The present study has been taken up to emphasize the role of the hybridization process for optimizing a given reinforced concrete (RC) frame. Although various primary techniques have been hybrid in the past with varying degree of success, the effect of hybridization of enhanced versions of standard optimization techniques has found little attention. The focus of the current study is to see if it is possible to maintain and carry the positive effects of enhanced versions of two different techniques while using their hybrid algorithms. For this purpose, enhanced versions of standard particle swarm optimization (PSO) and a standard gravitational search algorithm (GSA), were considered for optimizing an RC frame. The enhanced version of PSO involves its democratization by considering all good and bad experiences of the particles, whereas the enhanced version of the GSA is made self-adaptive by considering a specific range for certain parameters, like the gravitational constant and a set of agents with the best fitness values. The optimization process, being iterative in nature, has been coded in C++. The analysis and design procedure is based on the specifications of Indian codes. Two distinct advantages of enhanced versions of standard PSO and GSA, namely, better capability to escape from local optima and a faster convergence rate, have been tested for the hybrid algorithm. The entire formulation for optimal cost design of a frame includes the cost of beams and columns. The variables of each element of structural frame have been considered as continuous and rounded off appropriately to consider practical limitations. An example has also been considered to emphasize the validity of this optimum design procedure.
Five models and methodology are discussed in this paper for constructing classifiers capable of recognizing in real time the type of fuel injected into a diesel engine cylinder to accuracy acceptable in practical technical applications. Experimental research was carried out on the dynamic engine test facility. The signal of in-cylinder and in-injection line pressure in an internal combustion engine powered by mineral fuel, biodiesel or blends of these two fuel types was evaluated using the vibro-acoustic method. Computational intelligence methods such as classification trees, particle swarm optimization and random forest were applied.
The Bulletin of the Polish Academy of Sciences: Technical Sciences (Bull.Pol. Ac.: Tech.) is published bimonthly by the Division IV Engineering Sciences of the Polish Academy of Sciences, since the beginning of the existence of the PAS in 1952. The journal is peer‐reviewed and is published both in printed and electronic form. It is established for the publication of original high quality papers from multidisciplinary Engineering sciences with the following topics preferred: Artificial and Computational Intelligence, Biomedical Engineering and Biotechnology, Civil Engineering, Control, Informatics and Robotics, Electronics, Telecommunication and Optoelectronics, Mechanical and Aeronautical Engineering, Thermodynamics, Material Science and Nanotechnology, Power Systems and Power Electronics.
Journal Metrics: JCR Impact Factor 2018: 1.361, 5 Year Impact Factor: 1.323, SCImago Journal Rank (SJR) 2017: 0.319, Source Normalized Impact per Paper (SNIP) 2017: 1.005, CiteScore 2017: 1.27, The Polish Ministry of Science and Higher Education 2017: 25 points.
Abbreviations/Acronym: Journal citation: Bull. Pol. Ac.: Tech., ISO: Bull. Pol. Acad. Sci.-Tech. Sci., JCR Abbrev: B POL ACAD SCI-TECH Acronym in the Editorial System: BPASTS.
This research presents a new technique which includes the principle of a Bezier curve and Particle Swarm Optimization (PSO) together, in order to design the planar dipole antenna for the two different targets. This technique can improve the characteristics of the antennas by modifying copper textures on the antennas with a Bezier curve. However, the time to process an algorithm will be increased due to the expansion of the solution space in optimization process. So as to solve this problem, the suitable initial parameters need to be set. Therefore this research initialized parameters with reference antenna parameters (a reference antenna operates on 2.4 GHz for IEEE 802.11 b/g/n WLAN standards) which resulted in the proposed designs, rapidly converted into the goals. The goal of the first design is to reduce the size of the antenna. As a result, the first antenna is reduced in the substrate size from areas of 5850 mm2 to 2987 mm2(48.93% approximately) and can also operates at 2.4 GHz (2.37 GHz to 2.51 GHz). The antenna with dual band application is presented in the second design. The second antenna is operated at 2.4 GHz (2.40 GHz to 2.49 GHz) and 5 GHz (5.10 GHz to 5.45 GHz) for IEEE 802.11 a/b/g/n WLAN standards.
This paper presents the resolution of the optimal reactive power dispatch (ORPD) problem and the control of voltages in an electrical energy system by using a hybrid algorithm based on the particle swarmoptimization (PSO) method and interior point method (IPM). The IPM is based on the logarithmic barrier (LB-IPM) technique while respecting the non-linear equality and inequality constraints. The particle swarmoptimization-logarithmic barrier-interior point method (PSO-LB-IPM) is used to adjust the control variables, namely the reactive powers, the generator voltages and the load controllers of the transformers, in order to ensure convergence towards a better solution with the probability of reaching the global optimum. The proposed method was first tested and validated on a two-variable mathematical function using MATLAB as a calculation and execution tool, and then it is applied to the ORPD problem to minimize the total active losses in an electrical energy network. To validate the method a testwas carried out on the IEEE electrical energy network of 57 buses.
A transformer is an important part of power transmission and transformation equipment. Once a fault occurs, it may cause a large-scale power outage. The safety of the transformer is related to the safe and stable operation of the power system. Aiming at the problem that the diagnosis result of transformer fault diagnosis method is not ideal and the model is unstable, a transformer fault diagnosis model based on improved particle swarm optimization online sequence extreme learning machine (IPSO-OS-ELM) algorithm is proposed. The improved particle swarmoptimization algorithm is applied to the transformer fault diagnosis model based on the OS-ELM, and the problems of randomly selecting parameters in the hidden layer of the OS-ELM and its network output not stable enough, are solved by optimization. Finally, the effectiveness of the improved fault diagnosis model in improving the accuracy is verified by simulation experiments.
The proportional-integral-derivative (PID) controller is widely used in various industrial applications such as process control, motor drives, magnetic and optical memory, automotive, flight control and instrumentation. PID tuning refers to the generation of PID parameters (Kp, Ki, Kd) to obtain the optimum fitness value for any system. The determination of the PID parameters is essential for any system that relies on it to function in a stable mode. This paper proposes a method in designing a predictive PID controller system using particle swarm optimization (PSO) algorithm for direct current (DC) motor application. Extensive numerical simulations have been done using the Mathwork’s Matlab simulation environment. In order to gain full benefits from the PSO algorithm, the PSO parameters such as inertia weight, iteration number, acceleration constant and particle number need to be carefully adjusted and determined. Therefore, the first investigation of this study is to present a comparative analysis between two important PSO parameters; inertia weight and number of iteration, to assist the predictive PID controller design. Simulation results show that inertia weight of 0.9 and iteration number 100 provide a good fitness achievement with low overshoot and fast rise and settling time. Next, a comparison between the performance of the DC motor with PID-PSO, with PID of gain 1, and without PID were also discussed. From the analysis, it can be concluded that by tuning the PID parameters using PSO method, the best gain in performance may be found. Finally, when comparing between the PID-PSO and its counterpart, the PI-PSO, the PID-PSO controller gives better performance in terms of robustness, low overshoot (0.005%), low minimum rise time (0.2806 seconds) and low settling time (0.4326 seconds).
The paper presents a three-phase grid-tied converter operated under unbalanced and distorted grid voltage conditions, using a multi-oscillatory current controller to provide high quality phase currents. The aim of this study is to introduce a systematic design of the current control loop. A distinctive feature of the proposed method is that the designer needs to define the required response and the disturbance characteristic, rather than usually unintuitive coefficients of controllers. Most common approach to tuning a state-feedback controller use linear-quadratic regulator (LQR) technique or pole-placement method. The tuning process for those methods usually comes down to guessing several parameters. For more complex systems including multi-oscillatory terms, control system tuning is unintuitive and cannot be effectively done by trial and error method. This paper proposes particle swarm optimization to find the optimal weights in a cost function for the LQR procedure. Complete settings for optimization procedure and numerical model are presented. Our goal here is to demonstrate an original design workflow. The proposed method has been verified in experimental study at a 10 kW laboratory setup.
The near net shaped manufacturing ability of squeeze casting process requiresto set the process variable combinations at their optimal
levels to obtain both aesthetic appearance and internal soundness of the cast parts. The aesthetic and internal soundness of cast parts deal
with surface roughness and tensile strength those can readily put the part in service without the requirement of costly secondary
manufacturing processes (like polishing, shot blasting, plating, hear treatment etc.). It is difficult to determine the levels of the process
variable (that is, pressure duration, squeeze pressure, pouring temperature and die temperature) combinations for extreme values of the
responses (that is, surface roughness, yield strength and ultimate tensile strength) due to conflicting requirements. In the present
manuscript, three population based search and optimization methods, namely genetic algorithm (GA), particle swarm optimization (PSO)
and multi-objective particle swarm optimization based on crowding distance (MOPSO-CD) methods have been used to optimize multiple
outputs simultaneously. Further, validation test has been conducted for the optimal casting conditions suggested by GA, PSO and
MOPSO-CD. The results showed that PSO outperformed GA with regard to computation time.
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