M. V. C. Rao

A novel approach for unit commitment problem via an effective hybrid particle swarm optimization

This paper presents a new approach via hybrid particle swarm optimization (HPSO) scheme to solve the unit commitment (UC) problem. HPSO proposed in this paper is a blend of binary particle swarm optimization (BPSO) and real coded particle swarm optimization (RCPSO). The UC problem is handled by BPSO, while RCPSO solves the economic load dispatch problem. Both algorithms are run simultaneously, adjusting their solutions in search of a better solution. Problem formulation of the UC takes into consideration the minimum up and down time constraints, start-up cost, and spinning reserve and is defined as the minimization of the total objective function while satisfying all the associated constraints. Problem formulation, representation, and the simulation results for a ten generator-scheduling problem are presented. Results clearly show that HPSO is very competent in solving the UC problem in comparison to other existing methods.

On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systems

This paper deals with the concept of including the popular genetic algorithm operator, cross-over and root mean square (RMS) variants into particle swarm optimization (PSO) algorithm to make the convergence faster. Two different PSO algorithms are considered in this paper: the first one is the conventional PSO (cPSO) and the second is the global-local best values based PSO (GLbest-PSO). The GLbest-PSO includes global-local best inertia weight (GLbestIW) with global-local best acceleration coefficient (GLbestAC), whereas the cPSO has a time varying inertia weight (TVIW) and either time varying acceleration coefficient (TVAC) or fixed AC (FAC). The effectiveness of the cross-over operator with both PSO algorithms is tested through a constrained optimal control problem of a class of hybrid systems. The experimental results illustrate the advantage of PSO with cross-over operator, which sharpens the convergence and tunes to the best solution. In order to compare and verify the validity and effectiveness of the new approaches for PSO, several statistical analyses are carried out. The results clearly demonstrate that the GLbest-PSO with the cross-over operator is a very promising optimization technique. Similar conclusions can be made for the GLbest-PSO with RMS variants also.

New hybrid genetic operators for real coded genetic algorithm to compute optimal control of a class of hybrid systems

This paper introduces new hybrid cross-over methods and new hybrid selection methods for real coded genetic algorithm (RCGA), to solve the optimal control problem of a class of hybrid system, which is motivated by the structure of manufacturing environments that integrate process and optimal control. In this framework, the discrete entities have a state characterized by a temporal component whose evolution is described by event-driven dynamics and a physical component whose evolution is described by continuous time-driven systems. The proposed RCGA with hybrid genetic operators can outperform the conventional RCGA and the existing Forward Algorithms for this class of systems. The hybrid genetic operators improve both the quality of the solution and the actual optimum value of the objective function. A typical numerical example of the optimal control problem with the number of jobs varying from 5 to 25 is included to illustrate the efficacy of the proposed algorithm. Several statistical analyses are done to compare the betterment of the proposed algorithm over the conventional RCGA and Forward Algorithm. Hypothesis t-test and Analysis of Variance (ANOVA) test are also carried out to validate the effectiveness of the proposed algorithm.

A novel and effective particle swarm optimization like algorithm with extrapolation technique

A novel competitive approach to particle swarm optimization (PSO) algorithms is proposed in this paper. The proposed method uses extrapolation technique with PSO (ePSO) for solving optimization problems. By considering the basics of the PSO algorithm, the current particle position is updated by extrapolating the global best particle position and the current particle positions in the search space. The position equation is formulated with the global best (gbest) position, local best position (pbest) and the current position of the particle. The proposed method is tested with a set of 13 standard optimization benchmark problems and the results are compared with those obtained through two existing PSO algorithms, the canonical PSO (cPSO), the Global-Local best PSO (GLBest PSO). The cPSO includes a time-varying inertia weight (TVIW) and time-varying acceleration co-efficients (TVAC) while the GLBest PSO consists of Global-Local best inertia weight (GLBest IW) with Global-Local best acceleration co-efficient (GLBestAC). The simulation results clearly elucidate that the proposed method produces the near global optimal solution. It is also observed from the comparison of the proposed method with cPSO and GLBest PSO, the ePSO is capable of producing a quality of optimal solution with faster convergence rate. To strengthen the comparison and prove the efficacy of the proposed method a real time application of steel annealing processing (SAP) is also considered. The optimal control objectives of SAP are computed through the above said three PSO algorithms and also through two versions of genetic algorithms (GA), namely, real coded genetic algorithm (RCGA) and hybrid real coded genetic algorithm (HRCGA) and the results are analyzed with the proposed method. From the results obtained through benchmark problems and the real time application of SAP, it is clearly seen that the proposed ePSO method is competitive to the existing PSO algorithms and also to GAs.

Solving Unit Commitment Problem Using Hybrid Particle Swarm Optimization

This paper presents a Hybrid Particle Swarm Optimization (HPSO) to solve the Unit Commitment (UC) problem. Problem formulation of the unit commitment takes into consideration the minimum up and down time constraints, start up cost and spinning reserve, which is defined as the minimization of the total objective function while satisfying all the associated constraints. Problem formulation, representation and the simulation results for a 10 generator-scheduling problem are presented. Results shown are acceptable at this early stage.

A new and improved version of particle swarm optimization algorithm with global–local best parameters

This paper presents a new and improved version of particle swarm optimization algorithm (PSO) combining the global best and local best model, termed GLBest-PSO. The GLBest-PSO incorporates global–local best inertia weight (GLBest IW) with global–local best acceleration coefficient (GLBest Ac). The velocity equation of the GLBest-PSO is also simplified. The ability of the GLBest-PSO is tested with a set of bench mark problems and the results are compared with those obtained through conventional PSO (cPSO), which uses time varying inertia weight (TVIW) and acceleration coefficient (TVAC). Fine tuning variants such as mutation, cross-over and RMS variants are also included with both cPSO and GLBest-PSO to improve the performance. The simulation results clearly elucidate the advantage of the fine tuning variants, which sharpen the convergence and tune to the best solution for both cPSO and GLBest-PSO. To compare and verify the validity and effectiveness of the GLBest-PSO, a number of statistical analyses are carried out. It is also observed that the convergence speed of GLBest-PSO is considerably higher than cPSO. All the results clearly demonstrate the superiority of the GLBest-PSO.

On Abolishing Symmetry Requirements in the Formulation of a Five-Level Selective Harmonic Elimination Pulse-Width Modulation Technique

Selective harmonic elimination pulse width modulation (SHE-PWM) techniques offer a tight control of the harmonic spectrum of a given voltage waveform generated by a power electronic converter along with a low number of switching transitions. These optimal switching transitions can be calculated through Fourier theory, and for a number of years quarter-wave and half-wave symmetries have been assumed when formulating the problem. It was shown recently that symmetry requirements can be relaxed as a constraint. This changes the way the problem is formulated, and different solutions can be found without a compromise. This letter reports solutions to the switching transitions of a five-level SHE-PWM when both the quarter- and half-wave symmetry are abolished. Only the region of high-modulation indices is reported since the low-modulation indices region requires a unipolar waveform to be realized. Selected simulation and experimental results are reported to show the effectiveness of the proposed method

Accurate and reliable diagnosis and classification using probabilistic ensemble simplified fuzzy ARTMAP

In this paper, an accurate and effective probabilistic plurality voting method to combine outputs from multiple simplified fuzzy ARTMAP (SFAM) classifiers is presented. Five ELENA benchmark problems and five medical benchmark data sets have been used to evaluate the applicability and performance of the proposed probabilistic ensemble simplified fuzzy ARTMAP (PESFAM) network. Among the five benchmark problems in ELENA project, PESFAM outperforms the SFAM and multi-layer perceptron (MLP) classifier. In addition, the effectiveness of the proposed PESFAM is delineated in medical diagnosis applications. For the medical diagnosis and classification problems, PESFAM achieves 100 percent in accuracy, specificity, and sensitivity based on the 10-fold crossvalidation and these results are superior to those from other classification algorithms. In addition, a posteri probability of the predicted class can be used to measure the prediction reliability of PESFAM. The experiments demonstrate the potential of the proposed multiple SFAM classifiers in offering an optimal solution to the data-ordering problem of SFAM implementation and also as an intelligent medical diagnosis tool.

On the performance of the particle swarm optimization algorithm with various inertia weight variants for computing optimal control of a class of hybrid systems

This paper presents an alternative and efficient method for solving the optimal control of single-stage hybrid manufacturing systems which are composed with two different categories: continuous dynamics and discrete dynamics. Three different inertia weights, a constant inertia weight (CIW), time-varying inertia weight (TVIW), and global-local best inertia weight (GLbestIW), are considered with the particle swarm optimization (PSO) algorithm to analyze the impact of inertia weight on the performance of PSO algorithm. The PSO algorithm is simulated individually with the three inertia weights separately to compute the optimal control of the single-stage hybrid manufacturing system, and it is observed that the PSO with the proposed inertia weight yields better result in terms of both optimal solution and faster convergence. Added to this, the optimal control problem is also solved through real coded genetic algorithm (RCGA) and the results are compared with the PSO algorithms. A typical numerical example is also included in this paper to illustrate the efficacy and betterment of the proposed algorithm. Several statistical analyses are carried out from which can be concluded that the proposed method is superior to all the other methods considered in this paper.

Application of Active Force Control and Iterative Learning in a 5-Link Biped Robot

This paper investigates the efficacy of the implementation of the conventional Proportional-Derivative (PD) controller and different Active Force Control (AFC) strategies to a 5-link biped robot through a series of simulation studies. The performance of the biped system is evaluated by making the biped walk on a horizontal flat surface, in which the locomotion is constrained within the sagittal plane. Initially, a classical PD controller has been used to control the biped robot. Then, a disturbance elimination method called Active Force Control (AFC) schemes has been incorporated. The effectiveness and robustness of the AFC as “disturbance rejecter” has been examined when a conventional crude approximation (AFCCA), and an intelligent active force control scheme, which is known as Active Force Control and Iterative Learning (AFCAIL) are employed. It is found that for both of the AFC control schemes proposed, the system is robust and stable even under the influence of disturbances. An attractive feature of the AFCAIL scheme is that inertia matrix tuning becomes much easier and automatic without any degradation in the performance.