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Article title GENETIC MODIFICATION OF PROBABILISTIC ALGORITHM FOR PLACING ELEMENTS OF ECE UNDER EMC
Authors M. A. Zhilenkov, V. V. Kureichik
Section SECTION I. DESIGN AUTOMATION
Month, Year 07, 2017 @en
Index UDC 004.896
DOI
Abstract The solution of one of the important problems of design, the problem of electronic computing equipment (ECE) placement, is suggested. In solving this problem the criterion of electromagnetic compatibility (EMC) is selected as the basic one. Due to the big data processing the placement problem belongs to NP-complex and NP-difficult class of problem. The statement and the mathematical model of the placement problem are made, taking into account the electromagnetic compatibility criterion. A productive modification of the probabilistic genetic algorithm with Pareto set forecast based on the Strength Pareto Evolutionary Algorithm (SPEA2) method is proposed, which allows reducing the computing resource in comparison with the analogues algorithms. The function of assessing the quality of the obtained solutions is defined. Also, there described are the features of its calculation on the basis of dominating of alternative solutions. A modified procedure for updating the archive of the pareto-optimal solutions is developed. Also, features of calculation of the target fitness function based on the dominance of individuals and the mechanism for sorting by niches are described. A method for estimating the density and scatter function of points in a set of solutions is developed with a certain ratio of genetic reproduction, crossing-over, mutation operators, which has made it possible to reduce computational costs. The software is developed. Computational experiments are carried out on the basis of benchmarks and shows that the given algorithm allows obtaining optimal and quazi-optimal solutions in polynomial time. Time complexity of the developed algorithm is represented as O(nlogn) in the best case and О(n3) in the worst case.

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Keywords Electromagnetic compatibility; Accommodation; Probabilistic genetic algorithm; The Pareto set; Pareto-domination; SPEA2.
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