scholarly journals A Brief Survey on Nature Inspired Algorithms: Clever Algorithms for Optimization

2018 ◽  
Vol 7 (1) ◽  
pp. 27-32
Author(s):  
P. Sindhuja ◽  
P. Ramamoorthy ◽  
M. Suresh Kumar

This paper presents a brief survey on various optimization algorithms. To be more precise, the paper elaborates on clever Algorithms – a class of Nature inspired Algorithms. The Nature Inspired Computing (NIC) is an emerging area of research that focuses on Physics and Biology Based approach to the Algorithms for optimization. The Algorithms briefed in this paper have understood, explained, adapted and replicated the phenomena of Nature to replicate them in the artificial systems. This Cross – fertilisation of Nature Inspired Computing (NIC) and Computational Intelligence (CI) will definitely provide optimal solutions to existing problems and also open up new arenas in Research and Development. This paper briefs the classification of clever algorithms and the key strategies employed for optimization.

1990 ◽  
Author(s):  
Jesse Orlansky ◽  
Frances Grafton ◽  
Clessen J. Martin ◽  
William Alley ◽  
Bruce Bloxom

2021 ◽  
Author(s):  
Suvita Rani Sharma ◽  
Birmohan Singh ◽  
Manpreet Kaur

Author(s):  
Сергей Михайлович Савушкин

Важность определения конкретных, измеримых и объективно необходимых целей деятельности исправительных учреждений уголовно-исполнительной системы объясняется проблемами, с которыми сталкиваются сотрудники при выполнении функций, отдельные из которых не способствуют достижению целей уголовно-исполнительного законодательства РФ. В статье рассматриваются и подвергаются конструктивной критике цели уголовно-исполнительного законодательства, задачи уголовно-исполнительной системы (которые в 2004 г. были исключены из закона), основные задачи ФСИН России, основные цели Концепции развития уголовно-исполнительной системы РФ до 2020 г., цель Концепции федеральной целевой программы «Развитие уголовно-исполнительной системы (2017-2025 годы)». Приводятся цели классификации осужденных, которые предусмотрены Правилами Нельсона Манделы, как положительный опыт закрепления целей отдельного правового института. Высказывается позиция относительно необходимости закрепления целей отдельных институтов, промежуточных целей и важности определения точных критериев оценки достижимости отмеченных целей. Данная работа проводится для выявления имеющихся проблем, связанных с отсутствием конкретных показателей деятельности исправительных учреждений, выполнение которых должно способствовать достижению целей уголовно-исполнительного законодательства РФ. The importance of determining the specific, measurable and objectively necessary goals of the activities of correctional institutions of the penal system is explained by the problems faced by employees in performing functions, some of which do not contribute to the achievement of the goals of the penal legislation of the Russian Federation. The article discusses and criticizes constructively the goals of the penal legislation, the tasks of the penal system (which were excluded from the Law in 2004), the main tasks of the Federal Penitentiary Service of Russia, the main goals of the Development Concept of the Russian penal system until 2020, the goal of the Federal Concept target program "Development of the penal system (2017-2025)". The goals of the classification of convicts, which are provided for by the rules of Nelson Mandela, as a positive experience in fixing goals, a separate legal institution. A position is expressed regarding the need to consolidate the goals of individual institutions, intermediate goals and the importance of determining, exact criteria, assessing the attainability of the stated goals. This work is carried out in order to establish the existing problems associated with the lack of specific indicators of the activity of correctional institutions, the implementation of which should help achieve the goals of the criminal-executive legislation of the Russian Federation.


2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Nhat-Duc Hoang

To improve the efficiency of the periodic surveys of the asphalt pavement condition, this study puts forward an intelligent method for automating the classification of pavement crack patterns. The new approach relies on image processing techniques and computational intelligence algorithms. The image processing techniques of Laplacian pyramid and projection integral are employed to extract numerical features from digital images. Least squares support vector machine (LSSVM) and Differential Flower Pollination (DFP) are the two computational intelligence algorithms that are employed to construct the crack classification model based on the extracted features. LSSVM is employed for data classification. In addition, the model construction phase of LSSVM requires a proper setting of the regularization and kernel function parameters. This study relies on DFP to fine-tune these two parameters of LSSVM. A dataset consisting of 500 image samples and five class labels of alligator crack, diagonal crack, longitudinal crack, no crack, and transverse crack has been collected to train and verify the established approach. The experimental results show that the Laplacian pyramid is really helpful to enhance the pavement images and reveal the crack patterns. Moreover, the hybridization of LSSVM and DFP, named as DFP-LSSVM, used with the Laplacian pyramid at the level 4 can help us to achieve the highest classification accuracy rate of 93.04%. Thus, the new hybrid approach of DFP-LSSVM is a promising tool to assist transportation agencies in the task of pavement condition surveying.


Author(s):  
Engin Pekel ◽  
Ebru Pekel Özmen

Diabetes mellitus (DM) is a group of metabolic disorders with one common manifestation: elevated blood sugar or hyperglycemia. The diagnosis of diabetes is the most crucial point due to chronic hyperglycemia. This chapter improves the performance of the Classification and Regression Trees (CART) algorithm because the accurate classification of diabetes depends on the algorithm efficiency. Authors use the accuracy rate for the objective function in the prediction process by Genetic Algorithm (GA). The proposed GA-CART algorithm provides the best performance at 96.05%.


2020 ◽  
pp. 1580-1600
Author(s):  
Subhendu Kumar Pani

A wireless sensor network may contain hundreds or even tens of thousands of inexpensive sensor devices that can communicate with their neighbors within a limited radio range. By relaying information on each other, they transmit signals to a command post anywhere within the network. Worldwide market for wireless sensor networks is rapidly growing due to a huge variety of applications it offers. In this chapter, we discuss application of computational intelligence techniques in wireless sensor networks on the coverage problem in general and area coverage in particular. After providing different types of coverage encountered in WSN, we present a possible classification of coverage algorithms. Then we dwell on area coverage which is widely studied due to its importance. We provide a survey of literature on area coverage and give an account of its state-of-the art and research directions.


Author(s):  
Subhendu Kumar Pani

A wireless sensor network may contain hundreds or even tens of thousands of inexpensive sensor devices that can communicate with their neighbors within a limited radio range. By relaying information on each other, they transmit signals to a command post anywhere within the network. Worldwide market for wireless sensor networks is rapidly growing due to a huge variety of applications it offers. In this chapter, we discuss application of computational intelligence techniques in wireless sensor networks on the coverage problem in general and area coverage in particular. After providing different types of coverage encountered in WSN, we present a possible classification of coverage algorithms. Then we dwell on area coverage which is widely studied due to its importance. We provide a survey of literature on area coverage and give an account of its state-of-the art and research directions.


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