Niche Clona Selection Algorithm

Many practical problems can be formulated as problems of optimizing functions for which there are numerous heuristic algorithms so far. The genetic algorithm is one of such algorithms. However, the development and applications of the genetic algorithm are to certain extent limited by its premature convergence and low convergent speed that occur frequently in applications of this algorithm. But the immune system is a distributed, self-adapted complex system. Form such a system, useful strategies can be found to solve problems in engineering and sciences. And it is a significant subject to design effective algorithms for optimization problems by simulating biological immune activities.In this paper, we first review the development of the evolutionary algorithms, in particular of the genetic algorithm. Following this, we describe the basic principles of natural immune systems, the artificial immune system and various kinds of immune algorithms. Among these principles, the clonal selection principle is the most important for the artificial immune system. The immune algorithm can be derived from this principle and used to solve the problems of optimizing functions in an effective way. Finally, on the basis of analyzing the advantages and disadvantages and by referring to the natural sharing niche mechanism, we present for the clonal selection algorithm an improved algorithm called niche clonal selection algorithm.The niche clonal selection algorithm is an algorithm that redesigns the evaluation function with regard to "peak leaking" in the clonal selection algorithm. This paper will first introduce the sharing function to determine species similarity between the individuals of a community, and then use this function as a basis to design the evaluation function in place of the original simple evaluation function that is based on the fitness value as its sole standard. For the individuals of the community that gather into a scrap, the fitness value is reduced by applying the sharing function to penalize the scrap; thus the probability of selection is enhanced for this small-scale species, and is greater than the probability when the original adaptation value is shared. Hence it is possible for the small-scale species with low fitness to survive and to enter into the next generation smoothly. In this way, the niche technology will increase the diversity of the species and cause community to evolve in the direction of individual distributions with high qualities.Finally, after the experiment, it indicates that, compared with the standard genetic algorithm and the clonal selection algorithm, the improvement algorithm has the superiority of rapid convergence, strong ability for overall situation optimization and increase in population diversity. In view of some steps and parameter, it will give reasonable adjustment counting on the experiment result.In sum, optimization is an old and difficult problem. And the nature abounds with effective information-processing mechanisms. Therefore, we can simulate the principle and mechanism of natural evolution and the process of the development of biological intelligence in order to solve problems and take a step further to integrate the principles and techniques from mathematics, biology, and computer science so that the algorithms designed are more effective. This is one of the focuses in the international research on computer intelligence. We will combine the biological immune and the niche technology to develop new models for algorithms in this paper. And in the future, the theoretical bases will be explored and the applications will be extended.