Biblio

Ant Colony Optimization is applied to design a suitable and shortest route between the starting node point and the end node point in the Wireless Sensor Network (WSN). In general ant colony algorithm plays a good role in path planning process that can also applied in improving the network security. Therefore to protect the network from the malicious nodes an ACO based Dynamic Key Management (ACO-DKM) scheme is proposed. The routes are diagnosed through ACO method also the actual coverage distance and pheromone updating strategy is updated simultaneously that prevents the node from continuous monitoring. Simulation analysis gives the efficiency of the proposed scheme.

Web caching is a strategy that can be used to speed up website access on the client-side. This strategy is implemented by storing as many popular web objects as possible on the cache server. All web objects stored on a cache server are called cached data. Requests for cached web data on the cache server are much faster than requests directly to the origin server. Not all web objects can fit on the cache server due to their limited capacity. Therefore, optimizing cached data in a web caching strategy will determine which web objects can enter the cache server to have maximum profit. This paper simulates a web caching strategy optimization with a knapsack problem approach using the Ant Colony optimization (ACO), Genetic Algorithm (GA), and a combination of the two. Knapsack profit is seen from the number of web objects that can be entered into the cache server but with the minimum objective function value. The simulation results show that the combination of ACO and GA is faster to produce an optimal solution and is not easily trapped by the local optimum.

Recommender systems (RSs) are intelligent systems to help e-commerce users to find their preferred items among millions of available items by considering the profiles of both users and items. These systems need to predict the unknown ratings and then recommend a set of high rated items. Among the others, Collaborative Filtering (CF) is a successful recommendation approach and has been utilized in many real-world systems. CF methods seek to predict missing ratings by considering the preferences of those users who are similar to the target user. A major task in Collaborative Filtering is to identify an accurate set of users and employing them in the rating prediction process. Most of the CF-based methods suffer from the cold-start issue which arising from an insufficient number of ratings in the prediction process. This is due to the fact that users only comment on a few items and thus CF methods faced with a sparse user-item matrix. To tackle this issue, a new collaborative filtering method is proposed that has a trust-aware strategy. The proposed method employs the trust relationships of users as additional information to help the CF tackle the cold-start issue. To this end, the proposed integrated trust relationships in the prediction process by using the Ant Colony Optimization (ACO). The proposed method has four main steps. The aim of the first step is ranking users based on their similarities to the target user. This step uses trust relationships and the available rating values in its process. Then in the second step, graph clustering methods are used to cluster the trust graph to group similar users. In the third step, the users are weighted based on their similarities to the target users. To this end, an ACO process is employed on the users' graph. Finally, those of top users with high similarity to the target user are used in the rating prediction process. The superiority of our method has been shown in the experimental results in comparison with well-known and state-of-the-art methods.

As the demand of online data availability increases for sharing data, business analytics, security of available data becomes important issue, data needs to be protected from unauthorized access as well as it needs to provide authority that the data is received from a trusted owner. To provide owners identity digital watermarking technique is used since long time for multimedia data. This paper proposed a technique which supports watermarking on database as most of the data available today is in database format. The characters to be entered as watermark are converted into binary values; these binary values are hidden in the database using space character. Each bit is hidden in each tuple randomly. Ant colony optimization algorithm is proposed to select tuples where watermark bits are inserted. The proposed system is enhanced in terms of security due to use of ant colony optimization and resilient because even if some bits are modified the hidden text remains almost same.

The QoS performance of MANET routing protocols is significantly affected by the mobility conditions in network. Secondly, as MANET open nature network, there is strong possibility of different types of vulnerabilities such as blackhole attack, malicious attack, DoS attacks etc. In this research work, we are designing the novel opportunistic routing protocol in order to address the challenges of network security as well as QoS improvement. There two algorithms designed in this paper. First we proposed and designed novel QoS improvement algorithm based on optimization scheme called Ant Colony Optimization (ACO) with swarm intelligence approach. This proposed method used the RSSI measurements to determine the distance between two mobile nodes in order to select efficient path for communication. This new routing protocol is named as QoS Mobility Aware ACO (QMAA) Routing Protocol. Second, we designed security algorithm for secure communication and user's authentication in MANET under the presence attackers in network. With security algorithm the QoS aware protocol is proposed named as Secure-QMAA (SQMAA). The SQMAA achieved secure communications while guaranteed QoS performance against existing routing protocols. The simulation results shows that under the presence of malicious attackers, the performance of SQMAA are efficient as compared to QMAA and state-of-art routing protocol.

The behavior patterns in everyday life such as home, office, and commuting, and buying behavior model by day of the week, sea-son, location have hierarchies of various temporal granularity. Generally, in usual hierarchical data analysis, a basic hierarchical structure is given in advance. But it is difficult to estimate hierarchical structure beforehand for complex data. Therefore, in this study, we propose the algorithm to automatically extract both hierarchical structure and pattern from time series data using swarm intelligent method. We performed the initial operation test and confirmed that patterns can be extracted hierarchically.

Cloud computing is the expansion of parallel computing, distributed computing. The technology of cloud computing becomes more and more widely used, and one of the fundamental issues in this cloud environment is related to task scheduling. However, scheduling in Cloud environments represents a difficult issue since it is basically NP-complete. Thus, many variants based on approximation techniques, especially those inspired by Swarm Intelligence (SI) have been proposed. This paper proposes a machine learning algorithm to guide the cloud choose the scheduling technique by using multi criteria decision to optimize the performance. The main contribution of our work is to minimize the makespan of a given task set. The new strategy is simulated using the CloudSim toolkit package where the impact of the algorithm is checked with different numbers of VMs varying from 2 to 50, and different task sizes between 30 bytes and 2700 bytes. Experiment results show that the proposed algorithm minimizes the execution time and the makespan between 7% and 75%, and improves the performance of the load balancing scheduling.

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.

This paper proposes a cooperative continuous ant colony optimization (CCACO) algorithm and applies it to address the accuracy-oriented fuzzy systems (FSs) design problems. All of the free parameters in a zero- or first-order Takagi-Sugeno-Kang (TSK) FS are optimized through CCACO. The CCACO algorithm performs optimization through multiple ant colonies, where each ant colony is only responsible for optimizing the free parameters in a single fuzzy rule. The ant colonies cooperate to design a complete FS, with a complete parameter solution vector (encoding a complete FS) that is formed by selecting a subsolution component (encoding a single fuzzy rule) from each colony. Subsolutions in each ant colony are evolved independently using a new continuous ant colony optimization algorithm. In the CCACO, solutions are updated via the techniques of pheromone-based tournament ant path selection, ant wandering operation, and best-ant-attraction refinement. The performance of the CCACO is verified through applications to fuzzy controller and predictor design problems. Comparisons with other population-based optimization algorithms verify the superiority of the CCACO.