunity

It is generally known that the balance between convergence and diversity is a key issue for solving multi-objective optimization problems. Thus, a chaotic multi-objective particle swarm optimization approach incorporating clone immunity (CICMOPSO) is proposed in this paper. First, points in a non-dominated solution set are mapped to a parallel-cell coordinate system. Then, the status of the particles is evaluated by the Pareto entropy and difference entropy. At the same time, the algorithm parameters are adjusted by feedback information. At the late stage of the algorithm, the local-search ability of the particle swarm still needs to be improved. Logistic mapping and the neighboring immune operator are used to maintain and change the external archive. Experimental test results show that the convergence and diversity of the algorithm are improved.

The Application of Immune Genetic Algorithm in Parallel Sorting

Taking an immune operator added to the framework of the genetic algorithm, individuals vaccinated to further enhance the survival ability of the individual, the immune genetic algorithm as a set of immune mechanisms and evolutionary mechanisms in one of the new evolutionary algorithm is applied to reverse genome rearrangement problem is a new attempt, the genetic algorithm can be avoided premature shortcomings, while the immune genetic algorithm parallelization can improve convergence speed.

Fault Diagnosis of Oil-Immersed Transformers Using Self-Organization Antibody Network and Immune Operator

There are some drawbacks when diagnosis techniques based on one intelligent method are applied to identify incipient faults in power transformers. In this paper, a hybrid immune algorithm is proposed to improve the reliability of fault diagnosis. The proposed algorithm is a hybridization of self-organization antibody network (soAbNet) and immune operator. There are two phases in immune operator. One is vaccination, and the other is immune selection. In the process of vaccination, vaccines were obtained from training dataset by using consistency-preservingK-means algorithm (K-means-CP algorithm) and were taken as the initial antibodies for soAbNet. After the soAbNet was trained, immune selection was applied to optimize the memory antibodies in the trained soAbNet. The effectiveness of the proposed algorithm is verified using benchmark classification dataset and real-world transformer fault dataset. For comparison purpose, three transformer diagnosis methods such as the IEC criteria, back propagation neural network (BPNN), and soAbNet are utilized. The experimental results indicate that the proposed approach can extract the dataset characteristics efficiently and the diagnostic accuracy is higher than that obtained with other individual methods.

Application of Immune Genetic Algorithm in Survey Data Processing

The immune genetic algorithm is a kind of heuristic algorithm which simulates the biological immune system and introduces the genetic operator to its immune operator. Conquering the inherent defects of genetic algorithm that the convergence direction can not be easily controlled so as to result in the prematureness；it is characterized by a better global search and memory ability. The basic principles and solving steps of the immune genetic algorithm are briefly introduced in this paper. The immune genetic algorithm is applied to the survey data processing and experimental results show that this method can be practicably and effectively applied to the survey data processing.