Introduced Optimization Technique Research Articles

Hydrothermal–pumped-storage scheduling considering renewable energy uncertainty by improved cheetah optimization

The growth of renewable wind and solar energy in modern power systems affects the short-term scheduling of hydrothermal power generation. This article presents the optimum day-ahead scheduling of wind–solar–hydrothermal systems with pumped-storage plants (PSPs). To assess the contribution of PSPs in decreasing the generation costs of a renewable integrated power system, a scheduling model is formulated that considers different security constraints. An improved cheetah optimizer (ICO) is implemented on a renewable integrated test power system to solve the short-term optimum wind–solar–pumped-storage plant–hydrothermal scheduling (OWSPHTS) problem, taking into account all thermal, hydraulic and network constraints. Optimum solutions are obtained with different objectives considering renewable uncertainties. The proposed ICO solution method is compared with the similar algorithms in terms of optimal fuel costs, emissions, convergence success rate and computation time. The comparative solutions show that the introduced optimization technique is efficient for solving the real-world OWSPHTS problem.

Design and analysis of improved mountain gazelle optimization tuned PID and FOPID controllers for PV MPPT system

The solar photovoltaic (PV)-based Maximum Power Point Tracking (MPPT) systems have gained popularity in recent times. This work proposes the improvement and implementation of a newly introduced optimization technique, the Improved Mountain Gazelle Optimization (IMGO) algorithm, for tuning the Fractional Order Proportional-Integral-Derivative (FOPID) and Proportional-Integral-Derivative (PID) controllers for the MPPT control strategy. The performances of the controllers were evaluated with reference to error criteria and settling time of the response. The performance parameters mentioned above are compared with those of PID and FOPID controllers tuned using Genetic Algorithm (GA) and Grey-Wolf Optimization (GWO) algorithms. The simulation study was carried out in the MATLAB/SIMULINK environment. The analysis found that the FOPID controller tuned using the Improved Mountain Gazelle Optimization algorithm provides better results in terms of settling time and error when compared to the PID controller.

Back scattering response from single, finite and infinite array of nonlinear antennas based on intelligent water drops algorithm

PurposeThe purpose of this paper is to calculate the back scattering response from single, finite and infinite arrays of nonlinear antennas like the case where the antennas are exposed to high-value signals such as lightning strokes.Design/methodology/approachIn this paper, the authors have used a recently introduced optimization technique called intelligent water drop.FindingsThe results exhibit that the method used by the authors is faster and more accurate than other conventional optimization algorithms, i.e. particle swarm optimization and genetic algorithm.Originality/valueA new optimization algorithm is used to solve nonlinear problem accurately and sufficiently. Although the technique is not confined to the mentioned examples in the paper, it can be applied to other nonlinear circuits.

Optimum design of a hybrid PV–CSP–LPG microgrid with Particle Swarm Optimization technique

Designing an energy system using multiple energy sources including renewables and providing multiple energy services (e.g. electricity, heating) can enhance the reliability and efficiency of the system while mitigating the environmental footprint. However, interaction among various components, variation of the energy demand profile, and local ambient conditions make design optimization a complex task, and suggesting that efficient simulation tools and optimization techniques can help designers to determine the best solutions within a reasonable timeframe and budget.Previous work on a dynamic microgrid simulation tool called “u-Grid” used an exhaustive search technique to find optimum configurations. However, the high computational cost of the exhaustive search was a motivation to explore alternative optimization methods to improve the optimization process and also to enhance search speed. In this paper Particle Swarm Optimization (PSO) has been presented as a global optimizer and incorporated within the problem context. Results from the exhaustive search have been used as a benchmark for testing and validation of the newly introduced optimization technique. The result shows that the PSO method is an efficient technique which has the ability to determine a high quality design solution for an optimized microgrid with a relatively low computational cost. Applying this PSO-based algorithm to the case study has reduced the total computation time a factor of about 6 in a significantly smaller computational platform.

Fuzzy optimisation approach on the treatment of palm oil mill effluent (POME) via up-flow anaerobic sludge blanket–hollow centered packed bed (UASB–HCPB) reactor

An optimization study on the mesophilic anaerobic treatment of palm oil mill effluent (POME) in an up-flow anaerobic sludge blanket–hollow centered packed bed (UASB–HCPB) reactor was conducted for the first time by using kinetics modeling. Monod model was opted for the optimization due to its capability of describing the anaerobic digestion and the involvement of adequate amount of parameters in the kinetics model. The best-fit kinetic constants obtained from previous work were incorporated in the optimization model. Fuzzy optimization was adopted and it yielded the optimized chemical oxygen demand (COD) removal of 86.7% and volumetric methane production rate of 0.448 LCH4/L.day at hydraulic retention time (HRT) of 4.33 days, organic loading rate (OLR) of 14.75gCOD/L.day and mixed liquor volatile suspended solid (MLVSS) concentration of 19,130mg/L. A confirmative experiment was conducted and the results validate the legitimacy of the fuzzy optimization model. Further studies are required to improve the reliability of this newly introduced optimization technique in the wastewater treatment industry.

Migration Effects on BBO Evolution in Optimizing Fifteen Element Yagi-Uda Antenna Design

Biogeography Based Optimization (BBO) is a recently introduced optimization technique based on science of biogeography, i.e., study of distribution of biological species over space and time. In BBO, potential solutions of a problem are grouped in integer vectors known as habitats. Feature, i.e., Suitability Index Variable (SIV), sharing among various habitats is made to occur with migration operator where as exploration of new SIVs is done with mutation operator. Different migration variants are proposed to increase the diversity in the population, with objective of improved performance of BBO algorithm. Yagi-Uda antenna is a widely used antenna design due to various useful properties of high gain, low cost and ease of construction. Designing a Yagi-Uda antenna involves determination of element lengths and spacings between them to get desired radiation characteristics. In this paper, various migration variants of BBO algorithm, reported till date, are investigated to optimize the lengths and spacings for Yagi-Uda antenna elements for maximum gain. The results obtained with these migration variants are compared and the best results are presented in the ending sections of the paper.

Representation and optimization of software usage models with non-Markovian state transitions

We extend Markov usage models by admitting non-Markovian transitions between states. The suggested description syntax preserves the visualization capacities of Markov models and, although it is kept simple, provides the computational power of a universal programming language. It is shown that a previously introduced optimization technique for computing optimal test transition probabilities can be generalized to the presented framework. A medium-size example from a real-world application illustrates the approach.

Even faster lr parsing

AbstractConventional LR parser generators create tables which are used to drive a standard parser procedure. Much faster parsers can be obtained by compiling the table entries into code that is directly executed. A possible drawback with a directly executable parser is its large size. In this paper, we introduce optimization techniques that increase the parsing speed even further while simultaneously reducing the size of the parser.