Three local search meta-heuristics for the Minimum Interference Frequency Assignment Problem (MI-FAP) in cellular networks,

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ОСОБЛИВОСТІ ЗАСТОСУВАННЯ АЛГОРИТМУ АСО ДО ДЕЯКИХ ЗАДАЧ КРИПТОАНАЛІЗУ

Requirements for information security dictate the necessity of developing new methods of cryptanalysis. Modern cryptanalysis depend on mathematics, in particular on theory and optimization methods. Taking into account the generally recognized requirements for attack resistance of ciphers, the decryption problem should be considered as a combinatorial optimization problem The paper proves the necessary of  the development of new methods of cryptanalysis using metaheuristics, contains a retrospective review of publications in the last period in this area. The number of publications indicates the relevance of the research direction. Specialities of the application of the Ant Colony Optimization algorithm to cryptanalysis problems, in particular, factorization problem, are considered. The structure and general principles of the ACO algorithm are described, as well as the adaptation of this algorithm to the solution of a specific problem of combinatorial optimization. Various variants of the fitness function, features of their application, methods of narrowing the search space, rules for choosing the direction of movement on the graph, modification of local search are discussed. The addition of genetic operators of crossover, mutation, and selection is considered as one of the modification options. The conditions for stopping the operation of the algorithm are described. The various facts of using metaheuristics for solving combinatorial optimization problems arising in numerous subject areas, in particular, in cryptanalysis, are described.  It is emphasized that since theoretical studies of combinatorial optimization algorithms rarely allow obtaining results that can be applied in practice. The main tool for analyzing their effectiveness is a computational experiment.Вимоги до інформаційної безпеки диктують неохідність розвитку нових методів криптоаналізу. Сучасний криптоаналіз спирається на математику, зокрема на теорію та методи оптимізації. Враховучи загальновизнані вимоги до зламостійкості шифрів, задача розшифрування мусить розглядатися, як задача комбінаторної оптимізації. В роботі обґрунтовується необхідність розвитку нових методів криптоаналізу із застосуванням метаевристик, міститься ретрспективний огляд публікацій за останній період в даній області. Кількість публікацій свідчить про актуальність напрямку досліджень. Розглядаються особливості застосування алгоритму АСО (Ant Colony Optimization) до задач криптоаналізу, зокрема, задачі факторизації. Описується структура і загальні принципи роботи алгоритму АСО, адаптація даного алгоритму до розв’язання конкретної задачі комбінаторної оптимізації. Розглянуто різні варіанти фітнес-функції, особливості їх застосування, способи звуження простору пошуку, правила вибору напрямку руху на графі, модифікація локального пошуку. Як один із варіантів модифікації розглядається додавання генетичних операторів кросоверу, мутації, селекції. Описано умови припинення роботи алгоритму. Обґрунтовано доцільність застосування метаевристик для розв’зання задач комбінаторної оптимізації що виникають у різних предметних областях, зокрема, у криптоаналізі. Підкреслюється, що так як теоретичні дослідження алгоритмів комбінаторної оптимізації рідко дозволяють отримувати результати, які можуть бути застосовані на практиці, то основним інструментом аналізу їх ефективності є обчислювальний експеримент

A Meta-Optimization Approach to Solve the Set Covering Problem

Context: In the industry the resources are increasingly scarce. For this reason, we must make a good use of it. Being the optimization tools, a good alternative that it is necessary to bear in mind. A realworld problem is the facilities location being the Set Covering Problem, one of the most used models. Our interest, it is to find solution alternatives to this problem of the real-world using metaheuristics. Method: One of the main problems which we turn out to be faced on having used metaheuristic is the difficulty of realizing a correct parametrization with the purpose to find good solutions. This is not an easy task, for which our proposal is to use a metaheuristic that allows to provide good parameters to another metaheuristics that will be responsible for resolving the Set Covering Problem. Results: To prove our proposal, we use the set of 65 instances of OR-Library which also was compared with other recent algorithms, used to solve the Set Covering Problem. Conclusions: Our proposal has proved to be very effective able to produce solutions of good quality avoiding also have to invest large amounts of time in the parametrization of the metaheuristic responsible for resolving the problem

Un Enfoque de Meta-Optimización para Resolver el Problema de Cobertura de Conjunto

Context: In the industry the resources are increasingly scarce. For this reason, we must make a gooduse of it. Being the optimization tools, a good alternative that it is necessary to bear in mind. A realworldproblem is the facilities location being the Set Covering Problem, one of the most used models.Our interest, it is to find solution alternatives to this problem of the real-world using metaheuristics. Method: One of the main problems which we turn out to be faced on having used metaheuristic is thedifficulty of realizing a correct parametrization with the purpose to find good solutions. This is not aneasy task, for which our proposal is to use a metaheuristic that allows to provide good parameters toanother metaheuristics that will be responsible for resolving the Set Covering Problem. Results: To prove our proposal, we use the set of 65 instances of OR-Library which also was comparedwith other recent algorithms, used to solve the Set Covering Problem. Conclusions: Our proposal has proved to be very effective able to produce solutions of good qualityavoiding also have to invest large amounts of time in the parametrization of the metaheuristic responsiblefor resolving the problem.Contexto: En la industria los recursos son cada vez más escasos. Por esta razón debemos hacer un buen uso de ellos.Siendo las herramientas de optimización una buena alternativa que se debe tener presente. Un problema del mundo real lo contituye la ubicación de instalaciones siendo el Problema de Cobertura de Conjuntos uno de los modelos más utilizados. Nuestro interés, es encontrar alternativas de solución a este problema de la vida-real utilizando metaheuristicas. Método: Uno de los principales problemas a que nos vemos enfrentados al utilizar metaheurísticas es la dificultad de realizar una correcta parametrización con el objetivo de encontrar buenas soluciones. Esta no es una tarea fácil, para lo cual nuestra propuesta es utilizar una metaheurística que permita proporcionar buenos parametros a otra metaheurstica que será la encargada de resolver el Problema de Cobertura de Conjuntos. Resultados: Para probar nuestra propuesta, utilizamos el set de 65 instancias de OR-Library el cual además fue comparado con otros recientes algoritmos utilizados para resolver el Problema de Cobertura de Conjuntos. Conclusiones: Nuestra propuesta a demostrado ser muy efectiva logrando producir soluciones de buena calidad evitando además que se tenga que invertir gran cantidad de tiempo en la parametrización de la metaheurística encargada de resolver el problema

Penguins Search Optimisation Algorithm for Association Rules Mining

Association Rules Mining (ARM) is one of the most popular and well-known approaches for the decision-making process. All existing ARM algorithms are time consuming and generate a very large number of association rules with high overlapping. To deal with this issue, we propose a new ARM approach based on penguins search optimisation algorithm (Pe-ARM for short). Moreover, an efficient measure is incorporated into the main process to evaluate the amount of overlapping among the generated rules. The proposed approach also ensures a good diversification over the whole solutions space. To demonstrate the effectiveness of the proposed approach, several experiments have been carried out on different datasets and specifically on the biological ones. The results reveal that the proposed approach outperforms the well-known ARM algorithms in both execution time and solution quality

Grey Scale Image Multi-Thresholding Using Moth-Flame Algorithm and Tsallis Entropy

In the current era, image evaluations play a foremost role in a variety of domains, where the processing of digital images is essential to identify vital information. The image multi-thresholding is a vital image pre-processing field in which the available digital image is enhanced by grouping similar pixel values. Normally, the digital test images are available in RGB/greyscale format and the appropriate processing methodology is essential to treat the images with a chosen methodology. In the proposed approach, Tsallis Entropy (TE) supported multi-level thresholding is planned for the benchmark greyscale imagery of dimension 512x512x1 pixels using a chosen threshold values (T=2,3,4,5). This work suggests the possible Cost Value (CV) that can be considered during the optimization search and the proposed work is executed by considering the maximization of the TE as the CV. The entire thresholding task is executed using Moth-Flame Algorithm (MFA) and the accomplished results are validated based on the image quality measures of various thresholds. The attained result with MFO is better compared to the result of CS, BFO, PSO, and GA

Sharks, Zombies and Volleyball: Lessons from the Evolutionary Computation Bestiary

The field of optimization metaheuristics has a long history of finding inspiration in natural systems. Starting from classic methods such as Genetic Algorithms and Ant Colony Optimization, more recent methods claim to be inspired by natural (and sometimes even supernatural) systems and phenomena - from birds and barnacles to reincarnation and zombies. Since 2014 we publish a humorous website, The Bestiary of Evolutionary Computation, to catalog these methods, witnessing an explosion of metaphor-heavy algorithms in the literature. While metaphors can be powerful inspiration tools, we argue that the emergence of hundreds of barely discernible algorithmic variants under different labels and nomenclatures has been counterproductive to the scientific progress of the field, as it neither improves our ability to understand and simulate biological systems, nor contributes generalizable knowledge or design principles for global optimization approaches. In this short paper we discuss some of the possible causes of this trend, its negative consequences to the field, as well as some efforts aimed at moving the area of metaheuristics towards a better balance between inspiration and scientific soundness

Learning Opposites with Evolving Rules

The idea of opposition-based learning was introduced 10 years ago. Since then a noteworthy group of researchers has used some notions of oppositeness to improve existing optimization and learning algorithms. Among others, evolutionary algorithms, reinforcement agents, and neural networks have been reportedly extended into their opposition-based version to become faster and/or more accurate. However, most works still use a simple notion of opposites, namely linear (or type- I) opposition, that for each $x\in[a,b]$ assigns its opposite as $\breve{x}_I=a+b-x$. This, of course, is a very naive estimate of the actual or true (non-linear) opposite $\breve{x}_{II}$, which has been called type-II opposite in literature. In absence of any knowledge about a function $y=f(\mathbf{x})$ that we need to approximate, there seems to be no alternative to the naivety of type-I opposition if one intents to utilize oppositional concepts. But the question is if we can receive some level of accuracy increase and time savings by using the naive opposite estimate $\breve{x}_I$ according to all reports in literature, what would we be able to gain, in terms of even higher accuracies and more reduction in computational complexity, if we would generate and employ true opposites? This work introduces an approach to approximate type-II opposites using evolving fuzzy rules when we first perform opposition mining. We show with multiple examples that learning true opposites is possible when we mine the opposites from the training data to subsequently approximate $\breve{x}_{II}=f(\mathbf{x},y)$.Comment: Accepted for publication in The 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2015), August 2-5, 2015, Istanbul, Turke

Application of a Modified ACO Algorithm for Optimizing Routes and Externality Effect of Solid Waste Management

To improve solid waste management and maintain its sustainability, it is important to reduce both the solid waste operational cost which includes the monetary value of distances covered and the externality effects of solid waste management. Therefore, this paper presents an application of a modified Ant Colony System algorithm to a bi-objective model for solid waste management in the Shama District in the Western Region of Ghana. The objective is to optimize route lengths and externality effects of solid waste management. Data on route lengths and population of communities along the routes were collected from 20 communities in the Shama Distric. Externality effect was measured by considering the population of the communities along the routes, the cost of treating a common cold subject to the assumption of two percent of the population being affected by the externality effect. The implemented algorithm has demonstrated the bi-objective optimal solution of route length (km) and externality effect (GHS) of (11, 2100) achievable on the path , which respectively represents a path linking the following communities: Aboadze, Abuesi Assorko Essaman, Beposo, Bosomdo and Fawomanye. There is therefore the need to ensure that the communities involved are linked with good roads