Parallel Multi-memetic Global Optimization Algorithm for Optimal Control of Polyarylenephthalide’s Thermally-Stimulated Luminescence

Sakharov, Maxim; Karpenko, Anatoly

doi:10.1007/978-3-030-21803-4_20

Cited by 8 publications

(2 citation statements)

References 15 publications

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“…Many practical optimal control problems obtaining the optimal solution can be insufficient because they cannot be applied owing to various physical restrictions [1,2]. Additionally, modern systems that require any type of control are complex and usually described by high-dimensional systems of ordinary differential equations (ODEs) that have only numeric solutions [3,4].…”

Section: Introductionmentioning

confidence: 99%

“…We propose a new parallel algorithm based on the mind evolutionary computation (MEC) algorithm [25] to solve an optimal control problem. The classical MEC algorithm appeared to be successful in solving real-world global optimization problems and to be suitable for parallelization [1,4,25], like similar population-based algorithms [24]. The proposed method takes the architecture of the desktop grid into account by minimizing the number of information exchanges between computing nodes and can work both in synchronous and asynchronous modes.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%