Neighborhood research approach in swarm intelligence for solving the optimization problems

Kuliev, Elmar; Dukkardt, A.N.; Kureychik, Victor; Legebokov, A.A.

doi:10.1109/ewdts.2014.7027084

Cited by 13 publications

(3 citation statements)

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“…The set of objects s  S defined by the bioinspired search algorithm. The evolution of the set Z corresponds to the evolution of the population S. The subset P0 is a random initial population defined on the S. At each step the solution is calculated in the following way: Pt+1 = A(Pt), where A is a combination of various evolutionary operators [8].The combination of evolutionary operators A defines the optimality criterion, which also calculated at each step.…”

Section: Problem Formulationmentioning

confidence: 99%

“…At the same there is a conflict between complexity and requirements to the effective decision making in a real time. These problems cannot be completely solved by decision making parallelization, increasing number of operators, users or decision making persons (DMP) [5][6][7][8].For this reason we suggest to use new technologies at the interface between the informatics and biological cybernetics.…”

Section: Introductionmentioning

confidence: 99%

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Problem-oriented knowledge processing on the basis of hybrid approach

Kuliev¹,

Kravchenko²,

Kulieva³

et al. 2016

Proceedings of the 2016 Conference on Information Technologies in Science, Management, Social Sphere and Medicine

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The paper discusses a hybridization of adaptation mechanisms with self-organization in order to process problemoriented knowledge. The core of the proposed method consists in sequential execution of bionic and genetic algorithms. To demonstrate the hybrid algorithm we gave an example of a solution search. In the paper it is presented a formulation of the problem-oriented knowledge processing task during the optimal solution search. To solve this problem we developed a modified architecture of the hybrid search. The suggested architecture involves principal components of a bioinspired search. To improve the algorithm performance we provide an opportunity to add new algorithms with an evolutionary adaptation module and environment module. . Conducted experiments confirmed that time complexity of the developed approach is polynomial.

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Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Problem-oriented knowledge processing on the basis of hybrid approach

Kuliev¹,

Kravchenko²,

Kulieva³

et al. 2016

Proceedings of the 2016 Conference on Information Technologies in Science, Management, Social Sphere and Medicine

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show abstract

“…Эволюционные алгоритмы применяются для решения множества практических задач, таких как составление расписаний в промышленности [15,16], оптимизация разводки печатных плат [17][18][19][20], построение систем управления [21][22][23][24][25][26][27], сегментирование изображений [28], а также приближенного решения других задач комбинаторной [29][30][31][32] и вещественной оптимизации [33][34][35].…”

Section: Introductionunclassified

The “One-fifth Rule” with Rollbacks for Self-Adjustment of the Population Size in the (1 + (λ,λ)) Genetic Algorithm

Басин

Буздалов

Шалыто

2020

Model. anal. inf. sist.

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Self-adjustment of parameters can significantly improve the performance of evolutionary algorithms. A notable example is the (1 + (λ,λ)) genetic algorithm, where adaptation of the population size helps to achieve the linear running time on the OneMax problem. However, on problems which interfere with the assumptions behind the self-adjustment procedure, its usage can lead to the performance degradation. In particular, this is the case with the “one-fifth rule” on problems with weak fitness-distance correlation.We propose a modification of the “one-fifth rule” in order to have less negative impact on the performance in the cases where the original rule is destructive. Our modification, while still yielding a provable linear runtime on OneMax, shows better results on linear function with random weights, as well as on random satisfiable MAX-3SAT problems.

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