Clustering algorithms are an important component of data mining technology which has been applied widely in many applications including those that operate on Internet. Recently a new line of research namely Web Intelligence emerged that demands for advanced analytics and machine learning algorithms for supporting knowledge discovery mainly in the Web environment. The so called Web Intelligence data are known to be dynamic, loosely structured and consists of complex attributes. To deal with this challenge standard clustering algorithms are improved and evolved with optimization ability by swarm intelligence which is a branch of nature-inspired computing. Some examples are PSO Clustering (C-PSO) and Clustering with Ant Colony Optimization. The objective of this paper is to investigate the possibilities of applying other nature-inspired optimization algorithms (such as Fireflies, Cuckoos, Bats and Wolves) for performing clustering over Web Intelligence data. The efficacies of each new clustering algorithm are reported in this paper, and in general they outperformed C-PSO.
Passive-cooling building materials can achieve cooling without external energy consumption, which is an energy-saving and environmentally friendly cooling method. However, the existing passive-cooling building materials have the limitations of high cost, complicated processes, and a toxic organic solvent, which hinders the passive-cooling technology applied in practical building. To overcome these limitations, we developed a facile, high-efficiency, non-toxic, and superhydrophobic passive-cooling building coating (SPCBC) with an efficient cooling capability and excellent durability that was composed of polydimethylsiloxane and SiO2. The fabricated SPCBC demonstrated a high reflectance and a high emittance, showing a superior cooling capability with a 14 °C temperature drop compared with a bare cement surface on a hot summer day. In addition, the SPCBC could not be wetted or contaminated by muddy water, corrosive aqueous solutions, or dust, which presented an excellent anti-fouling and self-cleaning capability. Moreover, the fabricated SPCBC could work outdoors for 30 days, withstand UV irradiation for 30 days, and resist accelerated aging for 100 h without any significant changes in the superhydrophobicity and the cooling capability, meaning that the SPCBC had an outstanding durability. This work provides a new method to facilitate passive-cooling technology to apply in practical building in hot weather regions of the world.
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Nature-inspired algorithms such as Particle Swarm Optimization and Firefly Algorithm are among the most powerful algorithms for optimization. In this paper, we intend to formulate a new metaheuristic algorithm by combining Levy flights with the search strategy via the Firefly Algorithm. Numerical studies and results suggest that the proposed Levy-flight firefly algorithm is superior to existing metaheuristic algorithms. Finally implications for further research and wider applications will be discussed.
No abstract is provided for this article.
The amount of data available over Internet and World Wide Web is increasing exponentially. Retrieving data that is more close to user's query effectively and efficiently is a challenging task in Information Retrieval (IR) system. Clustering of Documents is one of the solutions to this. Clustering is the process of partitioning a set of objects in such a way that the objects in same cluster are more similar. The number of possible ways in which the documents can be clustered is enormous and this makes the problem to be a combinatorial optimization problem. Nature inspired algorithms are commanding tools to attack this type of problem. In this paper, an attempt has been made to use Cuckoo Search Optimization (CSO) algorithm to solve the problem of document clustering. The CSO algorithm is experimented with standard benchmark dataset, Classic4 dataset. The quality of solutions generated by CSO algorithm in terms of DB Index was compared with K-means algorithm and Ant Colony Optimization (ACO) algorithm. The results reveal that CSO algorithm is a viable to achieve world class solutions to high dimensional data clustering.
The firefly algorithm is a swarm intelligence-based algorithm, and its nonlinearity in search mechanisms can usually lead to subdivision and multiswarms, which means that it can be potentially more effective than single-swarm algorithms. This chapter introduces the main ideas of the firefly algorithm, followed by the introduction of the flower pollination algorithm. Both implementation details and examples will be presented to show how these algorithms work. Suggestions on modifications and multiobjective optimization will also be discussed.
Modern metaheuristic algorithms such as bee algorithms and harmony search start to demonstrate their power in dealing with tough optimization problems and even NP-hard problems. This book reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms in optimization, including genetic algorithms, bee algorithms, particle swarm optimization, simulated annealing, ant colony optimization, harmony search, and firefly algorithms. We also briefly introduce the photosynthetic algorithm, the enzyme algorithm, and Tabu search. Worked examples with implementation have been used to show how each algorithm works. This book is thus an ideal textbook for an undergraduate and/or graduate course. As some of the algorithms such as the harmony search and firefly algorithms are at the forefront of current research, this book can also serve as a reference book for researchers.
Bat algorithm is a population metaheuristic proposed in 2010 which is based on the echolocation or bio-sonar characteristics of microbats. Since its first implementation, the bat algorithm has been used in a wide range of fields. In this paper, we present a discrete version of the bat algorithm to solve the well-known symmetric and asymmetric Traveling Salesman Problems. In addition, we propose an improvement in the basic structure of the classic bat algorithm. To prove that our proposal is a promising approximation method, we have compared its performance in 37 instances with the results obtained by five different techniques: evolutionary simulated annealing, genetic algorithm, an island based distributed genetic algorithm, a discrete firefly algorithm and an imperialist competitive algorithm. In order to obtain fair and rigorous comparisons, we have conducted three different statistical tests along the paper: the Student׳s t-test, the Holm׳s test, and the Friedman test. We have also compared the convergence behavior shown by our proposal with the ones shown by the evolutionary simulated annealing, and the discrete firefly algorithm. The experimentation carried out in this study has shown that the presented improved bat algorithm outperforms significantly all the other alternatives in most of the cases.
A bilinear mathematical model of the imune system and its circulation is presented Then a joint algorithm for parameter and state estimation is derived. The estimation procedure is applied to the discrete bilinear stochastic model. Data for estimation is taken from a pure strain of female rats by means of compartmental tracer techniques for tagged lymphocytes.
No abstract is provided for this article.
A new resident virus propagation model with graded infection rate is established based on the properties of two important stages undergoing by resident virus,which are the latent phase,in which resident virus cannot infect other hosts because it has not yet been loaded into memory and the active phase,in which resident virus resides in memory and infects any suitable program that is executed on the computer.Two computer compartments with different infection rate are established.Furthermore,the dynamic behaviors of this model are investigated by stability theory and numerical simulations.It is found that the dynamical properties of this model are determined by basic reproductive rate.Specifically,virus-free equilibrium is globally asymptotically stable if basic reproductive rate is less than or equal to one,whereas the local asymptotical stability of the viral equilibrium is guaranteed if basic reproductive rate is bigger than one,followed by a conjecture on its global stability.Then the sensitivity analysis of basic reproductive rate to the system parameters is investigated and a collection of policies is advised to control the spread of computer virus over the Internet.
In order to better handle the new features that emerge at both ends of supply and demand, new measures are constantly being introduced, such as demand-side management (DSM) and prediction of uncertain output and load. However, the existing DSM strategies, like real-time price (RTP), and dispatch methods are optimized separately, and response models of active loads, such as the interruptible load (IL), are still imperfect, which make it difficult for the active distribution network (ADN) to achieve global optimal operation. Therefore, to better manage active loads, the response characteristics including both the response time and the responsibility and compensation model of IL for cluster users, and the real-time demand response model for price based load, were analyzed and established. Then, a collaborative optimization strategy of RTP and optimal dispatch of ADN was proposed, which can realize an economical operation based on mutual benefit and win-win mode of supply and demand sides. Finally, the day-ahead and intra-day integrative dispatch model using different time-scale prediction data was established, which can achieve longer-term optimization while reducing the impact of prediction errors on the dispatch results. With numerical simulations, the effectiveness and superiority of the proposed strategy were verified.
