Biblio

The threats posed by botnets in the cyber-space continues to grow each day and it has become very hard to detect or infiltrate bots given that the botnet developers each day keep changing the propagation and attack techniques. Currently, most of these attacks have been centered on stealing computing energy, theft of personal information and Distributed Denial of Service (DDoS attacks). In this paper, the authors propose a novel technique that uses the Non-Deterministic Polynomial-Time Hardness (NP-Hard Problem) based on the Traveling Salesperson Person (TSP) that depicts that a given bot, bj, is able to visit each host on a network environment, NE, and then it returns to the botmaster in form of instruction(command) through optimal minimization of the hosts that are or may be attacked. Given that bj represents a piece of malicious code and based on TSP-NP Hard Problem which forms part of combinatorial optimization, the authors present an effective approach for the detection of the botnet. It is worth noting that the concentration of this study is basically on the centralized botnet architecture. This holistic approach shows that botnet detection accuracy can be increased with a degree of certainty and potentially decrease the chances of false positives. Nevertheless, a discussion on the possible applicability and implementation has also been given in this paper.

Image registration is the process of aligning images of the same scene taken at different instances, from different viewpoints or by heterogeneous sensors. This can be achieved either by area based or by feature based image matching techniques. Feature based image registration focuses on detecting relevant features from the input images and attaching descriptors to these features. Matching visual descriptions of two images is a major task in image registration. This feature matching is currently done using Exhaustive Search (or Brute-Force) and Nearest Neighbour Search. The traditional method used for nearest neighbour search is by representing the data as k-d trees. This nearest neighbour search can also be performed using combinatorial optimization algorithms such as Simulated Annealing. This work proposes a method to perform image feature matching by nearest neighbour search done based on Threshold Accepting, a faster version of Simulated Annealing.The experiments performed suggest that the proposed algorithm can produce better results within a minimum number of iterations than many existing algorithms.

Metaheuristics include a wide range of optimization algorithms. Some of them are very well known and with proven value, as they solve successfully many examples of combinatorial NP-hard problems. Some examples of Metaheuristics are Genetic Algorithms (GA), Simulated Annealing (SA) or Ant Colony Optimization (ACO). Our company is devoted to making steel and is the biggest steelmaker in the world. Combining several industrial processes to produce 84.6 million tones (public official data of 2015) involves huge effort. Metaheuristics are applied to different scenarios inside our operations to optimize different areas: logistics, production scheduling or resource assignment, saving costs and helping to reach operational excellence, critical for our survival in a globalized world. Rather than obtaining the global optimal solution, the main interest of an industrial company is to have "good solutions", close to the optimal, but within a very short response time, and this latter requirement is the main difference with respect to the traditional research approach from the academic world. Production is continuous and it cannot be stopped or wait for calculations, in addition, reducing production speed implies decreasing productivity and making the facilities less competitive. Disruptions are common events, making rescheduling imperative while foremen wait for new instructions to operate. This position paper explains the problem of the time response in our industrial environment, the solutions we have investigated and some results already achieved.

Social animals as found in fish schools, bird flocks, bee hives, and ant colonies are able to solve highly complex problems in nature. This includes foraging for food, constructing astonishingly complex nests, and evading or defending against predators. Remarkably, these animals in many cases use very simple, decentralized communication mechanisms that do not require a single leader. This makes the animals perform surprisingly well, even in dynamically changing environments. The collective intelligence of such animals is known as swarm intelligence and it has inspired popular and very powerful optimization paradigms, including ant colony optimization (ACO) and particle swarm optimization (PSO). The reasons behind their success are often elusive. We are just beginning to understand when and why swarm intelligence algorithms perform well, and how to use swarm intelligence most effectively. Understanding the fundamental working principles that determine their efficiency is a major challenge. This tutorial will give a comprehensive overview of recent theoretical results on swarm intelligence algorithms, with an emphasis on their efficiency (runtime/computational complexity). In particular, the tutorial will show how techniques for the analysis of evolutionary algorithms can be used to analyze swarm intelligence algorithms and how the performance of swarm intelligence algorithms compares to that of evolutionary algorithms. The results shed light on the working principles of swarm intelligence algorithms, identify the impact of parameters and other design choices on performance, and thus help to use swarm intelligence more effectively. The tutorial will be divided into a first, larger part on ACO and a second, smaller part on PSO. For ACO we will consider simple variants of the MAX-MIN ant system. Investigations of example functions in pseudo-Boolean optimization demonstrate that the choices of the pheromone update strategy and the evaporation rate have a drastic impact on the running time. We further consider the performance of ACO on illustrative problems from combinatorial optimization: constructing minimum spanning trees, solving shortest path problems with and without noise, and finding short tours for the TSP. For particle swarm optimization, the tutorial will cover results on PSO for pseudo-Boolean optimization as well as a discussion of theoretical results in continuous spaces.

A biased random-key genetic algorithm (BRKGA) is a general search procedure for finding optimal or near-optimal solutions to hard combinatorial optimization problems. It is derived from the random-key genetic algorithm of Bean (1994), differing in the way solutions are combined to produce offspring. BRKGAs have three key features that specialize genetic algorithms: A fixed chromosome encoding using a vector of N random keys or alleles over the real interval [0, 1), where the value of N depends on the instance of the optimization problem; A well-defined evolutionary process adopting parameterized uniform crossover to generate offspring and thus evolve the population; The introduction of new chromosomes called mutants in place of the mutation operator usually found in evolutionary algorithms. Such features simplify and standardize the procedure with a set of self-contained tasks from which only one is problem-dependent: that of decoding a chromosome, i.e. using, the keys to construct a solution to the underlying optimization problem, from which the objective function value or fitness can be computed. BRKGAs have the additional characteristic that, under a weak assumption, crossover always produces feasible offspring and, therefore, a repair or healing procedure to recover feasibility is not required in a BRKGA. In this tutorial we review the basic components of a BRKGA and introduce an Application Programming Interface (API) for quick implementations of BRKGA heuristics. We then apply the framework to a number of hard combinatorial optimization problems, including 2-D and 3-D packing problems, network design problems, routing problems, scheduling problems, and data mining. We conclude with a brief review of other domains where BRKGA heuristics have been applied.

The cuttlefish optimization algorithm is a new combinatorial optimization algorithm in the family of metaheuristics, applied in the continuous domain, and which provides mechanisms for local and global research. This paper presents a new adaptation of this algorithm in the discrete case, solving the famous travelling salesman problem, which is one of the discrete combinatorial optimization problems. This new adaptation proposes a reformulation of the equations to generate solutions depending a different algorithm cases. The experimental results of the proposed algorithm on instances of TSPLib library are compared with the other methods, show the efficiency and quality of this adaptation.