752 publications from this institution
Probabilistic components are essential in nature-inspired algorithms because almost all metaheuristic algorithms use some forms of randomization and random initialization in terms of uniform distributions. This chapter introduces the fundamentals of probability distributions, Markov chains, Monte Carlo, and random walks. These concepts will form the foundations for algorithm analysis.
No abstract is provided for this article.
In a tower test of Newton's inverse-square law of gravitation we found no evidence for the non-Newtonian force, and the accuracy of the experiment constrains the Yukawa potential coupling constant |α| to be less than 0.0005.
No abstract is provided for this article.
Complex numbers can be useful in solving many engineering problems such as linear circuits, mechanical vibrations, signal processing and image processing. This chapter introduces the fundamentals of complex numbers and complex functions.
By analyzing the similarity of a self-organizing system and an optimization process, we highlight that optimization can be considered as self-organization. We analyze the characteristics of some popular met heuristic algorithms such as firefly algorithm and cuckoo search for applications in self-organizing systems.
A recently developed metaheuristic optimization algorithm, firefly algorithm (FA), mimics the social behavior of fireflies based on the flashing and attraction characteristics of fireflies. In the present study, we will introduce chaos into FA so as to increase its global search mobility for robust global optimization. Detailed studies are carried out on benchmark problems with different chaotic maps. Here, 12 different chaotic maps are utilized to tune the attractive movement of the fireflies in the algorithm. The results show that some chaotic FAs can clearly outperform the standard FA.
In this study, a recently developed metaheuristic optimization algorithm, the Firefly Algorithm (FA), is used for solving mixed continuous/discrete structural optimization problems. FA mimics the social behavior of fireflies based on their flashing characteristics. The results of a trade study carried out on six classical structural optimization problems taken from literature confirm the validity of the proposed algorithm. The unique search features implemented in FA are analyzed, and their implications for future research work are discussed in detail in the paper.
Bat algorithm (BA) is a bio-inspired algorithm developed by Yang in 2010 and BA has been found to be very efficient. As a result, the literature has expanded significantly in the last 3 years. This paper provides a timely review of the bat algorithm and its new variants. A wide range of diverse applications and case studies are also reviewed and summarized briefly here. Further research topics are also discussed.
The primary goal of an engineer is to find the best possible economical design and this goal can be achieved by considering multiple trials. A methodology with fast computing ability must be proposed for the optimum design. Optimum design of Reinforced Concrete (RC) structural members is the one of the complex engineering problems since two different materials which have extremely different prices and behaviors in tension are involved. Structural state limits are considered in the optimum design and differently from the superstructure members, RC footings contain geotechnical limit states. This study proposes a metaheuristic based methodology for the cost optimization of RC footings by employing several classical and newly developed algorithms which are powerful to deal with non-linear optimization problems. The methodology covers the optimization of dimensions of the footing, the orientation of the supported columns and applicable reinforcement design. The employed relatively new metaheuristic algorithms are Harmony Search (HS), Teaching-Learning Based Optimization algorithm (TLBO) and Flower Pollination Algorithm (FPA) are competitive for the optimum design of RC footings.
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.
Linear systems, especially large-scale spare systems, are very common in engineering simulations and computational sciences. In fact, most numerical methods for solving PDEs such as the finite difference methods and finite element methods often result in large, spare matrices. In this chapter, we introduce the basic concepts and solution methods of linear systems.
Many problems in science and engineering can be formulated as optimization problems, subject to complex, nonlinear constraints. The solutions of optimization problems often require sophisticated optimization techniques. Traditional algorithms may struggle to deal with such highly nonlinear problems. Nature-inspired algorithms can be good alternatives, and they are flexible and efficient for solving problems in optimization, data mining and machine learning. This chapter introduces the fundamentals of algorithms, classification of optimization problems and algorithms as well as a brief history of metaheuristics.
No abstract is provided for this article.
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The tragedy of the commons refers to the overuse of resources which are rival in consumption but lack excludability and it also refers to rent dissipation. While the tragedy of the anticommons is a tragedy closely connected with underuse of resources that are rival in consumption and with too strong excludability. The prior studies proved that the tragedy of the commons and the tragedy of the anticommons are symmetric from the perspective of pure mathematics, especially the game theory, which was later refuted by behavioral economics experiments. According to them, the tragedy of the anticommons is severer than the tragedy of the commons. The asymmetry of the tragedy of the commons and the tragedy of the anticommons is a paradox by these different research methods. This paradox shows that there are imperfections in the completely rational economic man hypothesis set up by neoclassical economics. As a fundamental theory, the tragedy of the commons is quite influential in many disciplines, such as microeconomics, public sector economics, ecological economics, environmental economics, management, sociology, property law, and political science. And the tragedy of the anticommons theory has also opened its door of both theoretical research and practical implications since its acceptance by Nobel laureate Buchanan, the main founder of public choice school. Only when theoretical issues are thoroughly discussed and made clear enough, can people avoid misunderstanding or misusing the commons theory. Thus, it is necessary to elucidate the paradox between them. Based on Simon's bounded rationality, Kahneman and Tversky's prospect theory, value function, Thaler's mental accounting, endowment effect, and other cognitive psychological tools, this study clearly shows that agents' decision-making process is not just based on the long-believed marginal benefit and marginal cost analysis advocated by traditional neoclassical economists. Agents' decision-making is a process in which agents selectively absorb, code the objective marginal revenue and marginal cost, and feed relevant information to their brain. Therefore, what plays a directly decisive role is not the objective marginal revenue and marginal cost per se, but the mentally perceived subjective utility of marginal revenue and marginal cost by the human brain. Followed by this research clue, the paradox between the tragedy of the commons and the tragedy of the anticommons is elucidated from the perspective of cognitive psychology.
There are many different optimization algorithms. To gain better understanding of such algorithms, we need to study them both mathematically and numerically. This chapter introduces some mathematical foundations for algorithm analysis, including norms, eigenvalues, convergence, computational complexity, and convexity.
In this paper, a Markov model of finite neuronal assembly activity is presented, Poisson approximation is considered, generating function of probability distribution is introduced, so that the Poisson approximation error is studied.
