A. Egemen Yilmaz

Calculation of optimized parameters of rectangular patch antenna using gravitational search algorithm

Gravitational Search Algorithm (GSA) is a novel optimization algorithm developed recently. Hence, it has not yet been applied for determination of the optimized parameters of microstrip patch antennas. Therefore, in this study, GSA has been applied for calculation of the length and width of the rectangular patch antenna. These parameters of rectangular patch antenna have been obtained under various resonant frequencies, substrate permittivity and thickness of the antenna.

A review on motion control of the Underwater Vehicles

Unmanned Underwater Vehicles have gained popularity for the last decades, especially for the purpose of not risking human life in dangerous operations. On the other hand, underwater environment introduces numerous challenges in control, navigation and communication of such vehicles. Certainly, this fact makes the development of these vehicles more interesting and engineering-wise more attractive. In this review study, among the mentioned problems, we focus on the control of underwater vehicles, particularly the motion control. We try to summarize the evolution of the underwater vehicle motion control studies throughout the last two decades, and classify them.

Potential applications of fuzzy logic in music

Even though the application spectrum of the fuzzy logic is quite wide, fuzzy based implementations in music are rarely encountered. In this study, we try to give the definitions of the problems in music theory; and we try to adapt fuzzy based reasoning particularly for the counterpoint problem. Despite the fact that this study is currently limited to note-against-note two-voice counterpoint technique, the approach can extended to more complicated problems of harmonization (either vocal or orchestral), improvization, and even composition.

Reference point based distributed computing for multiobjective optimization

As the computational complexity of the problem and/or the number of objectives increases, a large population has to be evaluated at each generation of algorithm, and this process needs more computational resources, or requires more time for the same computational resource. However, distributing the tasks into different processors (or cores) is a good solution in speeding up the process overall. In this study, a novel and pragmatic distributed computing approach for multiobjective evolutionary optimization algorithm is proposed. Instead of dividing the objective space into pre-defined cone-domination principles, as proposed in an earlier study, a distribution of reference points initialized on a hyper-plane spanning the entire objective space is assigned to different processors and the R-NSGA-II procedure is invoked to find respective partial efficient fronts. Our results show that the proposed distributed computing approach reduces the overall computational effort compared to that needed with a single-processor method.

Evaluation of the migrated solutions for distributing reference point-based multi-objective optimization algorithms

Abstract As the number of objectives and/or dimension of a given problem increases, or a real-world optimization problem is modeled in more detail, the optimization algorithm requires more computation time if the computational resources are fixed. Therefore, some more tools are needed to be developed for deployment of these resources. The parallelization is one of these tools based on distribution of the overall problem to different computational units. In this study, a distributed computing approach for multi-objective evolutionary optimization algorithms is proposed by application of a migration policy which is based on sharing the information for inter-processor collaboration. This idea is also intensified with the crossover operator at the evolutionary algorithms where the migrated solutions are applied to the crossover operator so that the performance of the overall approach increases. Besides, a new metric is defined for evaluation of the performance of the proposed distribution methodology. The performance of the proposed approaches is evaluated via well-known two- and three-objective well-known test problems.

A multiobjective optimization approach via systematical modification of the desirability function shapes

In this study, a method for solution of the multi-objective optimization problems via the desirability function aided particle swarm optimization has been proposed. The desirability function has been applied for normalization of each objective and then aggregation to a single objective. The geometric mean of the desirability values regarding each objective has been calculated as a part of the method. On the other hand, using a single-objective optimization algorithm yields a single solution rather than a complete solution set. Hence, the idea of changing the shapes of the desirability functions is implemented in order to achieve a complete solution set; in fact, this constitutes the main theme of this study. Therefore, the multi-objective problem has been degraded to a single-objective one, and it has been solved numerous times for each alternative desirability function shape. As a result, a set of biased solutions has been obtained in a very practical manner.

Fuzzy logic based four-voice choral harmonization in traditional style

Recently, there has been a great tendency to computer aided music generation especially due to the rapid developments in technology. In this area, many researchers handled a variety of methods in order to perform this fact by taking into account some kinds of rules defined by music theory. Unfortunately, handling all these complicated rules by means of advanced methods would not be sufficient. For that reason, most of the methods and applications presented in literature were criticized to be “lacking of sprit” like contributing a human sense (for harmonization) even by the developer of that particular method. In this study, we consider the particular problem of four-part choral harmonization to overcome this issue, and try to discuss at which points fuzzy logic is applicable throughout the solution. Departing from the fact that the definition of consonance can be related to a fuzzy set, we try to handle the harmonization problem by defining some mathematical operators on the fuzzy sets encountered.

Conjunction of heuristic algorithms with multidimensional scaling for localization at wireless ad-hoc networks

In wireless networks, the nodes are generally distributed in a field randomly. The topological information about the nodes is only supported from the distances between each node. This distance information may not be available due to the imperfect conditions among nodes. The positions of the distributed nodes on the field by using available distance data is called node localization problem. One of the methods which can be solved this problem is classical multidimensional algorithm (cMDS). In this study, cMDS is improved with the aid of heuristic algorithms. Three heuristic algorithms (simulated annealing, particle swarm optimization and genetic algorithm) are applied and results are compared with each other.

Particle Swarm Optimization with social exclusion and its application in electromagnetics

The behavior of Particle Swarm Optimization (PSO), a population based optimization algorithm, depends on the movements of the particles and the attractions among them. This behavior was extracted from the observations of the swarms in nature. Every swarm desires to remain powerful in order to survive in nature and to protect its descendants. Therefore, the weakest members in the swarm are isolated, and generally abandoned to live on their own resources. This act is known as social exclusion. In this research, this phenomenon is incorporated to PSO. At the early phase of time-line, the swarm is divided into two groups based on their cost/fitness values. Each group proceeds their own journey without the knowledge of other group. This new algorithm is named as Social Exclusion-PSO (SEPSO). First, the performance of this new algorithm was evaluated/compared with an inertia weight PSO via unimodal, multimodal, expended benchmark functions, and then, it is applied to the circular antenna array design problem. For each implementation, the performance of two sub-populations and the undivided population are presented to demonstrate and compare the behaviour of the socially excluded swarm. The results show that excluding the members with the worst cost values from the population increases the performance of the algorithm in terms of global best solution with approximately 20% smaller number of function evaluations.

Impact of problem dimension on the execution time of parallel particle swarm optimization implementation

In this study, parallel particle swarm optimization algorithm has been investigated as regards the impact of the problem properties on the execution time. Two major factors affect the performance of parallel evolutionary algorithms: the population size and the problem dimension. In this study, five well-know benchmark functions have been applied with different dimensions. Then, these functions have been compared as regards the execution time. Finally, uniformly distributed population has been compared with the chaotic distributed population based on the dimension and population size from previous discussion.