Solving global optimization problems using randomized search heuristics combined with quasi-Newton methods

In general, the presented algorithm can be successfully applied to solve global optimization problems with different types of complexities, such as multimodality, non-separability, nonlinearity, non-differentiability, gully trap, the high dimension of search space, high computational complexity, and more

Guided hybrid genetic algorithm for solving global optimization problems

The paper develops and implements a new algorithm for solving global optimization problems by combining genetic algorithm and quasi-Newton methods, which reproduces guided local search, and combines two successful modifications of the hybrid approach, the first of which BOHGA establishes a qualitative balance between local and global search, the second – HGDN – prevents re-exploration of previously explored areas of search space. In addition, a modified bump function and an adaptive scheme for determining its parameter – the radius of the "deflated" region of the objective function in the vicinity of the already found local minimum - were proposed to speed up the algorithm

Advances in Kriging-Based Autonomous X-Ray Scattering Experiments.

Autonomous experimentation is an emerging paradigm for scientific discovery, wherein measurement instruments are augmented with decision-making algorithms, allowing them to autonomously explore parameter spaces of interest. We have recently demonstrated a generalized approach to autonomous experimental control, based on generating a surrogate model to interpolate experimental data, and a corresponding uncertainty model, which are computed using a Gaussian process regression known as ordinary Kriging (OK). We demonstrated the successful application of this method to exploring materials science problems using x-ray scattering measurements at a synchrotron beamline. Here, we report several improvements to this methodology that overcome limitations of traditional Kriging methods. The variogram underlying OK is global and thus insensitive to local data variation. We augment the Kriging variance with model-based measures, for instance providing local sensitivity by including the gradient of the surrogate model. As with most statistical regression methods, OK minimizes the number of measurements required to achieve a particular model quality. However, in practice this may not be the most stringent experimental constraint; e.g. the goal may instead be to minimize experiment duration or material usage. We define an adaptive cost function, allowing the autonomous method to balance information gain against measured experimental cost. We provide synthetic and experimental demonstrations, validating that this improved algorithm yields more efficient autonomous data collection

Hybrid genetic deflated Newton method for global optimisation

Optimisation is a basic principle of nature and has a vast variety of applications in research and industry. There is a plurality of different optimisation procedures which exhibit different strengths and weaknesses in computational efficiency and probability of finding the global optimum. Most methods offer a trade-off between these two aspects. This paper proposes a hybrid genetic deflated Newton (HGDN) method to find local and global optima more efficiently than competing methods. The proposed method is a hybrid algorithm which uses a genetic algorithm to explore the parameter domain for regions containing local minima, and a deflated Newton algorithm to efficiently find their exact locations. In each iteration, identified minima are removed using deflation, so that they cannot be found again. The genetic algorithm is adapted as follows: every individual of every generation of offspring searches its adjacent space for optima using Newton’s method; when found, the optimum is removed by deflation, and the individual returns to its starting position. This procedure is repeated until no more optima can be found. The deflation step ensures that the same optimum is not found twice. In the subsequent genetic step, a new generation of offspring is created, using procreation of the fittest. We demonstrate that the proposed method converges to the global optimum, even for small populations. Furthermore, the numerical results show that the HGDN method can improve the number of necessary function and derivative evaluations by orders of magnitude

ІНФОРМАЦІЙНІ ТЕХНОЛОГІЇ: ТЕОРІЯ І ПРАКТИКА

У збірнику подано тези доповідей інтернет-конференції, яка відбулася 17 – 19 березня 2021 р. на базі Національного технічного університету «Дніпровська політехніка» в онлайн-форматі. Розглянуто результати теоретичних та експериментальних досліджень у питаннях моделювання, аналізу та оптимізації складних систем, розробки й практичного застосування інтелектуальних комп’ютерних систем в автоматиці, електроніці, вимірювальній техніці та економіці, у системах захисту інформації.Призначено для здобувачів вищої освіти, які вивчають інформаційні технології, аспірантів, науково-технічних працівників, викладачів вищих навчальних закладів. Збірник буде корисний також усім, хто працює в інформаційній галузі і цікавиться практичним застосуванням інтелектуальних систем.Міністерство освіти і науки України Національний технічний університет «Дніпровська пoлiтехнікa» Харківський національний університет міського господарства імені О.М. Бекетова Національний університет «Запорізька політехніка» Громадська організація «Системні дослідження