Wind Driven Optimization Research Articles

A Water Cycle Algorithm-Based Multilevel Thresholding System for Color Image Segmentation Using Masi Entropy

In this paper, a recently developed metaheuristic water cycle algorithm (WCA) is coupled with Masi entropy (Masi-WCA) to perform color image segmentation over the optimal threshold value selection process. Masi entropy gives the non-extensive/additive information that exists in an image by a tunable entropic parameter. The water cycle algorithm is a newly established population-based method which has been employed to exploit an optimal value of weighing factors for enforcement of constraints on individual components. The idea behind WCA is grounded on thought of water cycle and how streams and rivers flow downward toward the sea in the real world. The key feature of this paper is to exploit the modern optimization techniques such as water cycle algorithm, monarch butterfly optimization, grasshopper optimization algorithm, bat algorithm, particle swarm optimization, and wind-driven optimization for the color image segmentation purpose. In this paper, two objective (fitness) functions are exploited which are Tsallis and Masi entropy for a fair comparison of the proposed method. The proposed scheme is examined intensively regarding quality, and a statistical graph is included to compare the outcomes of the proposed Masi-WCA method against similar algorithms. Different to other recently developed optimization algorithms used for color image multilevel thresholding operations, WCA presents a better performance in terms of superior quality and fast convergence rate. Experimental evidence encourages the use of WCA for multilevel thresholding with Masi entropy, while it concludes that Tsallis entropy does not outperform over the proposed scheme.

Hierarchical Scheduling Scheme for AC/DC Hybrid Active Distribution Network Based on Multi-Stakeholders

This paper presents a hierarchical multi-stage scheduling scheme for the AC/DC hybrid active distribution network (ADN). The load regulation center (LRC) is considered in the developed scheduling strategy, as well as the AC and DC sub-network operators. They are taken to be different stakeholders. To coordinate the interests of all stakeholders, a two-level optimization model is established. The flexible loads are dispatched by LRC in the upper-level optimization model, the objective of which is minimizing the loss of the entire distribution network. The lower-level optimization is divided into two sub-optimal models, and they are carried out to minimize the operating costs of the AC/DC sub-network operators respectively. This two-level model avoids the difficulty of solving multi-objective optimization and can clarify the role of various stakeholders in the system scheduling. To solve the model effectively, a discrete wind-driven optimization (DWDO) algorithm is proposed. Then, considering the combination of the proposed DWDO algorithm and the YALMIP toolbox, a hierarchical optimization algorithm (HOA) is developed. The HOA can obtain the overall optimization result of the system through the iterative optimization of the upper and lower levels. Finally, the simulation results verify the effectiveness of the proposed scheduling scheme.

A novel beta differential evolution algorithm-based fast multilevel thresholding for color image segmentation

Multilevel thresholding for image segmentation is a crucial process in several applications such as feature extraction and pattern recognition. The meticulous search for the best values for the optimization of fitness function using classical operations needs profuse computational time, which also results in inaccuracy and instability. In this paper, a new beta differential evolution (BDE)-based fast color image multilevel thresholding scheme using two objective functions has been presented. The optimal threshold values are determined by maximizing Kapur’s and Tsallis entropy (entropy criterion) thresholding functions coupled with BDE algorithm. The efficiency of the proposed method is examined over existing multilevel thresholding methods such as artificial bee colony, particle swarm optimization, wind-driven optimization and differential evolution. These approaches are aimed to determine optimum threshold values at different levels of thresholding for color image segmentation. The proficiency of the presented methodology is demonstrated visually and computationally on five real-life true color images as well as four satellite images. Experimental outcomes are exhibited in terms of the optimal threshold value, best objective function and computational cost (in seconds) for each method at different thresholding levels. Afterward, the proposed scheme is examined intensively regarding the superiority of quality. The experimentally evaluated results show that the proposed BDE-based approach for multilevel color image segmentation can accurately and efficiently examine for multiple thresholds, which are near to optimal ones searched using an exhaustive search process.

A six-band ultra-thin polarization-insensitive pixelated metamaterial absorber using a novel binary wind driven optimization algorithm

ABSTRACTIn this article, a novel binary Wind Driven Optimization (WDO) algorithm is presented for the synthesis of six-band Metamaterial Absorber (MA). Synthesis of the unit cell structure of MA is done by optimizing the presence of each unit pixel of pixelated Frequency Selective Surface (FSS). Numerical simulation of this optimized absorber shows six discrete bands of absorptions at 7.6, 10.1, 13.3, 14.7, 15.8 and 16.7 GHz with more than 90% absorptivity under normal incidence of the wave. The proposed absorber is ultra-thin, polarization insensitive and has good performance over a wide angle of incidence. The numerical simulation and experimental results are in close agreement.

Wind Driven Optimization Algorithm Application to Load Frequency Control in Interconnected Power Systems Considering GRC and GDB Nonlinearities

Due to the great importance of the performance of load frequency controllers in power systems, a lot of effort have been performed to improve the performance of these controllers by fine tuning them. Evolutionary algorithms are the most popular techniques used to optimally tune the controllers. In this article, wind driven optimization (WDO) algorithm which is a newly developed evolutionary algorithm is used for tuning load frequency controllers. Based on simulation studies, the impact of different objective functions on the performance of the evolutionary algorithms in tuning the controllers is investigated. Also, using simulation studies carried out in a three areas interconnected power system, the effectiveness of the proposed optimization algorithm in comparison to other newly proposed evolutionary algorithms is demonstrated. Furthermore, the robustness of the proposed method in case of power system parameters and configuration variations is confirmed.

Synthesis of unequally spaced linear arrays using modified differential evolution algorithm

Unequally spaced linear arrays are synthesised for the sidelobe level (SLL) suppression, the null control in some specified directions and the beamwidth (BW) set within predefined limits. Based on the vector mapping method and the position perturbation technique, the constrained vector projection (CVP) is defined to offer flexibility in controlling null depths with multiple constraints. A modified differential evolution (DE) algorithm with the CVP method (in short MDE-CVP) is then performed in the array synthesis, which effectively reduces the optimisation space, avoids the infeasible solutions and enhances the global search ability. Three examples are presented and the first two results are compared with those obtained by learning particle swarm optimisation, invasive weed optimisation and wind-driven optimisation and a modified version of the central force optimisation algorithms. Comparison results indicate that the MDE-CVP algorithm outperforms the existing algorithms in terms of minimum SLL, BW control, null control and convergence characteristics.

A Domestic Microgrid with Optimized Home Energy Management System

Microgrid is a community-based power generation and distribution system that interconnects smart homes with renewable energy sources (RESs). Microgrid efficiently and economically generates power for electricity consumers and operates in both islanded and grid-connected modes. In this study, we proposed optimization schemes for reducing electricity cost and minimizing peak to average ratio (PAR) with maximum user comfort (UC) in a smart home. We considered a grid-connected microgrid for electricity generation which consists of wind turbine and photovoltaic (PV) panel. First, the problem was mathematically formulated through multiple knapsack problem (MKP) then solved by existing heuristic techniques: grey wolf optimization (GWO), binary particle swarm optimization (BPSO), genetic algorithm (GA) and wind-driven optimization (WDO). Furthermore, we also proposed three hybrid schemes for electric cost and PAR reduction: (1) hybrid of GA and WDO named WDGA; (2) hybrid of WDO and GWO named WDGWO; and (3) WBPSO, which is the hybrid of BPSO and WDO. In addition, a battery bank system (BBS) was also integrated to make our proposed schemes more cost-efficient and reliable, and to ensure stable grid operation. Finally, simulations were performed to verify our proposed schemes. Results show that our proposed scheme efficiently minimizes the electricity cost and PAR. Moreover, our proposed techniques, WDGA, WDGWO and WBPSO, outperform the existing heuristic techniques.

A WDO Framework for Optimal Deployment of DGs and DSCs in a Radial Distribution System Under Daily Load Pattern to Improve Techno-Economic Benefits

This article presents a methodology to determine optimal locations and sizes of DGs (Distributed Generator) and DSCs (D-STATCOM) simultaneously in a radial distribution network during a daily load pattern to improve the techno-economic benefits. An effective weighted objective function has been designed to address daily power loss minimization of the three techno-economic benefits, improvement of daily voltage profile and maximization of net annual savings due to the placement of DGs and DSCs. A repetitive backward-forward sweep based load flow has been used to calculate the daily power loss and bus voltages. To optimize the designed objective function, an efficient and simple nature-inspired wind driven optimization (WDO) algorithm has been used. To validate the effectiveness of the proposed methodology, different scenarios are considered and a detailed outcome analysis is presented.

Design of spiral heat exchanger from economic and thermal point of view using a tuned wind-driven optimizer

This paper presents an optimization of spiral heat exchangers by wind-driven optimization and a novel variant of this algorithm by the insertion of a statistic distribution to self-adapting of the evolution parameters of the algorithm. Spiral heat exchangers are the ideal type for cooling slurries and fluids that presents high viscosity and is very common in food and petrochemical industries. The variables used for the optimization were the spacing channels for hot and cold streams, the length, the width and the thickness of the heat exchanger. Two case studies were presented where the minimization of the cost and the maximization of the overall heat transfer coefficient were implemented. Reductions about 4.46 and 23% were obtained for the cost and increases about 18.8 and 16.4% were reached for the overall heat transfer coefficient for each case study in comparison to previous works. The proposed technique reached better results in all the simulations, in some cases with GA, but performed better accuracy among all the simulations.

Optimal Residential Load Scheduling Under Utility and Rooftop Photovoltaic Units

With the rapid advancement in technology, electrical energy consumption is increasing rapidly. Especially, in the residential sector, more than 80% of electrical energy is being consumed because of consumer negligence. This brings the challenging task of maintaining the balance between the demand and supply of electric power. In this paper, we focus on the problem of load balancing via load scheduling under utility and rooftop photovoltaic (PV) units to reduce electricity cost and peak to average ratio (PAR) in demand-side management. For this purpose, we adopted genetic algorithm (GA), binary particle swarm optimization (BPSO), wind-driven optimization (WDO), and our proposed genetic WDO (GWDO) algorithm, which is a hybrid of GA and WDO, to schedule the household load. For energy cost estimation, combined real-time pricing (RTP) and inclined block rate (IBR) were used. The proposed algorithm shifts load from peak consumption hours to off-peak hours based on combined pricing scheme and generation from rooftop PV units. Simulation results validate our proposed GWDO algorithm in terms of electricity cost and PAR reduction while considering all three scenarios which we have considered in this work: (1) load scheduling without renewable energy sources (RESs) and energy storage system (ESS), (2) load scheduling with RESs, and (3) load scheduling with RESs and ESS. Furthermore, our proposed scheme reduced electricity cost and PAR by 22.5% and 29.1% in scenario 1, 47.7% and 30% in scenario 2, and 49.2% and 35.4% in scenario 3, respectively, as compared to unscheduled electricity consumption.

An Efficient Demand Side Management System with a New Optimized Home Energy Management Controller in Smart Grid

The traditional power grid is inadequate to overcome modern day challenges. As the modern era demands the traditional power grid to be more reliable, resilient, and cost-effective, the concept of smart grid evolves and various methods have been developed to overcome these demands which make the smart grid superior over the traditional power grid. One of the essential components of the smart grid, home energy management system (HEMS) enhances the energy efficiency of electricity infrastructure in a residential area. In this aspect, we propose an efficient home energy management controller (EHEMC) based on genetic harmony search algorithm (GHSA) to reduce electricity expense, peak to average ratio (PAR), and maximize user comfort. We consider EHEMC for a single home and multiple homes with real-time electricity pricing (RTEP) and critical peak pricing (CPP) tariffs. In particular, for multiple homes, we classify modes of operation for the appliances according to their energy consumption with varying operation time slots. The constrained optimization problem is solved using heuristic algorithms: wind-driven optimization (WDO), harmony search algorithm (HSA), genetic algorithm (GA), and proposed algorithm GHSA. The proposed algorithm GHSA shows higher search efficiency and dynamic capability to attain optimal solutions as compared to existing algorithms. Simulation results also show that the proposed algorithm GHSA outperforms the existing algorithms in terms of reduction in electricity cost, PAR, and maximize user comfort.

Wind-Driven Optimization Technique for Estimation of Solar Photovoltaic Parameters

In order to increase the efficiency of the solar photovoltaic (PV) system, accurate electrical modeling of the system under different environmental conditions is necessary. The double-diode electrical model of solar PV is known to be more accurate than its single diode model counterpart since it takes into account the effect of recombination. However, because of its nonlinear characteristics, the parameters of the double-diode model (DDM) have to be identified using optimization algorithms. In this paper, the wind-driven optimization (WDO) algorithm is proposed as a potential new method for identifying the parameters of a 12-parameter DDM of the solar PV. The accuracy and flexibility of the proposed method are verified using three different sets of data: first, experimental data at the controlled environmental condition; second, data sheet values of different solar PV modules; and third, real-time experimental data at the uncontrolled environmental condition. Additionally, the performance of the WDO is compared with other well-known existing optimization techniques. The obtained results show that the WDO algorithm can provide optimized values with reduced mean absolute error in power and reduced root mean square error for different types of solar PV modules at different environmental conditions. We show that the WDO can be confidently recommended as a reliable optimization algorithm for parameter estimation of solar PV model.

Efficient Job Scheduling in Computational Grid Systems Using Wind Driven Optimization Technique

Computational Grid has been employed for solving complex and large computation-intensive problems with the help of geographically distributed, heterogeneous and dynamic resources. Job scheduling is a vital and challenging function of a computational Grid system. Job scheduler has to deal with many heterogeneous computational resources and to take decisions concerning the dynamic, efficient and effective execution of jobs. Optimization of the Grid performance is directly related with the efficiency of scheduling algorithm. To evaluate the efficiency of a scheduling algorithm, different parameters can be used, the most important of which are makespan and flowtime. In this paper, a very recent evolutionary heuristic algorithm known as Wind Driven Optimization (WDO) is used for efficiently allocating jobs to resources in a computational Grid system so that makespan and flowtime are minimized. In order to measure the efficacy of WDO, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are considered for comparison. This study proves that WDO produces best results.

Energy Efficient Integration of Renewable Energy Sources in the Smart Grid for Demand Side Management

With the emergence of smart grid (SG), the consumers have the opportunity to integrate renewable energy sources (RESs) and take part in demand side management. In this paper, we introduce generic home energy management control system (HEMCS) to efficiently schedule the household load and integrate RESs. The HEMCS is based on the genetic algorithm, binary particle swarm optimization, wind-driven optimization (WDO), and our proposed genetic WDO algorithm to schedule appliances of single and multiple homes. For energy cost calculation, real-time pricing (RTP) and inclined block rate schemes are combined, because in case of only RTP, there is a possibility of building peaks during off-peak hours that may damage the entire power system. Moreover, to control the demand under the grid station capacity, the feasible region is defined and a problem is formulated using multiple knapsack. Energy efficient integration of RESs in SG is a challenging task due to time varying and their intermittent nature. The simulation results show that the proposed scheme avoids voltage rise problem in areas with high penetration of renewable energy. Moreover, the proposed scheme also reduces the electricity cost up to 48% and peak to average ratio of aggregated load up to 37.69%.

Thinned Array Design With Minimum Number of Transducers for Multibeam Imaging Sonar

A large number of transducers employed in the multibeam imaging sonar array significantly increases the hardware complexity, costs, and computational load. Although a great amount of work has been done to minimize the number of transducers in the array, one can verify that these methods do not provide optimal results when applied to the case of multiple beams. In this paper, a novel thinning method is proposed for multiple-beam conditions where the number of transducers is minimized by an iterative process. Optimal common transducers’ positions are selected by a wind-driven optimization (WDO) algorithm, and transducers’ weights are optimized by convex optimization in order to suppress the peak sidelobe level (PSLL). The performance of the proposed method is evaluated via simulations and experiments. Results show that the method is applicable to the minimization of the number of transducers under hundreds of beam conditions, and consequently reduces the complexity of the imaging sonar system with imaging performance still acceptable.

Segmentation of intima media complex from carotid ultrasound images using wind driven optimization technique

Abstract Cardiovascular diseases are the third leading cause of death worldwide. The primitive indication of the possible onset of a cardiovascular disease is atherosclerosis, which is the accumulation of plaque on the arterial wall. The intima-media thickness (IMT) of the common carotid artery is an early marker of the development of cardiovascular disease. The computation of the IMT and the delineation of the carotid plaque are significant predictors for the clinical diagnosis of the risk of stroke. For a robust diagnosis, carotid ultrasound images must be free from speckle noise. To address this problem, we use state-of-the-art despeckling and enhancement methods in this work. Many edge-based methods for IMT estimation have been proposed to overcome the limitations of manual segmentation. In this paper, we present a fully automated region-of-interest (ROI) extraction and a threshold-based segmentation of the intima media complex (IMC) using a wind driven optimization (WDO) technique. A quantitative evaluation is carried out on 90 carotid ultrasound images of two different datasets. The obtained results are compared with those of state-of-the-art techniques such as a model-based approach, a dynamic programming method, and a snake segmentation method. The experimental analysis shows that the proposed method is robust in measuring the IMT in carotid ultrasound images.

An improved optimization technique for estimation of solar photovoltaic parameters

Abstract The nonlinear current vs voltage (I-V) characteristics of solar PV make its modelling difficult. Optimization techniques are the best tool for identifying the parameters of nonlinear models. Even though, there are different optimization techniques used for parameter estimation of solar PV, still the best optimized results are not achieved to date. In this paper, Wind Driven Optimization (WDO) technique is proposed as the new method for identifying the parameters of solar PV. The accuracy and convergence time of the proposed method is compared with results of Pattern Search (PS), Genetic Algorithm (GA), and Simulated Annealing (SA) for single diode and double diode models of solar PV. Furthermore, for performance validation, the parameters obtained through WDO are compared with hybrid Bee Pollinator Flower Pollination Algorithm (BPFPA), Flower Pollination Algorithm (FPA), Generalized Oppositional Teaching Learning Based Optimization (GOTLBO), Artificial Bee Swarm Optimization (ABSO), and Harmony Search (HS). The obtained results clearly reveal that WDO algorithm can provide accurate optimized values with less number of iterations at different environmental conditions. Therefore, the WDO can be recommended as the best optimization algorithm for parameter estimation of solar PV.

A new heuristically optimized Home Energy Management controller for smart grid

Recently, Home Energy Management (HEM) controllers have been widely used for residential load management in a smart grid. Generally, residential load management aims to reduce the electricity bills and also curtail the Peak-to-Average Ratio (PAR). In this paper, we design a HEM controller on the basis of four heuristic algorithms: Bacterial Foraging Optimization Algorithm (BFOA), Genetic Algorithm (GA), Binary Particle Swarm Optimization (BPSO), and Wind Driven Optimization (WDO). Moreover, we proposed a hybrid algorithm which is Genetic BPSO (GBPSO). All the selected algorithms are tested with the consideration of essential home appliances in Real Time Pricing (RTP) environment. Simulation results show that each algorithm in the HEM controller reduces the electricity cost and curtails the PAR. GA based HEM controller performs relatively better in term of PAR reduction; it curtails approximately 34% PAR. Similarly, BPSO based HEM controller performs relatively better in term of cost reduction, as it reduces approximately 36% cost. Moreover, GBPSO based HEM controller performs better than the other algorithms based HEM controllers in terms of both cost reduction and PAR curtailment.

A Wind Driven Optimization-Based Methodology for Robust Optimizations of Electromagnetic Devices under Interval Uncertainty

A methodology based on a new uncertainty quantization formulation and an improved wind driven optimization (WDO) algorithm is proposed for robust optimizations of electromagnetic devices under interval uncertainties. In the proposed methodology, the robust performances are enforced as the constraint functions, and the objective function is selected as the biasing force to evolve the iteration procedures. The WDO method is also improved to guarantee a good balance between the exploration and exploitation searches. Numerical results on a case study are reported to showcase the feasibility and merit of the proposed methodology in solving practical engineering design problems.

An Optimized Home Energy Management System with Integrated Renewable Energy and Storage Resources

Traditional power grid and its demand-side management (DSM) techniques are centralized and mainly focus on industrial consumers. The ignorance of residential and commercial sectors in DSM activities degrades the overall performance of a conventional grid. Therefore, the concept of DSM and demand response (DR) via residential sector makes the smart grid (SG) superior over the traditional grid. In this context, this paper proposes an optimized home energy management system (OHEMS) that not only facilitates the integration of renewable energy source (RES) and energy storage system (ESS) but also incorporates the residential sector into DSM activities. The proposed OHEMS minimizes the electricity bill by scheduling the household appliances and ESS in response to the dynamic pricing of electricity market. First, the constrained optimization problem is mathematically formulated by using multiple knapsack problems, and then solved by using the heuristic algorithms; genetic algorithm (GA), binary particle swarm optimization (BPSO), wind driven optimization (WDO), bacterial foraging optimization (BFO) and hybrid GA-PSO (HGPO) algorithms. The performance of the proposed scheme and heuristic algorithms is evaluated via MATLAB simulations. Results illustrate that the integration of RES and ESS reduces the electricity bill and peak-to-average ratio (PAR) by 19.94% and 21.55% respectively. Moreover, the HGPO algorithm based home energy management system outperforms the other heuristic algorithms, and further reduces the bill by 25.12% and PAR by 24.88%.