Osman Kaan Erol

A new optimization method: Big Bang-Big Crunch

Nature is the principal source for proposing new optimization methods such as genetic algorithms (GA) and simulated annealing (SA) methods. All traditional evolutionary algorithms are heuristic population-based search procedures that incorporate random variation and selection. The main contribution of this study is that it proposes a novel optimization method that relies on one of the theories of the evolution of the universe; namely, the Big Bang and Big Crunch Theory. In the Big Bang phase, energy dissipation produces disorder and randomness is the main feature of this phase; whereas, in the Big Crunch phase, randomly distributed particles are drawn into an order. Inspired by this theory, an optimization algorithm is constructed, which will be called the Big Bang-Big Crunch (BB-BC) method that generates random points in the Big Bang phase and shrinks those points to a single representative point via a center of mass or minimal cost approach in the Big Crunch phase. It is shown that the performance of the new (BB-BC) method demonstrates superiority over an improved and enhanced genetic search algorithm also developed by the authors of this study, and outperforms the classical genetic algorithm (GA) for many benchmark test functions.

A stochastic neighborhood search approach for airport gate assignment problem

An appropriate and efficient gate assignment is of great importance in airports since it plays a major role in the revenue obtained from the airport operations. In this study, we have focused mainly on maximum gate employment, or in other words minimize the total duration of un-gated flights. Here, we propose a method that combines the benefits of heuristic approaches with some stochastic approach instead of using a purely probabilistic approach to top-down solution of the problem. The heuristic approaches are usually used in order to provide a fast solution of the problem and later stochastic searches are used in order to ameliorate the previous results of the heuristic approach whenever possible. The proposed method generates an assignment order for the whole planes that corresponds to assignment priority. The ordering process is followed by the allocation step. Since, in practice, each airport has its own physical architecture, there have been arisen many constraints mainly concerning airplane types and parking lots in this step. Sequentially handling the plane ordering and allocation phases provides us great modularity in handling the constraints. The effectiveness of the proposed methodology has been tried to be illustrated firstly on fictively generated flight schedule data and secondly on the real world data obtained from a real world application developed for Istanbul Ataturk Airport.

Big Bang - Big Crunch optimization algorithm hybridized with local directional moves and application to target motion analysis problem

Big Bang - Big Crunch (BB-BC) optimization algorithm relies on one of the theories of the evolution of the universe; namely, the Big Bang and Big Crunch Theory [1]. It was proposed as a novel optimization method in 2006 and is shown to be capable of quick convergence. In this work, local search moves are injected in between the original “banging” and “crunching” phases of the optimization algorithm. These phases preserve their structures; but the representative point (“best” or “fittest” point) attained after crunching phase of the iteration is modified with local directional moves using the previous representative points. This hybridization scheme smoothens the path going to optima and decreases the process time for reaching the global minima. The results over benchmark test functions have proven that BB-BC Algorithm enhanced with local directional moves has provided more accuracy with the same computation time or for the same number of function evaluations. As a real world case study, the newly proposed routine is applied in target motion analysis problem where the basic parameters defining the target motion is estimated through noise corrupted measurement data.

A Genetic Programming Classifier Design Approach for Cell Images

This paper describes an approach for the use of genetic programming (GP) in classification problems and it is evaluated on the automatic classification problem of pollen cell images. In this work, a new reproduction scheme and a new fitness evaluation scheme are proposed as advanced techniques for GP classification applications. Also an effective set of pollen cell image features is defined for cell images. Experiments were performed on Bangor/Aberystwyth Pollen Image Database and the algorithm is evaluated on challenging test configurations. We reached at 96 % success rate on the average together with significant improvement in the speed of convergence.

Evolutionary algorithm with modifications in the reproduction phase

All traditional evolutionary algorithms are heuristic population-based search procedures that incorporate random variation and selection. The number of calculations in these algorithms is generally proportional to population size. Classical genetic algorithms (GAs), for instance, require the calculation of the fitness values of every individual in the population. A new evolutionary algorithm that combines two basic operators of GAs: namely, selection and crossover are proposed. The new operator is applied to two randomly selected individuals from the existing population. It is shown that the number of calculations is reduced greatly compared to classical GAs, while performance is enhanced on the functions studied. Moreover, simulation studies indicate that the new algorithm can produce a performance comparable with the more intelligent and hybrid evolutionary techniques given in the related literature.

Method for providing live content during playback of recorded streams in personal video recorders

Personal video recorders provide the facility of recording live broadcast streams thus users can replay their recordings whenever they want. However, during playback of those recorded streams, users are isolated from the live content (e.g., latest news) in which they might be interested. In this paper, we propose an MPEG-2 transport stream compatible method which can inform the user about live content thats in user interest area during playback. The method-specific structures, algorithms and an implementation on a Linux platform are presented in the paper

Design of optimum fuzzy controller using genetic algorithms

This paper describes a new method for determining optimal fuzzy controller decision table. By the use of genetic algorithm the need for heuristic fuzzy rules is omitted. The advantages and limitations of the method described are discussed. Simulations have been carried out to demonstrate the effectiveness of the new method, and the results are compared with a classical PID controller to show the improvement in performance.

An optimization method for preventive control using differential evolution with consecutive search space reduction

This paper proposes a new methodology to improve the population based optimization techniques applied for preventive control actions enhancing power system security. The preventive control studied includes both generation rescheduling and load curtailment. We first investigate how the size of the search space affects and improves the best solution obtained in the optimization process. Then, we develop a new methodology that involves a number of optimization algorithms running consecutively as the size of the search space of each algorithm is reduced according to the objective function. The extensive computational requirement for dynamic security assessment during the optimization processes is overcome by the application of neural networks. The methodology is successfully applied for solving the security constrained optimization problem of a 16-generator 68-bus test system with both continuous and discrete decision variables using consecutive differential evolution optimization algorithms.

Scalable Super-Resolution Imaging

In this study, we have developed a novel method to obtain high resolution images using an ordinary digital camera. Using successively taken images, our fusion algorithm has a profound effect in the quality of the image which contains richer independent pixels than the originals do. This method, which aims to increase the spatial resolution by adding dependent data to the picture, is superior over classical super-resolution methods. Seven DOF industrial robotic arm, PA10-7C, by Mitsubishi is utilized for the experimental part of the work.