A Multi-Learning Immune Algorithm for Numerical Optimization

Abstract

SUMMARYThe emergence of nature-inspired algorithms (NIA) is a great milestone in the field of computational intelligence community. As one of the NIAs, the artificial immune algorithm (AIS) mimics the principles of the biological immune system, and has exhibited its effectiveness, implicit parallelism, flexibility and applicability when solving various engineering problems. Nevertheless, AIS still suffers from the issues of evolution premature, local minima trapping and slow convergence due to its inherent stochastic search dynamics. Much effort has been made to improve the search performance of AIS from different aspects, such as population diversity maintenance, adaptive parameter control, etc. In this paper, we propose a novel multi-learning operator into the AIS to further enrich the search dynamics of the algorithm. A framework of embedding multiple commonly used mutation operators into the antibody evolution procedure is also established. Four distinct learning operators including baldwinian learning, cauchy mutation, gaussian mutation and lateral mutation are selected to merge together as a multi-learning operator. It can be expected that the multi-learning operator can effectively balance the exploration and exploitation of the search by enriched dynamics. To verify its performance, the proposed algorithm, which is called multi-learning immune algorithm (MLIA), is applied on a number of benchmark functions. Experimental results demonstrate the superiority of the proposed algorithm in terms of convergence speed and solution quality.

Immune algorithm combined with estimation of distribution for traveling salesman problem

“…It is a population-based problem-solving technique and mimics the mechanisms of the biological immune response, which depicts the procedures of responses when a biological immune system is exposed to an antigen. The most commonly used mechanisms of the biological immune system are clonal selection proliferation, negative selection, immune network, danger theory, and dendritic cell model [50,51]. Among them, the clonal selection algorithm is a special class of IA, and it is inspired by the clonal selection principle.…”

An Adaptive Elastic Net Method for Edge Linking of Images

“…However, as a penalty method, the Elastic Net always suffers from a parameter tuning problem whenever applied to traveling salesman problems, of course a fundamental drawback of this type of networks (Stone, 1992;Jiahai Wang et al, 2003). Furthermore, as a gradient decent algorithm, the Elastic Net method attempts to take the best path to the nearest minimum, whether global or local.…”

“…Furthermore, as a gradient decent algorithm, the Elastic Net method attempts to take the best path to the nearest minimum, whether global or local. So it is always confronted with a local minimum problem (Jiahai Wang et al, 2003;Durbin et al, 1989). In addition, while it generates reasonably good solutions, running time is long.…”