Abstract
The main aim of the research is the development of effective methods and algorithms based on the hybrid principles functioning of the immune system and evolutionary search to determine a global optimal solution to optimisation problems. Artificial immune algorithms are characterised as diverse ones, extremely reliable and implicitly parallel. The integration of modified evolutionary algorithms and immune algorithms is proposed to be used for the solution of above problem. There is no exact method for the efficient solving unclear optimisation problems within the polynomial time. However, by determining close to optimal solutions within the reasonable time, the hybrid immune algorithm (HIA) is capable to offer multiple solutions, which provide compromise between several goals. Quite few researches have been focused on the optimisation of more than one goal and even fewer used to have distinctly considered diversity of solutions that plays fundamental role in good performance of any evolutionary calculation method.
Highlights
The existing application of artificial immune algorithms (AIA) for optimisation problems is usually studied as a competitive adaptive mechanism for the artificial neural networks, in which the adaptation mechanism becomes active in case of approaching threat followed with the further output accumulation
This research studies the symbiosis of the artificial immune algorithms, based on the bionic search [1]
The given research represents a hybrid immune algorithm to solve the problem of finding the minimum cost of the optimal flow of transport network, which is a symbiosis of the modified genetic and immune algorithms and operators allowing us to control the adaptation parameters by executing the algorithm, taking into account the influence of possible changes in the boundary conditions
Summary
The existing application of artificial immune algorithms (AIA) for optimisation problems is usually studied as a competitive adaptive mechanism for the artificial neural networks, in which the adaptation mechanism becomes active in case of approaching threat followed with the further output accumulation. Artificial immune algorithms are used to solve the problems connected with the anomalies identification and detection. This research studies the symbiosis of the artificial immune algorithms, based on the bionic search [1]. There are certain advantages of artificial immune algorithms to bionic search systems as the follows: wide variety, high reliability, implicit parallelism. At the same time there are some disadvantages of AIA: training complexity, difficulties by choosing offspring for cloning and mutation, multiplicity of initial parameters at the start of functioning algorithm, determination of parent chromosomes to form offsprings, etc
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