Hüseyin Haklı

Artificial bee colony algorithm with variable search strategy for continuous optimization

The artificial bee colony (ABC) algorithm is a swarm-based optimization technique proposed for solving continuous optimization problems. The artificial agents of the ABC algorithm use one solution update rule during the search process. To efficiently solve optimization problems with different characteristics, we propose the integration of multiple solution update rules with ABC in this study. The proposed method uses five search strategies and counters to update the solutions. During initialization, each update rule has a constant counter content. During the search process performed by the artificial agents, these counters are used to determine the rule that is selected by the bees. Because the optimization problems and functions have different characteristics, one or more search strategies are selected and are used during the iterations according to the characteristics of the numeric functions in the proposed approach. By using the search strategies and mechanisms proposed in the present study, the artificial agents learn which update rule is more appropriate based on the characteristics of the problem to find better solutions. The performance and accuracy of the proposed method are examined on 28 numerical benchmark functions, and the obtained results are compared with various classical versions of ABC and other nature-inspired optimization algorithms. The experimental results show that the proposed algorithm, integrated and improved with search strategies, outperforms the basic variants and other variants of the ABC algorithm and other methods in terms of solution quality and robustness for most of the experiments.

A novel particle swarm optimization algorithm with Levy flight

Particle swarm optimization (PSO) is one of the well-known population-based techniques used in global optimization and many engineering problems. Despite its simplicity and efficiency, the PSO has problems as being trapped in local minima due to premature convergence and weakness of global search capability. To overcome these disadvantages, the PSO is combined with Levy flight in this study. Levy flight is a random walk determining stepsize using Levy distribution. Being used Levy flight, a more efficient search takes place in the search space thanks to the long jumps to be made by the particles. In the proposed method, a limit value is defined for each particle, and if the particles could not improve self-solutions at the end of current iteration, this limit is increased. If the limit value determined is exceeded by a particle, the particle is redistributed in the search space with Levy flight method. To get rid of local minima and improve global search capability are ensured via this distribution in the basic PSO. The performance and accuracy of the proposed method called as Levy flight particle swarm optimization (LFPSO) are examined on well-known unimodal and multimodal benchmark functions. Experimental results show that the LFPSO is clearly seen to be more successful than one of the state-of-the-art PSO (SPSO) and the other PSO variants in terms of solution quality and robustness. The results are also statistically compared, and a significant difference is observed between the SPSO and the LFPSO methods. Furthermore, the results of proposed method are also compared with the results of well-known and recent population-based optimization methods.

Support vector machines classification based on particle swarm optimization for bone age determination

This paper proposes a new approach for training support vector machines with a bone age determination system.The proposed approach is a combination of particle swarm optimization (PSO) and support vector machines (SVMs).The performance and accuracy of the proposed PSO-SVM algorithm are examined on a bone age data set.The results obtained by PSO-SVM show that PSO-SVM is more effective than the previous study based on conventional SVM. The evaluation of bone development is a complex and time-consuming task for the physicians since it may cause intraobserver and interobserver differences. In this study, we present a new training algorithm for support vector machines in order to determine the bone age in young children from newborn to 6 years old. By the new algorithm, we aimed to assist the radiologists so as to eliminate the disadvantages of the methods used in bone age determination. To achieve this purpose, primarily feature extraction procedure was performed to the left hand wrist X-ray images by using image processing techniques and the features related with the carpal bones and distal epiphysis of radius bone were obtained. Then these features were used for the input arguments of the classifier. In the classification process, a new training algorithm for support vector machines was proposed by using particle swarm optimization. When training support vector machines, particle swarm optimization was used for generating a new training instance which will represent the whole training set of the related class by using the training set. Finally, these new instances were used as the support vectors and classification process was carried out by using these new instances. The performance of the proposed method was compared with the naive Bayes, k-nearest neighborhood, support vector machines and C4.5 algorithms. As a result, it was determined that the proposed method was found successful than the other methods for bone age determination with a classification performance of 74.87%.

Levy Flight Distribution for Scout Bee in Artificial Bee Colony Algorithm

There are many population based optimization methods used for numeric functions and engineering problems. The biggest problem of these methods is setting the balance between exploration and exploitation. The artificial bee colony algorithm proposed by Karaboga gives better results compared to other known nature-inspired methods. Yet, while the ABC algorithm is better in the exploration part, which is known as exploring new places, it is not well enough in the exploitation part, which is explained as exploiting the results found. To overcome this problem, instead of random distribution of the scout bees in the search space in ABC algorithm, this paper proposed the Levy Flight ABC (LFABC) algorithm performing the distribution using Levy Flight method. By this way, it was ensured for the ABC algorithm to improve the exploitation. The two methods were tested on 10 benchmark functions, and the proposed method was seen to perform the results better.

A new approach for automating land partitioning using binary search and Delaunay triangulation

Display Omitted This paper proposes a new approach for automating land partitioning.Binary search and Delaunay triangulation is used by proposed algorithm.The time analysis of the proposed algorithm are evaluated on a study area.The results shows that the proposed approach fastens the land partitioning process.This study has set the stage for enactment of the ideal plan of land partitioning. One of the most important, yet time-consuming steps of the land consolidation process, which is related to pooling fragmented lands together, is the production of land partitioning plans. After the land redistribution process is finished, the land partitioning process begins. In that process, the locations of parcels within the blocks are determined. Due to the non-uniform geometric shapes of the blocks, the areas of the parcels cannot be divided directly. The production of an ideal land partitioning plan is not suitable automatically unless a quick, accurate process to divide the lands is secured. In this study, production of a pre-land partitioning plan is realized using both the binary search method and the Delaunay triangulation method, taking into consideration shape, size, value and road access criteria. The result of the experimental study shows that the proposed approach for dividing the parcels makes the process take place more quickly. Thus, a solid base for creating an automatic land partitioning plan-one that is closest to an ideal plan-will be provided with this study.

A New Algorithm Based on Artificial Bee Colony Algorithm for Energy Demand Forecasting in Turkey

In this study, an energy demand forecasting algorithm based on the Artificial Bee Colony with Variable Search Strategies (ABCVSS) method was proposed in order to determine Turkeys long-term energy demand. Linear and quadratic equations were used for energy demand forecasting and the coefficients of the equations were determined by means of the ABCVSS method. With the ABCVSS method, an attempt was made to enhance the local and global searching capacity of the ABC algorithm by using five different search strategies. GDP, population, imports and exports data of the period from 1979 to 2005 were chosen as the input parameters for the proposed method. Long-term energy demand was predicted through one scenario and the obtained performance from the proposed method was compared to those obtained from PSO, ACO and HAP algorithms in the literature. It was determined that the proposed method is statistically more successful than the other methods.