Population Diversity in Ant-inspired Optimization Algorithms

Finding a balance between exploration and exploitation is very important in the case of metaheuristics optimization, especially in the systems leveraging population of individuals expressing (as in Evolutionary Algorithms, etc.) or constructing (as in Ant Colony Optimization) solutions. Premature convergence is a real problem and finding means of its automatic detection and counteracting are of great importance. Measuring diversity in Evolutionary Algorithms working in real-value search space is often computationally complex, but feasible while measuring diversity in combinatorial domain is practically impossible (cf. Closest String Problem). Nevertheless, we propose several practical and feasible diversity measurement techniques dedicated to Ant Colony Optimization algorithms, leveraging the fact that even though analysis of the search space is at least an NP problem, we can focus on the pheromone table, where the direct outcomes of the search are expressed and can be analyzed. Besides proposing the measurement techniques, we apply them to assess the diversity of several variants of ACO, and closely analyze their features for the classic ACO. The discussion of the results is the first step towards applying the proposed measurement techniques in auto-adaptation of the parameters affecting directly the exploitation and exploration features in ACO in the future

Optimal leach protocol with improved bat algorithm in wireless sensor networks

© 2019, Korean Society for Internet Information. All rights reserved. A low-energy adaptive clustering hierarchy (LEACH) protocol is a low-power adaptive cluster routing protocol which was proposed by MIT’s Chandrakasan for sensor networks. In the LEACH protocol, the selection mode of cluster-head nodes is a random selection of cycles, which may result in uneven distribution of nodal energy and reduce the lifetime of the entire network. Hence, we propose a new selection method to enhance the lifetime of network, in this selection function, the energy consumed between nodes in the clusters and the power consumed by the transfer between the cluster head and the base station are considered at the same time. Meanwhile, the improved FTBA algorithm integrating the curve strategy is proposed to enhance local and global search capabilities. Then we combine the improved BA with LEACH, and use the intelligent algorithm to select the cluster head. Experiment results show that the improved BA has stronger optimization ability than other optimization algorithms, which the method we proposed (FTBA-TC-LEACH) is superior than the LEACH and LEACH with standard BA (SBA-LEACH). The FTBA-TC-LEACH can obviously reduce network energy consumption and enhance the lifetime of wireless sensor networks (WSNs)

Multiobjective differential evolution enhanced with principle component analysis for constrained optimization

Multiobjective evolutionary algorithms (MOEAs) have been successfully applied to a number of constrained optimization problems. Many of them adopt mutation and crossover operators from differential evolution. However, these operators do not explicitly utilise features of fitness landscapes. To improve the performance of algorithms, this paper aims at designing a search operator adapting to fitness landscapes. Through an observation, we find that principle component analysis (PCA) can be used to characterise fitness landscapes. Based on this finding, a new search operator, called PCA-projection, is proposed. In order to verify the effectiveness of PCA-projection, we design two algorithms enhanced with PCA-projection for solving constrained optimization problems, called PMODE and HECO-PDE, respectively. Experiments have been conducted on the IEEE CEC 2017 competition benchmark suite in constrained optimization. PMODE and HECO-PDE are compared with the algorithms from the IEEE CEC 2018 competition and another recent MOEA for constrained optimization. Experimental results show that an algorithm enhanced with PCA-projection performs better than its corresponding opponent without this operator. Furthermore, HECO-PDE is ranked first on all dimensions according to the competition rules. This study reveals that decomposition-based MOEAs, such as HECO-PDE, are competitive with best single-objective and multiobjective evolutionary algorithms for constrained optimization, but MOEAs based on non-dominance, such as PMODE, may not

Uso de machine learning para classificação de fornecedores no contexto da data science

Decision-making for groups, public or private, is indispensable to the development of organizations, and searching for mechanisms to support the managers more assertively is fundamental to this goal. Know how to use raw data transforming them into knowledge allows these decisions to be based on data besides purely on intuition. Between the important decisions taken by any organization, the classification and selection of suppliers are an important practice to industrial engineering and Data Science is an ascendant field that studies data and how to realize this transformation of raw data into knowledge. To this research were used real data from suppliers of an enterprise of the aeronautical sector in its analyses. So, this research acted between Data Science and Classification and Selection of suppliers and had the focus on a problem known as clusterization that is the segmentation of data in regions as homogeneous as possible when there´s no existence of previous categories and aim to solve this problem supporting in the supplier´s management. This happens in practice using Data Science tools known as Machine Learning that are algorithms that can be used in the segmentation of groups without an initial classification. To the development has been used the procedure CRISP-DM that allows elucidate analyses´ problems helping to structure the scientific thinking. That way, by using this procedure, this dissertation had its general objetive in the use of the technique of Machine Learning to help in the classification and selection of suppliers of that organization. Having two specific objectives, the first one consisted of an analysis of those algorithms in the demonstration of the operation and behavior of the classic algorithms of clusterization of the real database. The second one consisted in analyzing those clusterization algorithms in search of the most appropriate to the supplier´s base culminating with the creation and suggestion of a framework that can be used for future clustering analises. The clustering modelings were realized and through internal and stability validations had their efficiency tested allowing the data to be split into clusters. The use of CRISP-DM allowed that the clustering framework was proposedA tomada de decisão por grupos, públicos ou privados, é indispensável para o desenvolvimento das organizações e encontrar mecanismos que apoiem os gestores de forma mais assertiva é fundamental para essa finalidade. Saber utilizar dados brutos transformando os em conhecimento permite que essas decisões sejam baseadas em dados além de puramente em intuição. Dentre as decisões importantes tomadas por qualquer organização, a classificação e seleção de fornecedores é uma prática importante para a engenharia de produção e a Data Science é um campo ascendente que estuda dados e como realizar essa transformação de dados brutos em conhecimento. Para essa pesquisa foram utilizados dados reais dos fornecedores de uma empresa do setor aeronáutico em suas análises. Então, esta pesquisa atuou entre Data Science e Classificação e Seleção de fornecedores e teve seu foco no problema conhecido como clusterização (ou agrupamento) que é a segmentação de dados em regiões o mais homogêneas possíveis quando não se apresentam categorias prévias e busca-se resolver este problema auxiliando no gerenciamento dos fornecedores. Isso acontece na prática utilizando-se de ferramentas de Data Science conhecidas como Machine Learning que consistem em algoritmos que podem ser utilizados na segmentação de grupos sem nenhuma classificação inicial. Para o desenvolvimento utilizou-se o procedimento CRISP DM que permite solucionar problemas de análises de dado ajudando a estruturar o pensamento científico. Desta forma, por meio do auxílio desse procedimento, esta dissertação teve como objetivo geral utilizar a técnica de Machine Learning para auxiliar na classificação e seleção de fornecedores dessa organização. Tendo dois objetivos específicos, o primeiro consistindo na demonstração do funcionamento e comportamento dos algoritmos clássicos de clusterização na base de dados real das técnicas clássicas. E o segundo objetivo específico consistiu em analisar esses algoritmos de clusterização em busca do mais apropriado para a base de fornecedores em estudo culminando com a criação e sugestão de um framework que pode ser utilizado para análises futuras de clusterização. As modelagens das clusterizações foram realizadas e por meio de validação interna e de estabilidade a eficiência delas foi testada permitindo que os dados fossem separados em clusters. A utilização do CRISP-DM permitiu que o framework para clusterização fosse proposto

A Deep Learning-based Approach to Identifying and Mitigating Network Attacks Within SDN Environments Using Non-standard Data Sources

Modern society is increasingly dependent on computer networks, which are essential to delivering an increasing number of key services. With this increasing dependence, comes a corresponding increase in global traffic and users. One of the tools administrators are using to deal with this growth is Software Defined Networking (SDN). SDN changes the traditional distributed networking design to a more programmable centralised solution, based around the SDN controller. This allows administrators to respond more quickly to changing network conditions. However, this change in paradigm, along with the growing use of encryption can cause other issues. For many years, security administrators have used techniques such as deep packet inspection and signature analysis to detect malicious activity. These methods are becoming less common as artificial intelligence (AI) and deep learning technologies mature. AI and deep learning have advantages in being able to cope with 0-day attacks and being able to detect malicious activity despite the use of encryption and obfuscation techniques. However, SDN reduces the volume of data that is available for analysis with these machine learning techniques. Rather than packet information, SDN relies on flows, which are abstract representations of network activity. Security researchers have been slow to move to this new method of networking, in part because of this reduction in data, however doing so could have advantages in responding quickly to malicious activity. This research project seeks to provide a way to reconcile the contradiction apparent, by building a deep learning model that can achieve comparable results to other state-of-the-art models, while using 70% fewer features. This is achieved through the creation of new data from logs, as well as creation of a new risk-based sampling method to prioritise suspect flows for analysis, which can successfully prioritise over 90% of malicious flows from leading datasets. Additionally, provided is a mitigation method that can work with a SDN solution to automatically mitigate attacks after they are found, showcasing the advantages of closer integration with SDN