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The purpose of this paper is to propose an extended immune optimization algorithm using division as well as integration processing
based on immune cell-cooperation and to investigate its validity by computer simulations. In the biological immune system,
the immune cell-cooperation is a framework including MHC and immune network, the function of whi...
Citations
Recently, a number of parallelized optimization algorithms have been proposed. We have proposed a co-evolutionary immune algorithm (IA) to solve the division-of-labor problems, in particular the n-th agents travelling salesman problem (n-TSP). In this article, we extend the co-evolutionary IA for a large-scale n-TSP with (1) an improvement for the search speed through parallelized search on the PC-cluster, and (2) the introduction of a new division-processing pre-estimated division processing to improve the search ability. Some computational experiments show the proposed method can obtain better quality solutions for division-of-labor problems, and present an applicable parameter cofinguration.
The objective of this paper is to propose an evolutionary
optimization algorithm using MHC and 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's work domain in a
multi-agent system (MAS) by two immune functions. First, the major
histocompatibility complex (MHC) distinguishes a "self" from the other
"non-self", 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's travelling salesman problem
(called n-TSP)" as a typical case problem of multi-agent system. The
effectiveness of solving MAS is clarified through sets of simulations
