CiteULike: nojhan Chen

Creating, Destroying, and Restoring Value in Wikipedia

Reid Priedhorsky — 2008-04-02T09:39:59-00:00

(2--4 November 2007)

Wikipedia’s brilliance and curse is that any user can edit any of the encyclopedia entries. We introduce the notion of the impact of an edit, measured by the number of times the edited version is viewed. Using several datasets, including recent logs of all article views, we show that frequent edi- tors dominate what people see when they visit Wikipedia, and that this domination is increasing.∗ Similarly, using the same impact measure, we show that the probability of a typ- ical article view being damaged is small but increasing, and we present empirically grounded classes of damage. Finally, we make policy recommendations for Wikipedia and other wikis in light of these ﬁndings.

Seeker Optimization Algorithm

Chaohua Dai — 2007-09-19T07:54:53-00:00

Computational Intelligence and Security (2007), pp. 167-176.

A novel swarm intelligence paradigm called seeker optimization algorithm (SOA) for the real-parameter optimization is proposed in this paper. The SOA is based on the concept of simulating the act of humans’ intelligent search with their memory, experience, and uncertainty reasoning. In this sense, the individual of this population is called seeker or searcher just from which the new algorithm’ name is derived. After given start point, search direction, search radius, and trust degree, every seeker moves to a new position (next solution) based on his social learning, cognitive learning, and uncertainty reasoning. The algorithm’s performance was studied using several typically complex functions. In almost all cases studied, SOA is superior to continuous genetic algorithm (GA) and particle swarm optimization (PSO) in all optimization quality, robustness and efficiency.

Common model analysis and improvement of particle swarm optimizer

Feng Pan — 2007-08-31T09:59:17-00:00

Journal of Control Theory and Applications, Vol. 5, No. 3. (2007), pp. 233-238.

Abstract Particle swarm optimizer (PSO), a new evolutionary computation algorithm, exhibits good performance for optimization problems, although PSO can not guarantee convergence of a global minimum, even a local minimum. However, there are some adjustable parameters and restrictive conditions which can affect performance of the algorithm. In this paper, the algorithm are analyzed as a time-varying dynamic system, and the sufficient conditions for asymptotic stability of acceleration factors, increment of acceleration factors and inertia weight are deduced. The value of the inertia weight is enhanced to (−1, 1). Based on the deduced principle of acceleration factors, a new adaptive PSO algorithmharmonious PSO (HPSO) is proposed. Furthermore it is proved that HPSO is a global search algorithm. In the experiments, HPSO are used to the model identification of a linear motor driving servo system. An Akaike information criteria based fitness function is designed and the algorithms can not only estimate the parameters, but also determine the order of the model simultaneously. The results demonstrate the effectiveness of HPSO.

An agent-based evolutionary strategic negotiation for project dynamic scheduling

Yee-Ming Chen — 2007-02-07T10:14:24-00:00

The International Journal of Advanced Manufacturing Technology

Simulated annealing with asymptotic convergence for nonlinear constrained optimization

Benjamin Wah — 2006-12-20T07:56:36-00:00

Journal of Global Optimization

An intelligent genetic algorithm designed for global optimization of multi-minima functions

Li-Ning Xing — 2006-08-29T20:52:13-00:00

Applied Mathematics and Computation, Vol. 178, No. 2. (15 July 2006), pp. 355-371.

Many practical problems often lead to large non-convex non-linear programming problems that have multi-minima. The global optimization algorithms of these problems have received much attention over the last few years. Generally, stochastic algorithms are suitable for these problems, but not efficient when there are too many minima. Genetic algorithms are stochastic search approaches based on randomized operators, such as selection, crossover and mutation, inspired by the natural reproduction and evolution of the living creatures. However, the existing genetic algorithms cannot solve global optimization of multi-minima functions effectively. A new algorithm called intelligent genetic algorithm (IGA) is proposed for the global optimization of multi-minima functions. IGA integrates many cross operator, mutation operator and reattempt operation. It can select the appropriate cross operator, mutation operator or reattempt operation according to the current optimization result. It converges to the global optimization solution without the influence of random searching process. At first, this paper introduces the foundation for designing intelligent genetic algorithm; secondly, this paper reports on the development of an intelligent genetic algorithm approach for global optimization problems; thirdly, the proposed method is illustrated by means of some numerical examples; finally, the conclusions of this study are drawn with possible directions for subsequent studies. The feasibility, the efficiency and the effectiveness of IGA are tested in detail through a set of benchmark multi-modal functions, of which global and local minima are known. The experimental results suggest that results from IGA are better than results from other methods. In conclusion, the performance of IGA is better than that of other methods, IGA results are satisfactory for all the functions.