Meta-heuristic Optimization Technique Research Articles

Development of Hybrid Models Using Metaheuristic Optimization Techniques to Predict the Carbonation Depth of Fly Ash Concrete

Development of Hybrid Models Using Metaheuristic Optimization Techniques to Predict the Carbonation Depth of Fly Ash Concrete

An Automated Framework for Variability Analysis for Integrated Circuits Using Metaheuristics

This work aims to analyze the variability of integrated circuits and systems. An automated framework is presented for variability analysis that exploits the metaheuristic optimization techniques. The efficacy of the proposed approach is demonstrated by two case studies- one is the estimation of variability in phase noise in RF CMOS LC tank oscillators (frequency domain analysis) and other being the estimation of variability in the differential output signal of a current mode driver (time-domain analysis). The proposed approach is investigated and validated by comparing the results from the traditional Monte-Carlo simulations and the Ordinary-Least Squares based Polynomial Chaos Expansion. A significant gain in the computational time is reported while maintaining accuracy in the results. The proposed methodology is not just limited to variability analysis applications but also can be used to solve the circuit optimization problems.

HHO-Based Vector Quantization Technique for Biomedical Image Compression in Cloud Computing

In the present digital era, the exploitation of medical technologies and massive generation of medical data using different imaging modalities, adequate storage, management, and transmission of biomedical images necessitate image compression techniques. Vector quantization (VQ) is an effective image compression approach, and the widely employed VQ technique is Linde–Buzo–Gray (LBG), which generates local optimum codebooks for image compression. The codebook construction is treated as an optimization issue solved with utilization of metaheuristic optimization techniques. In this view, this paper designs an effective biomedical image compression technique in the cloud computing (CC) environment using Harris Hawks Optimization (HHO)-based LBG techniques. The HHO-LBG algorithm achieves a smooth transition among exploration as well as exploitation. To investigate the better performance of the HHO-LBG technique, an extensive set of simulations was carried out on benchmark biomedical images. The proposed HHO-LBG technique has accomplished promising results in terms of compression performance and reconstructed image quality.

Applied Metaheuristic Optimization in Asphalt Pavement Management


 
 
 
 Pavement engineering is a crossroads between geotechnical and transportation engineering with a sound base on construction materials. There are multiple applications of optimization algorithms in pavement engineering, emphasizing pavement management for its socioeconomic implications and back-calculation of layer properties for its complexity. A detailed literature review shows that optimization has been a permanent concern in pavement engineering. However, only in the last two decades, the increase in computational power allowed the implementation of metaheuristic optimization techniques with promising results in research and practice. Pavement management requires powerful optimization tools for multi-objective problems such as minimizing costs and maximizing the pavement state from network to project level with constrained budgets. A substantial amount of research focuses on genetic algorithms (GA), but new developments include particle intelligence (PSO, ACO, and ABC). The study must go beyond small-sized networks to improve the management of existing road infrastructure (pavement, bridges) based on mechanistic and reliability criteria.
 
 
 

Assessment of jeans sewing thread consumption by applying metaheuristic optimization methods

Assessment of jeans sewing thread consumption by applying metaheuristic optimization methods

A solution to economic dispatch problem using hybrid particle chemical reaction optimization (HP-CRO) algorithm

The electrical power demand is on heave due to constant increase in demand from manufacturing firms and ménage. Therefore optimized and cost operative power generation is the requirement of the day. In power system operation and planning the Economic Dispatch (ED) problem is prevailing and noteworthy one. The ED problem of several standards is disentangled with so many classical and meta-heuristics optimization techniques. In the triumphed research work, a unique and innovative optimization procedure, Chemical Reaction Optimization (CRO) hybridized with Particle Swarm Optimization (PSO) algorithm is recommended for the solution of ED problem. Generating unit power limits, load fulfillment and prohibited operating zones are considered as constrained for different IEEE standard ED problems. The non-convex economic dispatch problem is coded at Hybrid Particle Chemical Reaction Optimization (HP-CRO) charter and employed in MATLAB for 50 trials, 50 search agents and 1000 iterations. The offered technique is applied on various IEEE standard test systems including 6, 15 and 40 generating units. The exhibited results are paralleled with stated practices mentioned in the literature for above test systems. The results obtained from proposed techniques give an edge to those obtained from other techniques from cost point of view.

Cascade 2DOF-PIDN-FOPIDN Controller based AGC System of a Multi-Source Restructured Power System with HES and IPFC

Background/Objectives: The essential goal of the Automatic Generation Control (AGC) framework is to limit the transient deviations in the area frequencies and tie-line power oscillations and to guarantee their steady-state errors to be zeros. Methods: The article proposes a new cascade Two Degree of Freedom PID including filter along with Fractional Order PID together with filter (2DOF-PIDN-FOPIDN) regulator which are designed using Buzzard Optimization Algorithm (BUZOA) technique and executed in the secondary controller of the AGC loop of a two-region thermal-hydro-wind restructured power system. The proposed novel 2DOF-PIDN-FOPIDN regulator consists of an integer order cascaded controller through a fractional order controller. It guarantees with the aim of the controller intend process be easy to execute and has an outstanding ability to manage parameters improbability, removes steady-state errors, and guarantees enhanced stability as compared with the 2 DOF-FOPIDN regulators. The controller gains and other control constraints were tuned with novel and efficient meta-heuristic optimization techniques such as the BUZOA technique. Further, the AGC loop of the proposed system is enhanced with the application of sophisticated Hydrogen Energy Storage (HES) units connected in the control area and FACTS devises like Interline Power Flow Controller (IPFC) which is also introduced in series with tie-line of the interconnected areas of the restructured power system under different possible exchanges. Findings and Novelty: The reproduction results uncover that the predominance of a new cascade 2DOF-PIDN-FOPIDN regulator, the unique presentation of AGC system of a two-region interconnected restructured power framework has worked on as far as less peak variation and settling time of region frequencies and tie-line power in different transactions as contrasted 2 DOF-FOPIDN regulators. Moreover, the execution of HES and IPFC units effectively arrest the fundamental decrease in frequency like the tie-line power deviation after an abrupt load disturbance to give the critical advantage of stability. Keywords: Buzzard Optimization Algorithm, 2-DOF-FOPIDN controller, Cascade 2DOF-PIDN-FOPIDN controller, HES, IPFC

Application of the Sine-Cosine Algorithm to the Optimal Design of a Closed Coil Helical Spring

This paper proposes the application of the sinecosine algorithm (SCA) to the optimal design of a closed coil helical spring. The optimization problem addressed corresponds to the minimization of total spring volume subject to physical constraints that represents the closed coil helical spring such as maximum working load, shear stress, and minimum diameter requirements, among other. The resulting mathematical formulation is a complex nonlinear and non-convex optimization model that is typically addressed in literature with trial and error methods or heuristic algorithms. To solve this problem efficiently, the SCA is proposed in this research. This optimization algorithm belongs to the family of the metaheuristic optimization techniques, it works with controlled random processes guided by sine and cosine trigonometric functions, that allows exploring and exploiting the solution space in order to find the best solution to the optimization problem. By presenting as main advantage an easy implementation at any programming language using sequential quadratic programming; eliminating the need to uses specialized and costly software. Numerical results demonstrating that the proposes SCA allows reaching lower spring volume values in comparison with literature approaches, such as genetic algorithms, particle swarm optimization methods, among others. All the numerical simulations have been implemented in the MATLAB software.

Energy Efficient Optimal Resource Allocation in Multi-RAT Heterogeneous Network

ABSTRACT In this paper, we address the frequent problem associated with user association and resource allocation along with optimal deployment of base station (BS) in multiple radio access technology (Multi-RAT)-assisted heterogeneous network (Het-Net). Considering real time user scenarios, optimal resource allocation in Het-Net while ensuring each user’s minimum required data rate is a challenging task to be performed. Here, we propose a novel algorithm with a well-known and efficient meta-heuristic optimization technique to resolve the aforementioned problem. We use hybrid memory-based dragonfly algorithm with differential evolution (DADE) for its excellent convergence characteristics. Extensive simulations are performed to determine the optimal network utility under the consideration of nonuniform user distribution and fine-tuning their respective service class and contract of association parameters. Simulation results depict that the proposed algorithm improves the overall network utility in terms of radio resource utilization and energy consumption while satisfying the user demands. Comparative analysis of the proposed technique with the other state-of-the-art algorithm depicts the superiority of the proposed algorithm in terms of accuracy and consistency. We also perform optimal multi-RAT cell planning under the above constraints including a network blackout scenario. The algorithm ensures each user coverage by optimally allocating the available resources.

Re-Allocation of Distributed Generations Using Available Renewable Potential Based Multi-Criterion-Multi-Objective Hybrid Technique

Integration of Distributed generations (DGs) and capacitor banks (CBs) in distribution systems (DS) have the potential to enhance the system’s overall capabilities. This work demonstrates the application of a hybrid optimization technique the applies an available renewable energy potential (AREP)-based, hybrid-enhanced grey wolf optimizer–particle swarm optimization (AREP-EGWO-PSO) algorithm for the optimum location and sizing of DGs and CBs. EGWO is a metaheuristic optimization technique stimulated by grey wolves, and PSO is a swarm-based metaheuristic optimization algorithm. Hybridization of both algorithms finds the optimal solution to a problem through the movement of the particles. Using this hybrid method, multi-criterion solutions are obtained, such as technical, economic, and environmental, and these are enriched using multi-objective functions (MOF), namely minimizing active power losses, voltage deviation, the total cost of electrical energy, total emissions from generation sources and enhancing the voltage stability index (VSI). Five different operational cases were adapted to validate the efficacy of the proposed scheme and were performed on two standard distribution systems, namely, IEEE 33- and 69-bus radial distribution systems (RDSs). Notably, the proposed AREP-EGWO-PSO algorithm compared the AREP at the candidate locations and re-allocated the DGs with optimal re-sizing when the EGWO-PSO algorithm failed to meet the AREP constraints. Further, the simulated results were compared with existing optimization algorithms considered in recent studies. The obtained results and analysis show that the proposed AREP-EGWO-PSO re-allocates the DGs effectively and optimally, and that these objective functions offer better results, almost similar to EGWO-PSO results, but more significant than other existing optimization techniques.

Optimal Placement of Distributed Photovoltaic Systems and Electric Vehicle Charging Stations Using Metaheuristic Optimization Techniques

In this study, the concept of symmetry is introduced by finding the optimal state of a power system. An electric vehicle type load is present, where the supply stores’ electrical energy causes an imbalance in the system. The optimal conditions are related by adjusting the voltage of the bus location. The key variables are the load voltage deviation (LVD), the variation of the load and the power, and the sizing of the distributed photovoltaic (DPV), which are added to the system for power stability. Here, a method to optimize the fast-charging stations (FCSs) and DPV is presented using an optimization technique comparison. The system tests the distribution line according to the bus grouping in the IEEE 33 bus system. This research presents a hypothesis to solve the problem of the voltage level in the system using metaheuristic algorithms: the cuckoo search algorithm (CSA), genetic algorithm (GA), and simulated annealing algorithm (SAA) are used to determine the optimal position for DPV deployment in the grid with the FCSs. The LVD, computation time, and total power loss for each iteration are compared. The voltage dependence power flow is applied using the backward/forward sweep method (BFS). The LVD is applied to define the objective function of the optimization techniques. The simulation results show that the SAA showed the lowest mean computation time, followed by the GA and the CSA. A possible location of the DPV is bus no. 6 for FCSs with high penetration levels, and the best FCS locations can be found with the GA, with the best percentage of best hit counter on buses no. 2, 3, 13, 14, 28, 15, and 27. Therefore, FCSs can be managed and handled in optimal conditions, and this work supports future FCS expansion.

A comparative study of meta-heuristic and conventional optimization techniques of grid connected photovoltaic system

This paper presents the meta-heuristic and conventional optimizations techniques for the grid connected photovoltaic solar system. The perturb and observe (P&O) and particle swarm optimization (PSO) algorithms are proposed to track the maximum power point (MPP) of the photovoltaic solar system (PVSS). The regularization of the current supplied into the grid is ensured by the proportional integral (PI) corrector whose parameters are generated by the genetic algorithm (GA). The results of these two MPPT methods are compared and showed that the PSO is more efficient than the P&O. The use of GA algorithm to determine PI parameters allowed to obtain 0.89% of total distortion harmonic (THD).

Antlion Optimization Algorithm for Optimal Self-Scheduling Unit Commitment in Power System Under Uncertainties

optimal and economic operation is one of the main topics in power systems. In this paper, a stochastic single objective framework for GenCoʼs optimal self-scheduling unit commitment under the uncertain condition and in the presence of SH units is proposed. In order to solve this problem, a new meta-heuristic optimization technique named antlion optimizer (ALO) has been used. Some of the capabilities of the ALO algorithm for solving the optimization problems included : (1) the exploration and utilization, (2) abiding convergence, (3) capable of maintaining population variety, (4) lack of regulation parameters, (5) solving problems with acceptable quality. To approximate the simulation conditions to the actual operating conditions, the uncertainties of the energy price, spinning and non-spinning reserve (operating services) prices, as well as the renewable energy resources uncertainty, are considered in the proposed model. The objective function of the problem is profit maximization and modeled as a mixed-integer programming (MIP) problem. The proposed model is implemented on an IEEE 118-bus test system and is solved in the form of six case studies. Finally, the simulation results substantiate the strength and accuracy of the proposed model.

Metaheuristic optimization in neural network model for seasonal data

The use of metaheuristic optimization techniques in obtaining the optimal weights of neural network model for the time series was the main part of this research. The three optimization methods used as experiments were genetic algorithm (GA), particle swarm optimization (PSO), and modified bee colony (MBC). Feed forward neural network (FFNN) was the neural network (NN) architecture chosen in this research. The limitations and weaknesses of gradient-based methods for learning algorithm inspired some researchers to use other techniques. A reasonable choice is non-gradient based method. Neural network is inspired by the characteristics of creatures. Therefore, the optimization techniques which are also resemble the patterns of life in nature will be appropriate. In this study, various scenarios on the three metaheuristic optimization methods were applied to get the best one. The proposed procedure was applied to the rainfall data. The experimental study showed that GA and PSO were recommended as optimization methods at FFNN model for the rainfall data.

An over current relay coordination: A comparative analysis of metaheuristic and linear program approach

A number of recent or novel metaheuristic algorithms have been explored in the subject of optimization; although now not all of these algorithms are as efficient as their creators claim, a few have been confirmed to be a pretty efficient and useful tool for addressing complex optimization problems. On the other aspect, LINGO is a software tool used for linear, nonlinear, optimization problems. This paper's objective is to study how well the LINGO optimizer performs as contrasted to metaheuristic optimization strategies for over current relay coordination. The operation of directional over current relay is demonstrated with the applicability of LINGO optimizer and metaheuristic strategies. In this study, two distinct case studies, which include a mixed overhead line-underground cable and a DER primarily based IEC microgrid benchmark, are tested for comparative evaluation among the LINGO optimizer and metaheuristic strategies. During the first stage of case study I, the crow search algorithm (CSA), a novel metaheuristic technique, was proposed, and its results are contrasted with the most popular conventional techniques: Particle swarm optimization (PSO) and water cycle algorithm (WCA). It reveals that implementing the proposed CSA algorithms improved over all time setting concerning the most common traditional techniques such as PSO and WCA. And, after implementing the LINGO optimizer, it is proven that compared with other metaheuristic optimization techniques, the second one proposed LINGO optimizer provides advanced consequences. In a similar manner, both proposed methods were applied and determined their performances in case study II and once more LINGO proved its strength over the metaheuristic approach.

Enhancement of frequency stability of power systems integrated with wind energy using marine predator algorithm based PIDA controlled STATCOM

This paper proposes a novel optimized Proportional-Integral-Derivative-Acceleration (PIDA) controlled static synchronous compensator (STATCOM) to improve the frequency response of power systems through reactive power modulation. The proposed controller can maintain the system frequency within permissible limits upon load change or generation loss. The gains of the PIDA controller are fine-tuned using the Marine Predator Algorithm (MPA), which is a newly metaheuristic optimization technique. Moreover, the performance of the MPA-PIDA controlled STATCOM is tested in the two-area four-machine system (Kundur system) and the New England IEEE 39-bus system. The results show that the MPA-PIDA controller can provide better frequency response compared with the multi-band controller presented in the previous literature. In addition, the robustness of the proposed controller is verified when case studies are integrated with wind generation. The results show that the primary frequency response is greatly improved by using the MPA-PIDA controlled STATCOM in all studied cases. Moreover, the results are given in the form of time domain simulations using MATLAB/SIMULINK.

Hybrid Particle Swarm and Ranked Firefly Metaheuristic Optimization-Based Software Test Case Minimization

Software testing is a valuable and time-consuming activity that aims to improve the software quality. Due to its significance, combinatorial testing focuses on fault identification by the interaction of small amount of input factors. But, deep testing is not sufficient due to time or resources availability. To select the optimal test cases with least computation time, Hybrid Multi Criteria Particle Swarm and Ranked Firefly Metaheuristic Optimization(HMCPW-RFMO) technique are introduced. Initially, the population of the test cases is randomly initialized. Then the fitness is calculated by the pairwise coverage, execution cost, fault detection capability and average execution frequency. RFM approach starts with ‘n’ fireflies. The light intensity of each firefly gets initialized.If the light intensity of one firefly is minor than the other one, it moves near the brighter one. Next, the rank is given to the firefly based on their light intensity. Lastly, the high ranked firefly is chosen as a global best solution.The result reveals that HMCPW-RFMO technique improves the software quality.

Metaheuristic optimization techniques to design solar-fuel cell-battery energy system for locomotives

This work proposes a design methodology of a Solar-Fuel Cell-Battery Energy System (SFCBES), which is proficient in supplying the energy requirements of a passenger train and with least cost, decide ratings of fuel cell pack and battery. The prospective work considers Indian Railways passenger locomotive with its real-time driving cycles. A design optimization method is articulated to solve cost minimization objective function, under the constraints such as fuel cell capacity, battery SOC limits, and average power demand of the locomotive. Detailed modelling of the hybrid power sources and locomotive is done and estimated for two practical driving cycles of the locomotive. Various cases including two intelligent energy management methods, three gradients, a hybrid power system with and without solar panels are considered. In this work, six metaheuristic algorithms namely Artificial Bee Colony (ABC), Differential Evolution (DE), Firefly Algorithm (FA), Particle Swarm Optimization (PSO), Invasive Weed Optimization (IWO), and Teaching Learning Based Optimization (TLBO) are employed in the solution of cost minimization problem. The results declared that, among various algorithms considered, TLBO gave a better performance with minimum cost and earlier convergence among the cases considered.

The effect of maximum iteration using 3 dimensional limit equilibrium method in open pit mine

Nickel ore mines have a high potential of landslides due to their weak material base, which consists of soil. It is caused by the increase of soil density in rain conditions, leading to decreased soil shear strength (c) and internal friction angle (ϕ). This research aims to determine the optimum value of the maximum iteration number based on the Cuckoo Search and Particle Swarm Optimization search method. In this research, the slope is analyzed using the 3 Dimensional limit equilibrium method “Simplified Bishop,” a slope stability analysis method that uses the principle of static equilibrium. Alongside this method, the Cuckoo Search and Particle Swarm Optimization is adopted. The Cuckoo Search and Particle Swarm Optimization are metaheuristic optimization techniques used as the slipped surface search method. Series of 3-dimensional limit equilibrium computation is performed using different amounts of nests in the cuckoo search method and different particle values and maximum iteration number. Cuckoo Search method to achieve optimal nest 100 and iteration of 80 with the fastest compute time of 3 minutes 49 seconds. While the Particle Swarm Optimization to achieve optimal on particles 60, iteration as much as 480 with a compute time of 6 minutes 46 second, with a factor of safety value of 1,12.

Contrast Enhancement of Images Using Meta-Heuristic Algorithm

One of the most important processes in image processing is image enhancement, which aims to enhance image contrast and quality of information. Due to the lack of adequate conventional image enhancement and the challenge of mean shift, intelligence-based image enhancement systems are becoming an essential requirement in image processing. This paper proposes a new approach for enhancing low contrast images utilizing a modified measure and integrating a new Chaotic Crow Search (CCS) and Krill Herd (KH) Optimization-based metaheuristic algorithm. Crow Search Algorithm is a cutting-edge meta-heuristic optimization technique. Chaotic maps are incorporated into the Crow Search Method in this work to improve its global optimization. The new Chaotic Crow Search Algorithm depends on chaotic sequences to replace a random location in the search space and the crow's recognition factor. Based on a new fitness function, Krill Herd optimization is utilized to optimize the tunable parameter. The fitness function requires different primary objective functions that use the image's edge, entropy, grey level co-occurrence matrix (GLCM) contrast, and GLCM energy for increased visual, contrast, and other descriptive information. The results proved that the suggested approach outperforms all-new methods in terms of contrast, edge details, and structural similarity, both subjectively and statistically.