OPTIMIZATION METHOD BASED ON THE SYNTHESIS OF CLONAL SELECTION AND ANNEALING SIMULATION ALGORITHMS
DOI:
https://doi.org/10.15588/1607-3274-2019-2-10Keywords:
metaheuristics, clonal selection, annealing simulation, optimization, technology of information parallel processing.Abstract
Context. The problem of increasing the efficiency of optimization methods by synthesizing metaheuristics is considered. The object
of the research is the process of finding a solution to optimization problems.
Objective. The goal of the work is to increase the efficiency of searching for a quasi-optimal solution at the expense of a metaheuristic
method based on the synthesis of clonal selection and annealing simulation algorithms.
Method. The proposed optimization method improves the clonal selection algorithm by dynamically changing based on the annealing
simulation algorithm of the mutation step, the mutation probability, the number of potential solutions to be replaced. This
reduces the risk of hitting the local optimum through extensive exploration of the search space at the initial iterations and guarantees
convergence due to the focus of the search at the final iterations. The proposed optimization method makes it possible to find a conditional
minimum through a dynamic penalty function, the value of which increases with increasing iteration number. The proposed
optimization method admits non-binary potential solutions in the mutation operator by using the standard normal distribution instead
of the uniform distribution.
Results. The proposed optimization method was programmatically implemented using the CUDA parallel processing technology
and studied for the problem of finding the conditional minimum of a function, the optimal separation problem of a discrete set, the
traveling salesman problem, the backpack problem on their corresponding problem-oriented databases. The results obtained allowed
to investigate the dependence of the parameter values on the probability of mutation.
Conclusions. The conducted experiments have confirmed the performance of the proposed method and allow us to recommend it
for use in practice in solving optimization problems. Prospects for further research are to create intelligent parallel and distributed
computer systems for general and special purposes, which use the proposed method for problems of numerical and combinatorial
optimization, machine learning and pattern recognition, forecast.
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Copyright (c) 2019 O. O. Grygor, E. E. Fedorov, T. Y. Utkina, A. G. Lukashenko, K. S. Rudakov K. S., D. A. Harder, V. M. Lukashenko
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