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Article title APPROACH TO RESEARCH ENVIRONS IN SWARMS ALGORITHM FOR SOLUTION OF OPTIMIZING PROBLEMS
Authors E.V. Kuliev, A.A. Legebokov, A.N. Dukkardt
Section SECTION I. EVOLUTIONARY MODELLING, GENETIC AND BIONIC ALGORITHMS
Month, Year 07, 2014 @en
Index UDC 004.82
DOI
Abstract The article discusses the key problem swarm algorithms and bioinspired approach, which consists in the definition of the proximity of solutions and study emerging neighborhoods for solving optimization problems. A detailed study is one of the most important tasks of the design phase of the design, namely the problem of component placement VLSI quality solutions which directly affects the quality tracing schemes and their heat, time, energy characteristics. The solution of the problems surrounding area and the proximity of solutions within them demonstrated by their research methods hybrid solutions. The technique of search neighborhoods in swarms algorithm, based on the principles of self- organization and a greedy approach. An important mechanism in the bee colony algorithm is a promising research decisions and positions of their neighborhoods in the solution space. The authors propose a new principle for the formation of neighborhood position in the solution space; it uses the concept of a circular neighborhood in the search space. We consider a bioinspired approach to the study of neighborhood solutions. The basic idea of this approach is the sequential operation of the genetic and evolutionary algorithms. In a method for finding optimal solutions bioinspired authors proposed an adaptive filter that rejects solutions with low value of the objective function. Experimental studies confirming that the computational and time complexity of the developed approach does not go beyond polynomial dependence. Research results allowed the authors to note that the genetic algorithm has more to place the random factor, and the evolutionary algorithm factor orientation.

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Keywords Swarm algorithm; genetic algorithm; evolutionary algorithm; "greedy" operator adaptation; neighborhood population.
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