• Journal of Institute of Control, Robotics and Systems
• /
• v.19 no.10
• /
• pp.867-872
• /
• 2013

Many practical processes in industry have nonlinearities of some forms. One commonly encountered form is actuator saturation which can cause a detrimental effect known as integrator windup. Therefore, a strategy of attenuating the effects of integrator windup is required to guarantee the stability and performance of the overall control system. In this paper, optimal tuning of a PID (Proportional-Integral-Derivative) controller with an anti-windup scheme is presented to enhance the tracking performance of the PID control system in the presence of the actuator saturation. First, we investigate effective anti-windup schemes. Then, the parameters of both the PID controller and the anti-windup scheme are optimally tuned by an EA (Evolutionary Algorithm) such as the IAE (Integral of Absolute Error) is minimized. A set of simulation works on two high-order processes demonstrates the benefit of the proposed method.

• The Journal of the Korea Contents Association
• /
• v.9 no.10
• /
• pp.40-50
• /
• 2009

Incorrect deployment of RFID readers occurs reader-to-reader interferences in many applications using RFID technologies. Reader-to-reader interference occurs when a reader transmits a signal that interferes with the operation of another reader, thus preventing the second reader from communicating with tags in its interrogation zone. Interference detected by one reader and caused by another reader is referred to as a reader collision. In RFID systems, the reader collision problem is considered to be the bottleneck for the system throughput and reading efficiency. In this paper, we propose a novel RFID reader anti-collision algorithm based on evolutionary algorithm(EA). First, we analyze characteristics of RFID antennas and build database. Also, we propose EA encoding algorithm, fitness algorithm and genetic operators to deploy antennas efficiently. To show superiority of our proposed algorithm, we simulated our proposed algorithm. In the result, our proposed algorithm obtains 95.45% coverage rate and 10.29% interference rate after about 100 generations.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.31 no.2
• /
• pp.141-155
• /
• 2006

In this paper, we address an extended hub-and-spoke transportation network design problem (EHSNP). In the existing hub location problems, the location and number of spokes, and shipments on spokes are given as input data. These may, however, be viewed as the variables according to the areas which they cover. Also, the vehicle routing in each spoke needs to be considered to estimate the network cost more correctly. The EHSNP is a problem of finding the location of hubs and spokes, and pickup/delivery routes from each spoke, while minimizing the total related transportation cost in the network. The EHSNP is an integrated problem that consists of several interrelated sub-problems. To solve EHSNP, we present an approach based on a symbiotic evolutionary algorithm (symbiotic EA), which are known as an efficient tool to solve complex integrated optimization problems. First, we propose a framework of symbiotic EA for EHSNP and its genetic elements suitable for each sub-problem. To analyze the proposed algorithm, the extensive experiments are performed with various test-bed problems. The results show that the proposed algorithm is promising in solving the EHSNP.

• Proceedings of the Korea Contents Association Conference
• /
• 2009.05a
• /
• pp.715-719
• /
• 2009

Incorrect deployment of RFID readers occurs reader-to-reader interferences in many applications using RFID technologies. Reader-to-reader interference occurs when a reader transmits a signal that interferes with the operation of another reader, thus preventing the second reader from communicating with tags in its interrogation zone. Interference detected by one reader and caused by another reader is referred to as a reader collision. In RFID systems, the reader collision problem is considered to be the bottleneck for the system throughput and reading efficiency. In this paper, we propose a novel RFID reader anti-collision algorithm based on evolutionary algorithm(EA). First, we analyze characteristics of RFID antennas and build database. Also, we propose EA encoding algorithm, fitness algorithm and genetic operators to deploy antennas efficiently. To show superiority of our proposed algorithm, we simulated our proposed algorithm. In the result, our proposed algorithm obtains 95.45% coverage rate and 10.29% interference rate after about 100 generations.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.32 no.1
• /
• pp.77-91
• /
• 2007

In this paper, we present a symbiotic evolutionary algorithm for multi-objective optimization. The goal in multi-objective evolutionary algorithms (MOEAs) is to find a set of well-distributed solutions close to the true Pareto optimal solutions. Most of the existing MOEAs operate one population that consists of individuals representing the entire solution to the problem. The proposed algorithm has a two-leveled structure. The structure is intended to improve the capability of searching diverse and food solutions. At the lower level there exist several populations, each of which represents a partial solution to the entire problem, and at the upper level there is one population whose individuals represent the entire solutions to the problem. The parallel search with partial solutions at the lower level and the Integrated search with entire solutions at the upper level are carried out simultaneously. The performance of the proposed algorithm is compared with those of the existing algorithms in terms of convergence and diversity. The optimization problems with continuous variables and discrete variables are used as test-bed problems. The experimental results confirm the effectiveness of the proposed algorithm.

• Industrial Engineering and Management Systems
• /
• v.11 no.3
• /
• pp.215-223
• /
• 2012

This paper focuses on three very similar evolutionary algorithms: genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE). While GA is more suitable for discrete optimization, PSO and DE are more natural for continuous optimization. The paper first gives a brief introduction to the three EA techniques to highlight the common computational procedures. The general observations on the similarities and differences among the three algorithms based on computational steps are discussed, contrasting the basic performances of algorithms. Summary of relevant literatures is given on job shop, flexible job shop, vehicle routing, location-allocation, and multimode resource constrained project scheduling problems.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.24 no.3
• /
• pp.330-336
• /
• 2014

Many industrial processes such as continuous stirred tank reactors(CSTRs), desalination plant, distillation columns, pH neutralization processes and so on exhibit highly nonlinear characteristic and time-varying behavior during operation. The control of such processes has been challenging to control engineers. Hence, a variety of forms of PID controllers and their tuning rules for industrial processes have been developed to guarantee the best performance. In this paper, a scheme that designs the practical PID controller with an anti-windup strategy incorporating with an evolutionary algorithm(EA) is presented for the concentration control of a nonisothermal CSTR. EA is used to tune the parameters of the overall PID control process with anti-windup by minimizing the integral of absolute error(IAE). Simulation works for reference tracking and disturbance rejecting performances and robustness to parameter changes show the feasibility of using the proposed method.

• Steel and Composite Structures
• /
• v.24 no.2
• /
• pp.227-237
• /
• 2017

This paper presents optimization of a long-span portal steel frame under dynamic wind loads using a surrogate-assisted evolutionary algorithm. Long-span portal steel frames are often used in low-rise industrial and commercial buildings. The structure needs be able to resist the wind loads, and at the same time it should be as light as possible in order to be cost-effective. In this work, numerical model of a portal steel frame is constructed using structural analysis program (SAP2000), with the web-heights at five locations of I-sections of the columns and rafters as the decision variables. In order to evaluate the performance of a given design under dynamic wind loading, the equivalent static wind load (ESWL) is obtained from a database of wind pressures measured in wind tunnel tests. A modified formulation of the problem compared to the one available in the literature is also presented, considering additional design constraints for practicality. Evolutionary algorithms (EA) are often used to solve such non-linear, black-box problems, but when each design evaluation is computationally expensive (e.g., in this case a SAP2000 simulation), the time taken for optimization using EAs becomes untenable. To overcome this challenge, we employ a surrogate-assisted evolutionary algorithm (SAEA) to expedite the convergence towards the optimum design. The presented SAEA uses multiple spatially distributed surrogate models to approximate the simulations more accurately in lieu of commonly used single global surrogate models. Through rigorous numerical experiments, improvements in results and time savings obtained using SAEA over EA are demonstrated.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.8 no.2
• /
• pp.326-354
• /
• 2014

In this paper, a new green clustering algorithm is proposed to be as a first approach in the framework of an energy efficient strategy for centralized enterprise high-density WLANs. Traditionally, in order to maintain the network coverage, all the APs within the WLAN have to be powered-on. Nevertheless, the new algorithm can power-off a large proportion of APs while the coverage is maintained as its always-on counterpart. The two main components of the new approach are the faster procedure based on K-means and the more accurate procedure based on Evolutionary Algorithm (EA), respectively. The two procedures are processes in parallel for different designed requirements and there is information interaction in between. In order to implement the new algorithm, EA is applied to handle the optimization of multiple objectives. Moreover, we adapt the method for selection and recombination, and then introduce a new operator for mutation. This paper also presents simulations in scenarios modeled with ray-tracing method and FDTD technique, and the results show that about 67% to 90% of energy consumption can be saved while it is able to maintain the original network coverage during periods when few users are online or the traffic load is low.

• International Journal of Control, Automation, and Systems
• /
• v.1 no.2
• /
• pp.184-193
• /
• 2003

Biaffine Matrix Inequality (BMI) is known to provide the most general framework in control synthesis, but problems involving BMI's are very difficult to solve because nonconvex optimization should be solved. In the previous paper, we proposed a new solver for problems involving BMI's using Evolutionary Algorithms (EA). In this paper, we solve several control synthesis examples such as Reduced-order control, Simultaneous stabilization, Multi-objective control, $H_{\infty}$ optimal control, Maxed $H_2$ / $H_{\infty}$control design, and Robust $H_{\infty}$ control. Each of these problems is formulated as the standard BMI form, and solved by the proposed algorithm. The performance in each case is compared with those of conventional methods.