O. Tolga Altinoz

Bio: O. Tolga Altinoz is an academic researcher from Ankara University. The author has contributed to research in topics: Particle swarm optimization & Optimization problem. The author has an hindex of 6, co-authored 39 publications receiving 138 citations. Previous affiliations of O. Tolga Altinoz include Hacettepe University.

Multiobjective Hooke–Jeeves algorithm with a stochastic Newton–Raphson-like step-size method

Abstract: Computational optimization algorithms are focused on the improvement of meta-heuristic algorithms in a way that they can able to handle problems with more than one objective; such improved algorithms are called multiobjective optimization algorithms As the number of objectives is increased, the complexity of the algorithm is increased with respect to the computational cost Because classical optimization algorithms follow the direction of descending values by calculating derivations of the function, it is possible to evaluate a classical optimization algorithm as the core of a novel multiobjective optimization algorithm Among the classical optimization algorithms, in this study, the Hooke–Jeeves (HJ) algorithm is selected as the basis of the proposed multiobjective optimization algorithm, in which members of the proposed population-based HJ algorithm move to the Pareto front by checking two neighborhood solutions at each dimension, with a dynamic distance that is calculated by using the Newton–Raphson-like stochastic step-size method Unlike various multiobjective optimization algorithms, the performance of the proposed algorithm is greatly dependent on the decision space dimension instead of the number of objectives As the number of objectives increases without changing the decision dimension, the computational cost almost remains the same In addition, the proposed algorithm can be applied to single, multiple and many objective optimization problems In this study, initially, the behaviors of the HJ and proposed multiobjective HJ algorithms are evaluated by theoretical and graphical demonstrations Next, the performance of the proposed method is evaluated on well-known benchmark problems, and the performance of this algorithm is compared with the Nondominated Sorting Genetic Algorithm-II (NSGA-II) algorithm by using three different metric calculations Finally, the algorithm is applied to many-objective optimization problems, and the performance of the proposed algorithm is evaluated based on the obtained results

Application of Chaos Embedded PSO for PID Parameter Tuning

Abstract: Proportional-Integral-Derivative (PID) control is the most common method applied in the industry due to its simplicity. On the other hand, due to its difficulties, parameter tuning of the PID controllers are usually performed poorly. Generally, the design objectives are obtained by adjusting the controller parameters repetitively until the desired closed-loop system performance is achieved. This allows researchers to use more advanced and even some heuristic methods to achieve the optimal PID parameters. This paper focuses on application of the chaos embedded particle swarm optimization algorithm (CPSO) for PID controller tuning, and demonstrates how to employ the CPSO method to find optimal PID parameters in details. The method is applied to optimal PID parameter tuning for three typical systems with various ordered, and comparisons with the conventional PSO and the Ziegler-Nichols methods are performed. The numerical results from the simulations verify the performance of the proposed scheme.

Adaptive Integral Backstepping Motion Control for Inverted Pendulum

Abstract: The adaptive backstepping controller for inverted pendulum is designed by using the general motion control model. Backstepping is a novel nonlinear control technique based on the Lyapunov design approach, used when higher derivatives of parameter estimation appear. For easy parameter adaptation, the mathematical model of the inverted pendulum converted into the motion control model. This conversion is performed by taking functions of unknown parameters and dynamics of the system. By using motion control model equations, inverted pendulum is simulated without any information about not only parameters but also measurable dynamics. Also these results are compare with the adaptive backstepping controller which extended with integral action that given from [1]. Keywords—Adaptive Backstepping, Inverted Pendulum, Nonlinear Adaptive Control.

Particle swarm optimisation-based PID controller tuning for static power converters

Abstract: PID controllers have been widely used to control many systems. Despite the simplicity and wide usage of these controllers, determination of the controller parameters is a major problem in the control field. These parameters have a great inuence on the controllers performance. This paper presents an alternative way of parameter determination for PID controllers to control static power converters. The particle swarm optimisation (PSO) algorithm was used to obtain optimal proportional-integral derivative (PID) controller parameters for voltage regulation of a DC-DC Buck converter. Firstly, the mathematical model of the converter was obtained and the controller parameter was optimised via different types of PSO algorithms, and the algorithm which produced the best performance, is selected as the optimisation algorithm. Secondly, the performance of the optimised controller was compared with the classical method, and finally the performance of the optimised (tuned) controller under line and load variations was evaluated by hardware implementation. The controller design, the optimisation procedure, and the controller performance analysis approaches are presented in detail.

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

Abstract: 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.

Towards a quick computation of well-spread pareto-optimal solutions

Abstract: The trade-off between obtaining a good distribution of Pareto-optimal solutions and obtaining them in a small computational time is an important issue in evolutionary multi-objective optimization (EMO). It has been well established in the EMO literature that although SPEA produces a better distribution compared to NSGA-II, the computational time needed to run SPEA is much larger. In this paper, we suggest a clustered NSGA-II which uses an identical clustering technique to that used in SPEA for obtaining a better distribution. Moreover, we propose a steady-state MOEA based on e-dominance concept and efficient parent and archive update strategies. Based on a comparative study on a number of two and three objective test problems, it is observed that the steady-state MOEA achieves a comparable distribution to the clustered NSGA-II with a much less computational time.

A Survey on Parallel Particle Swarm Optimization Algorithms

Abstract: Most of the complex research problems can be formulated as optimization problems. Emergence of big data technologies have also commenced the generation of complex optimization problems with large size. The high computational cost of these problems has rendered the development of optimization algorithms with parallelization. Particle swarm optimization (PSO) algorithm is one of the most popular swarm intelligence-based algorithm, which is enriched with robustness, simplicity and global search capabilities. However, one of the major hindrance with PSO is its susceptibility of getting entrapped in local optima and; alike other evolutionary algorithms the performance of PSO gets deteriorated as soon as the dimension of the problem increases. Hence, several efforts are made to enhance its performance that includes the parallelization of PSO. The basic architecture of PSO inherits a natural parallelism, and receptiveness of fast processing machines has made this task pretty convenient. Therefore, parallelized PSO (PPSO) has emerged as a well-accepted algorithm by the research community. Several studies have been performed on parallelizing PSO algorithm so far. Proposed work presents a comprehensive and systematic survey of the studies on PPSO algorithms and variants along with their parallelization strategies and applications.

A State-of-the-Art Survey on Multidimensional Scaling-Based Localization Techniques

Abstract: Current and future wireless applications strongly rely on precise real-time localization. A number of applications, such as smart cities, Internet of Things (IoT), medical services, automotive industry, underwater exploration, public safety, and military systems require reliable and accurate localization techniques. Generally, the most popular localization/positioning system is the global positioning system (GPS). GPS works well for outdoor environments but fails in indoor and harsh environments. Therefore, a number of other wireless local localization techniques are developed based on terrestrial wireless networks, wireless sensor networks (WSNs), and wireless local area networks (WLANs). Also, there exist localization techniques which fuse two or more technologies to find out the location of the user, also called signal of opportunity-based localization. Most of the localization techniques require ranging measurements, such as time of arrival (ToA), time difference of arrival (TDoA), direction of arrival (DoA), and received signal strength (RSS). There are also range-free localization techniques which consider the proximity information and do not require the actual ranging measurements. Dimensionality reduction techniques are famous among the range free localization schemes. Multidimensional scaling (MDS) is one of the dimensionality reduction technique which has been used extensively in the recent past for wireless networks localization. In this paper, a comprehensive survey is presented for MDS and MDS-based localization techniques in WSNs, IoT, cognitive radio networks, and 5G networks.