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Research on Immune Algorithm of Subregional Caccine Extraction
Abstract
Based on the analysis of the flaw mechanism of tradition genetic algorithm and critically inheriting the immunity theory foundation, the paper proposes a new optimized algorithm - Subregion Artificial immunity Optimized Algorithm Based on Parallel, which can speed up the partially convergence and at the same time maintain the global convergence through the simulation of actual immunity behavior of organism. The basic thought of this algorithm is firstly to divide a complex question into several simple questions, secondly to carry out parallel "immunity" computation according to those simple questions, thirdly to pour the results (the antibodies) into the global immunity algorithm, which not only simplifies the complex question, but also raises the efficiency of the algorithm.
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The objective is to propose an immune distributed competitive
problem solver using MHC and an immune network and to verify its
validity by means of computer simulations. Our algorithm solves the
division-of-labor issues and problems for each agent work domain in a
multi-agent system (MPS) by two immune functions. First, the Major
Histocompatibility Complex (MHC) distinguishes “self” from
“nonself”, used in the process of eliminating states of
competition. Second, the immune network that produces specific
antibodies by modification of immune cells is used to produce adaptive
behaviors for agents. Then, to investigate the validity of the proposed
method, this algorithm is applied to the “N-th agent travelling
salesmen problem (n-TSP)” as a typical case problem for
multi-agent systems. The effectiveness of solving multi-agent system
problems in this way is clarified through some simulations
A new method employing the immune algorithm (IA) as the search
method for the shape optimization of an electromagnetic device is
presented. The method is applied to the shape optimization of a pole
face of an electromagnet. For the magnetic field analysis the finite
element method is used. It is shown that the proposed method with the IA
is very useful for the shape optimization of electromagnetic devices
The problem of protecting computer systems can be viewed generally as the problem of learning to distinguish self from other. We describe a method for change detection which is based on the generation of T cells in the immune system. Mathematical analysis reveals computational costs of the system, and preliminary experiments illustrate how the method might be applied to the problem of computer viruses. 1 Introduction The problem of ensuring the security of computer systems includes such activities as detecting unauthorized use of computer facilities, guaranteeing the integrity of data files, and preventing the spread of computer viruses. In this paper, we view these protection problems as instances of the more general problem of distinguishing self (legitimate users, corrupted data, etc.) from other (unauthorized users, viruses, etc.). We introduce a change-detection algorithm that is based on the way that natural immune systems distinguish self from other. Mathematical analysis ...
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