Mind Evolutionary Computation Co-algorithm for Optimizing Optical Systems

Sakharov, Maxim; Houllier, Thomas; Lépine, Thierry

doi:10.1007/978-3-030-50097-9_48

Cited by 6 publications

(1 citation statement)

References 9 publications

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“…In this work, we incorporated the following changes into the multipopulation SMEC algorithm: a step adaptation strategy [35], projection onto a search domain technique to handle constraints [36], and a smoothing method to producing control strategies that can 6 of 14 be put into practice [17]. For the purpose of parallelization, the master-slave paradigm was used [18].…”

Section: Parallel Mind Evolutionary Computation Algorithmsmentioning

confidence: 99%

Studying the Efficiency of Parallelization in Optimal Control of Multistage Chemical Reactions

et al. 2022

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In this paper, we investigate the problem of optimal control of complex multistage chemical reactions, which is considered a nonlinear global constrained optimization problem. This class of problems is computationally expensive due to the inclusion of multiple parameters and requires parallel computing systems and algorithms to obtain a solution within a reasonable time. However, the efficiency of parallel algorithms can differ depending on the architecture of the computing system. One available approach to deal with this is the development of specialized optimization algorithms that consider not only problem-specific features but also peculiarities of a computing system in which the algorithms are launched. In this work, we developed a novel parallel population algorithm based on the mind evolutionary computation method. This algorithm is designed for desktop girds and works in synchronous and asynchronous modes. The algorithm and its software implementation were used to solve the problem of the catalytic reforming of gasoline and to study the parallelization efficiency. Results of the numerical experiments are presented in this paper.

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