Ling Wang

Comparative performance analysis of various binary coded PSO algorithms in multivariable PID controller design

In this paper, comparative performance analysis of various binary coded PSO algorithms on optimal PI and PID controller design for multiple inputs multiple outputs (MIMO) process is stated. Four algorithms such as modified particle swarm optimization (MPSO), discrete binary PSO (DBPSO), modified discrete binary PSO (MBPSO) and probability based binary PSO (PBPSO) are independently realized using MATLAB. The MIMO process of binary distillation column plant, described by Wood and Berry, with and without a decoupler having two inputs and two outputs is considered. Simulations are carried out to minimize two objective functions, that is, time integral of absolute error (ITAE) and integral of absolute error (IAE) with single stopping criterion for each algorithm called maximum number of fitness evaluations. The simulation experiments are repeated 20 times with each algorithm in each case. The performance measures for comparison of various algorithms such as mean fitness, variance of fitness, and best fitness are computed. The transient performance indicators and computation time are also recorded. The inferences are made based on analysis of statistical data obtained from 20 trials of each algorithm and after having comparison with some recently reported results about same MIMO controller design employing real coded genetic algorithm (RGA) with SBX and multi-crossover approaches, covariance matrix adaptation evolution strategy (CMAES), differential evolution (DE), modified continuous PSO (MPSO) and biggest log modulus tuning (BLT). On the basis of simulation results PBPSO is identified as a comparatively better method in terms of its simplicity, consistency, search and computational efficiency.

A novel modified binary differential evolution algorithm and its applications

Differential Evolution (DE) is a simple yet efficient global optimization algorithm. However, the standard DE and most of its variants operate in the continuous space, which cannot solve the binary-coded optimization problems directly. To tackle this problem, this paper proposes a novel modified binary differential evolution algorithm (NMBDE) inspired by the concept of Estimation of Distribution Algorithm and DE. A novel probability estimation operator is developed for NMBDE, which can efficiently maintain diversity of population and achieve a better tradeoff between the exploration and exploitation capabilities by cooperating with the selection operator. Furthermore, the parameter study of NMBDE is run and the analysis is performed to improve the global search ability and scalability of algorithm. The effectiveness and efficiency of NMBDE was verified in applications to the numerical optimization and multidimensional knapsack problems. The experimental results demonstrate that NMBDE has the better global search ability and outperforms the discrete binary DE, the modified binary DE, the discrete binary Particle Swarm Optimization and the binary Ant System in terms of accuracy and convergence speed.

A Discrete Harmony Search Algorithm

Harmony search (HS), inspired by the music improvisation process, is a new meta-heuristic optimization method and has been used to tackle various optimization problems in discrete and continuous space successfully. However, the standard HS algorithm is not suitable for settling discrete binary problems. To extend HS to solve the binary-coded problems effectively, a novel discrete binary harmony search (DBHS) algorithm is proposed in this paper. A new pitch adjustment rule is developed to enhance the optimization ability of DBHS. Then parameter studies are performed to investigate the properties of DBHS, and the recommended parameter values are given. The results of numerical experiments demonstrate that the proposed DBHS is valid and outperforms the discrete binary particle swarm optimization algorithm and the standard HS.

A modified binary differential evolution algorithm

Differential evolution (DE) is a simple, yet efficient global optimization algorithm. As the standard DE and most of its variants operate in the continuous space, this paper presents a modified binary differential evolution algorithm (MBDE) to tackle the binary-coded optimization problems. A novel probability estimation operator inspired by the concept of distribution of estimation algorithm is developed, which enables MBDE to manipulate binary-valued solutions directly and provides better tradeoff between exploration and exploitation cooperated with the other operators of DE. The effectiveness and efficiency of MBDE is verified in application to numerical optimization problems. The experimental results demonstrate that MBDE outperforms the discrete binary DE, the discrete binary particle swarm optimization and the binary ant system in terms of both accuracy and convergence speed on the suite of benchmark functions.

A Simple Human Learning Optimization Algorithm

This paper presents a novel Simple Human Learning Optimization (SHLO) algorithm, which is inspired by human learning mechanisms. Three learning operators are developed to generate new solutions and search for the optima by mimicking the learning behaviors of human. The 0-1 knapsack problems are adopted as benchmark problems to validate the performance of SHLO, and the results are compared with those of binary particle swarm optimization (BPSO), modified binary differential evolution (MBDE), binary fruit fly optimization algorithm (bFOA) and adaptive binary harmony search algorithm (ABHS). The experimental results demonstrate that SHLO significantly outperforms BPSO, MBDE, bFOA and ABHS. Considering the ease of implementation and the excellence of global search ability, SHLO is a promising optimization tool.

A novel hybrid binary PSO algorithm

The continuous PSO algorithm has been widely researched and also applied as an intelligent computational technique to solve problems requiring iterative solutions based on some predefined objective function. However, the research on binary version of PSO (DBPSO) is still underway. The major research concerns are to accelerate the convergence speed retaining the search ability and reliability of the algorithm. To achieve this, a novel hybrid binary particle swarm optimization (HBPSO) algorithm is proposed in this paper. It combines the PSOs concept and GA. In the existing standard binary PSO (DBPSO) two new operators such as crossover and mutation are incorporated to accelerate the convergence speed and to avoid possible stuck in local optimum thereby maintaining population diversity. The proposed HBPSO algorithm has been studied on 6 bench mark optimization problems. The experimental results such as minimum fitness, mean fitness, and variance of fitness over 50 consecutive trials on each objective function indicate that the HBPSO algorithm consistently outperforms the DBPSO and its variants in terms of convergence speed and search accuracy on a bulk of bench mark problems.

Optimal node placement in industrial Wireless Sensor Networks using adaptive mutation probability binary Particle Swarm Optimization algorithm

Industrial Wireless Sensor Networks (IWSNs), a novel technique in the field of industrial control, can greatly reduce the cost of measurement and control, as well as improve productive efficiency. Different from Wireless Sensor Networks (WSNs) in non-industrial areas, IWSNs has high requirements for reliability, especially for large-scale industry application. As the network architecture has great influences on the performance of IWSNs, this paper discusses the node placement problem in IWSNs. Considering the reliability requirements, the setup cost and energy balance in IWSNs, the node placement model of IWSNs is built and an adaptive mutation probability binary Particle Swarm Optimization algorithm (AMPBPSO) is proposed to solve this model. Experimental results show that AMPBPSO is effective for the optimal node placement in IWSNs with various kinds of field scales and different node densities and outperforms discrete binary Particle Swarm Optimization (DBPSO) and standard Genetic Algorithm (SGA) in terms of network reliability, load uniformity, total cost and convergence speed.

Determination of the PID controller parameters by Modified Binary Particle Swarm Optimization algorithm

The control of the bed temperature which is the key variable in Circulating Fluidized Bed Boiler (CFBB) system is a challenging problem due to its characteristics of the strong nonlinearity, large time delay and time-varying. It is very difficult to tune its PID controller based on the traditional PID tuning methods such as the classic Z-N method. Therefore, a Modified Binary Particle Swarm Optimization (MBPSO) algorithm is introduced to tune the parameters of bed temperature controller of CFBB in this paper, which is easy to implement and works with excellent optimization ability. To test and verify the effectiveness of the proposed method, the control performance on CFBBs bed temperature control is compared with Z-N method, and the tuning methods based on the standard PSO and discrete binary PSO. The simulation results show that PID tuning based MBPSO is valid and outperforms the ones based on the original discrete binary PSO algorithm and Z-N method, while the standard PSO fails to tackle this problem.

A modified multi-objective binary particle swarm optimization algorithm

In recent years a number of works have been done to extend Particle Swarm Optimization (PSO) to solve multi-objective optimization problems, but a few of them can be used to tackle binary-coded problems. In this paper, a novel modified multi-objective binary PSO (MMBPSO) algorithm is proposed for the better multi-objective optimization performance. A modified updating strategy is developed which is simpler and easier to implement compared with standard discrete binary PSO. The mutation operator and dissipation operator are introduced to improve the search ability and keep the diversity of algorithm. The experimental results on a set of multi-objective benchmark functions demonstrate that the proposed MBBPSO is a competitive multi-objective optimizer and outperforms the standard binary PSO algorithm in terms of convergence and diversity.

A Frequency Domain Approach to PID Controllers Design in Boiler-Turbine Units

This paper proposes a frequency domain approach—direct Nyquist array (DNA) method, to the design of PID controllers for multivariable boiler-turbine units based on gain and phase margins. The main objective is to propose an integrated method for the design and auto-tuning of simple yet robust PID controllers that can be more easily implemented for the boiler-turbine units in modern power plants. For this, the model of the original multi-input multi-output (MIMO) system is first transformed into a diagonal or diagonal dominance matrix after the system is appropriately compensated. Then, various PID controller design methods for single-input single-output (SISO) systems can be easily extended to decoupled or quasi-decoupled MIMO systems. In particular, the proposed method allows the user to specify the robustness and other key performances of the system through the gain and phase margin specifications. Simulation results illustrate the efficacy of the proposed method, showing that the designed controller for a boiler-turbine unit has a reduced number of elements by a half and produces much better dynamic performances than the one designed by Tan’s method.