Modified Optimization Algorithm Research Articles

Parallel Genetic Algorithm Interface II: A novel computational tool for accelerated simulation-based optimization

The ever increasing power of computational tools encouraged by the general need for development of more sustainable technologies fuels the interest in modern optimization approaches. While simulation-based optimization has been receiving considerable attention in the past decades, it still struggles to overcome some challenges, namely excessive computation time. This study proposes a novel optimization interface, the Parallel Genetic Algorithm Interface II (PAGAN-II), which utilizes parallelization of flowsheet simulations to drastically reduce the optimization time without the need to use clustered CPUs and/or modified optimization algorithms. Results of a detailed performance study showed up to 2100% increase in computation rate when optimizing demanding process flowsheets; and approximately 300% increase when optimizing simple ones. Capabilities of the proposed interface were demonstrated by optimization of a 5 MTPA C3MR LNG technology processing 12 different feedstocks, where a 15–30% decrease in the specific energy consumption was achieved. At the same time, the algorithm increased the optimization speed 13-fold compared to the traditional approach. This translates into a reduction of optimization time from 69 days of non-stop computation to approximately 7 days.

Audio analysis speeding detection techniques based on metaheuristic-optimized machine learning models

Law enforcement plays an important role in road safety across the world. Speed enforcement merits special attention as the correlation between exceeding speed limits and the risk of fatalities and serious injuries is well-known. Methods based on radar and light detection and ranging techniques have established themselves as effective methods for measuring vehicle speeds in real time on roads. However, the price of instruments used for these measurements limits their widespread applicability. This work explored the potential of coupling artificial intelligence and audio analysis for vehicle speed assessment. The potential of deep neural networks and extreme gradient boosting is explored on a recently proposed real-world vehicle speed dataset. Frequency analysis techniques are also applied to help reduce the computational demands of experimentation. Additionally, a modified optimization algorithm is introduced to help select optimal control parameters and improve the performance of the suggested method. Five experiments were executed to demonstrate the potential for detecting the exact vehicle speed using regression techniques as well as utilizing classifications to detect vehicles breaking the speed limit. The proposed methods demonstrated promising potential when applied to this pressing challenge.

Interpretable hierarchical belief rule base expert system for complex system modeling

Belief rule base (BRB) expert system has been widely used in complex system modeling. Readability and consistency are the primary characteristics and advantages of BRB. However, these two characteristics may be lost in BRB establishment and optimization. Aiming at this problem, a novel hierarchical BRB (H-BRB) expert system is proposed. To quantify the interpretability of BRB, the definition of interpretability of BRB is proposed from the readability and the consistency. Two strategies are proposed to construct the H-BRB expert system with good readability. A new optimization method is proposed to guarantee the consistency of H-BRB, including a new optimization mechanism, a new objective function with interpretability constraints, and a modified optimization algorithm. With all the above, an H-BRB expert system with good readability and consistency can be established. A case study of the health state assessment of the aerospace relay is implemented to verify the effectiveness of the proposed method.

A modified Salp Swarm Algorithm for parameter estimation of fractional-order chaotic systems

For the parameter estimation problem in research related to the fractional-order chaotic systems (FOCSs), a modified optimization algorithm based on Salp Swarm Algorithm (SSA) was developed in this paper. The proposed algorithm introduced several improvements on SSA: adding a grouping step, introducing “betrayal” behavior, and improving the update method of the followers. We applied multiple classical optimization algorithms to conduct the parameter estimation experiments on the fractional-order Lorenz chaotic system (Lorenz-FOCS) and the fractional-order Financial chaotic system (Financial-FOCS). In addition, we explored the impact of searching space on parameters estimation through experiments. The experimental results confirmed the feasibility of the modified Salp Swarm Algorithm (MSSA). The MSSA performed better than the SSA and other classical optimization algorithms in terms of the estimation accuracy and convergence rate.

Brain Tumor Detection Through Modified Optimization Algorithm by Region-based Image Fusion

This article is about the fusion of Brain images which are having different features. This article addresses the problems raised in pixel-level image fusion,such as blurring and artifacts caused by the unwanted addition of noise components in fused images. We extracted the regions of the brain image with the proposed tested optimal thresholding-based segmentation by optimizing thresholds with the proposed Sine function adapted improved Whale Optimization Algorithm (SiWOA) algorithm. We named the proposed fusion as SiWOA-FUSION. These optimal segmented regions and discrete wavelet coefficients of images are fused based on interval type - 2 fuzzy rules. Finally, combined image visual quality is optimized with SiWOA by assuming the amount of the correlation of differences (ACD) as an objective function. Experiments are tested on standard benchmark databases and proved better than existing methods.

Optimization of prestressed concrete bridge girder section using a modified harmony search algorithm

Precast I girders provide rapid construction and structural simplicity, making them an increasingly popular choice for bridge construction. The current standard sections adopted by the American Association of State Highway and Transportation Officials (AASHTO) are not SI friendly and may be improved utilizing increasingly efficient metaheuristic optimization algorithms. Thus, this research study aims to obtain the optimum shape of Precast I (PCI) bridge girders commonly used in bridge construction due to various advantages. The optimization problem is solved using a modified metaheuristic optimization algorithm considering a number of constraints that are stipulated by the AASHTO LRFD code requirements. To show the capabilities of the utilized modified optimization algorithm, a comparison is performed with its baseline counterpart. The structural design optimization of bridge superstructures is performed with the objective of minimizing the total cost of the resulting superstructure. Consequently, the most economical shape of the PCI girders is obtained. The results of this analysis are then compared AASHTO and the California Department of Transportation (Caltrans) PCI girders. The new I girder sections exhibits 13–17% and 5–16% better structural efficiency factor (ρ) as well as 14–19% and 4–21% structural efficiency ratio (α) than Caltrans and AASHTO girders, respectively.Thus, new sections for PCI girders are developed having various heights to facilitate more economical design of bridges having various geometric properties such as span length, girder spacing and slab thickness.

Improved Gradient-Based Optimizer for Modelling Thermal and Hydropower Plants

Adequate and accurate models of thermal power plants (TPP) and hydropower plants (HPP) are of great importance for the optimal economic functioning of electric power systems. The extracted models have a direct impact on economic dispatch calculations. In this research, a modified optimization algorithm called an improved gradient-based optimizer (IGBO) is deployed for the optimal extraction of TPP and HPP input-output parameters. Firstly, the IGBO is tested for use with well-known benchmark functions and shows outstanding performance over the original GBO and other competitive algorithms. For the input-output parameters extraction of TPP and HPP, the sum of the absolute error (SAE) is utilized as a fitness function to be minimized. Secondly, nine models of TPP and HPP are employed for parameter identification using IGBO. Simulation outcomes prove the capability of IGBO to accurately extract input-output parameters of TPP and HPP. Moreover, the convergence characteristics of IGBO are remarkable among investigated optimization algorithms.

Implementation of Modified Multi-Objective Particle Swarm Optimization to multi-machine power system stability

This paper addresses a coordinated wind turbine equipped with a doubly fed induction generator, photovoltaic plant, and a fractional lead-lag stabilizer to enhance the low-frequency oscillation (LFO) in response to their effects on the power system under load conditions. To achieve better stability in the power system, the DFIG wind turbine and PV plant are equipped with a power oscillation damper (POD) in a practical form by single-input signal output. Although power system stabilizers (PSSs) are the first choice offered for multimachine power system stability, they cannot guarantee power system stability with different types of power plants and their interconnections. To get the high-speed response and the least steady-state error, the fractional-order lead-lag stabilizer is suggested. To save the initial price of equipping all generators with the stabilizer, only the best locations are selected using a probabilistic method formulated in the frequency domain. The optimal tuning of FL-L and POD variables is formulated by the optimization problem, and the developed version of particle swarm optimization is used to solve it. Finally, the suggested coordinated controller is implemented in the MATLAB tool. Moreover, the suggested controller is compared to other available controllers by several indices. The simulation results show that the proposed controller can provide acceptable performance in low frequency oscillation in large scale power systems. According to the simulation results, the damping ratio was modified from 0.1343 to 0.5773. It leads to a reduction in the settlement time from 3.65 s to 2.14 s. Also, the proposed modified optimization algorithm makes a faster and better global and local search compared to other optimization algorithms.

Opposition-Based Deer Hunting Optimization-Based Hybrid Classifier for Intrusion Detection in Wireless Sensor Networks

This paper tempts to implement a new machine-learning algorithm for detecting attacks in WSN. The developed model involves three main phases (a) Data Acquisition, (b) Feature Extraction, and (c) Detection. Next to the data acquisition from different benchmark datasets, the attributes in the form of features are extracted. Further, a new hybrid machine learning algorithm with the integration of Neural Network (NN), and Fuzzy Classifier is used for detection, and it is termed as FNN. As an improvement to the developed hybrid model, the number of hidden neurons in NN, and the membership function of Fuzzy Classifier is optimized by a modified optimization algorithm called Opposition-based Deer Hunting Optimization Algorithm (O-DHOA). Finally, the experiment analysis of our proposed model provides an effective solution to solve the problem of IDS detection and improves the performance of intrusion detection.

Robust Parameters Tuning of Different Power System Stabilizers Using a Quantum Artificial Gorilla Troops Optimizer

Electrical power system abnormalities may have several negative consequences on its stable operation. As a result, preserving its stability under such operational states has become an ongoing challenge for power engineers. PSSs are created as auxiliary controllers to address the instability issues produced upon disturbances. They dampen the oscillations induced by the disturbances by giving the system the necessary damping torque. This research aims at presenting a comprehensive study for the optimum tuning of power system stabilizer (PSS) of different structures. This aim is accomplished with the help of a novel modified optimization algorithm called Quantum Artificial Gorilla Troops Optimizer. Validation of the modified optimizer is firstly investigated with the well-known benchmark optimization functions and shows superiority over Gorilla Troops Optimizer and competitive algorithms. The research is extended to the application of the optimum tuning of various PSS structures of the single machine to the infinite bus model. The proposed optimization algorithm shows fast convergence over investigated optimization algorithms. Moreover, the Tilt-integral-derivative based PSS shows better performance characteristics in terms of lower settling time, and lower maximum and undershoot values over the conventional lead-lag PSS, dual input PSS, and fractional-order proportional-integral-derivative based PSS.

On the Interpretability of Belief Rule-Based Expert Systems

As the generalization of fuzzy systems, the belief rule base (BRB) expert system is transparent and interpretable. However, the interpretability of BRB has almost been ignored recently and leads to the decrease of model credibility. The main reason is the lack of unified guidelines for establishing an interpretable BRB expert system. In this article, the interpretability characteristics of BRB are summarized systematically, which can be used as the guideline of BRB establishment. Four interpretability criteria are proposed to ensure the interpretability of BRB in the optimization. A modified optimization algorithm with the interpretability constraints transformed from the interpretability criteria is further developed. As such, an interpretable BRB can be established. A case study for health state evaluation of the aerospace relay is conducted to verify the effectiveness of the proposed method.

Performance evaluation of Non-Uniform circular antenna array using integrated harmony search with Differential Evolution based Naked Mole Rat algorithm

In many wireless applications, circular antenna arrays are commonly used but managing the lower side lobe with high directivity is still challenging. Many typical methods could be used to synthesize the array in real time but lag behind to retain high directivity and the low sidelobe level. This study formulates an optimization problem to obtain the desired main lobe direction, suppress the subsidiary lobe, and maximizes the directivity. A modified optimization algorithm is proposed called Integrated Harmony Search with Differential Evolution based Naked Mole Rat Algorithm (IHDN) to rapidly and reliably refine control parameters. Simulations are performed, and results are compared to other traditional algorithms. The findings show that the proposed algorithm results in excellent side lobe minimization with good directivity. The size analysis indicates that good directivity values with low sidelobe levels can be obtained for a smaller number of antenna elements.

Prediction method for thermal-hydraulic parameters of nuclear reactor system based on deep learning algorithm

The nuclear reactor core and the steam generator are the core components of nuclear reactor system, which directly determine the heat releasing and carrying capacity of nuclear reactor system. The traditional thermal-hydraulic design of nuclear reactor system generally selects the linear iterative mode, which requires the repeated “calculation-evaluation-correction”. The whole process is tedious and time-consuming, which also relies on the design experience. In recent years, the successful application of artificial intelligence technology in many fields has brought new enlightenment to the innovation for the thermal-hydraulic design of nuclear reactor system. In this paper, a prediction program for the thermal-hydraulic parameters of nuclear reactor system was established by means of deep learning algorithm, which was mainly composed of the neural network module, the activation function module, the error function module, the optimization algorithm module and the data initialization module. Then, the KLT-40S nuclear reactor core and the tube-in-tube once-through steam generator were selected as the analysis objects, and the corresponding input card of RELAP5/SCDAPSIM program was built. Based on the prediction program, the calculation results of RELAP5/SCDAPSIM program were studied, which could realize the rapid prediction for the thermal-hydraulic parameters of nuclear reactor system, and were in good agreement with the calculation results of RELAP5/SCDAPSIM program.

Modified teaching-learning-based optimization by orthogonal learning for optimal design of an electric vehicle charging station

The provision of a safe environment has led to the growth of electric vehicles (EVs), whose propagation in the market depends on features such as price, battery technology, economy, and improvement of charging stations. This paper proposes a charging station for plug-in electric vehicles (PEVs) connected to the distribution system, along with the energy storage system's batteries, diesel generator, and photovoltaic panels. The charging facilities are also designed and optimized at three levels of fast, medium, and slow speeds. Since this model integrates many decision variables and cannot be accurately solved by traditional mathematical methods, a new modified optimization algorithm is presented. The modified teaching-learning-based optimization (TLBO) based on orthogonal learning (OL), or OLTLBO, is proposed to solve the optimization problem. The results confirm that the model successfully uses all the available options to design the EVCS.

Grey wolf optimizer and other metaheuristic optimization techniques with image processing as their applications: a review

Image processing is an evolutionary field in the domain of computer vision that currently has a comprehensive spectrum of applications. It is being employed in image segmentation, image classification, medical imaging, image compression, etc. A lot of real-world prominent issues are tackling employed through these application techniques. These techniques can be employed by means of various algorithms; however, these offered immensely dominant outcomes with existing and modified optimization algorithms. Some of the metaheuristic optimization algorithms applied during the above techniques include Ant Colony Optimization (ACO), Genetic Algorithm (GA), Bat Algorithm (BA), Grey Wolf Optimizer (GWO), Evolutionary Strategy (ES), Particle Swarm Optimization (PSO), Genetic Programming (GP), and so forth. Hence, this manuscript’s main objective is that the study of several applied optimization algorithms and their variants thus lead to the various domain of image processing concludes it work more efficiently and robustly.

Three-spool turbofan pass-off test data analysis using an optimization-based diagnostic technique

Production engine pass-off testing is a compulsory technique adopted to ensure that each engine meets the required performance criteria before entering into service. Gas turbine performance analysis greatly supports this process and substantial economic benefits can be achieved if an effective and efficient analysis is attained. This paper presents the use of an integrated method to enable engine health assessment using real pass-off test data of production engines obtained over a year. The proposed method is based on a well-established diagnostic technique enhanced for a highly-complex problem of a three-spool turbofan engine. It makes use of a modified optimization algorithm for the evaluation of the overall engine performance in the presence of component degradation, as well as, sensor noise and bias. The developed method is validated using simulated data extracted from a representative adapted engine performance model. The results demonstrate that the method is successful for 82% of the fault scenarios considered. Next, the pass-off test data are analyzed in two stages. Initially, correlation and trend analyses are conducted using the available measurements to obtain diagnostic information from the raw data. Subsequently, the method is utilized to predict the condition of 264 production turbofan engines undergoing a compulsory pass-off test.

A modified simplex based direct search optimization algorithm for adaptive transversal FIR filters.

In this paper, a novel modified optimization algorithm is presented, which combines Nelder-Mead (NM) method with a gradient-based approach. The well-known Nelder Mead optimization technique is widely used but it suffers from convergence issues in higher dimensional complex problems. Unlike the NM, in this proposed technique we have focused on two issues of the NM approach, one is shape of the simplex which is reshaped at each iteration according to the objective function, so we used a fixed shape of the simplex and we regenerate the simplex at each iteration and the second issue is related to reflection and expansion steps of the NM technique in each iteration, NM used fixed value of , that is, = 1 for reflection and = 2 for expansion and replace the worst point of the simplex with that new point in each iteration. In this way NM search the optimum point. In proposed algorithm the optimum value of the parameter is computed and then centroid of new simplex is originated at this optimum point and regenerate the simplex with this centroid in each iteration that optimum value of will ensure the fast convergence of the proposed technique. The proposed algorithm has been applied to the real time implementation of the transversal adaptive filter. The application used to demonstrate the performance of the proposed technique is a well-known convex optimization problem having quadratic cost function, and results show that the proposed technique shows fast convergence than the Nelder-Mead method for lower dimension problems and the proposed technique has also good convergence for higher dimensions, that is, for higher filter taps problem. The proposed technique has also been compared with stochastic techniques like LMS and NLMS (benchmark) techniques. The proposed technique shows good results against LMS. The comparison shows that the modified algorithm guarantees quite acceptable convergence with improved accuracy for higher dimensional identification problems.

Artificial neural network with modified rider optimization for design and control of PV-integrated quasi Z-source cascaded multilevel inverter system

The modeling of the controller for quasi Z-Source Cascaded Multilevel Inverter (qZS-CMI)-dependent 3-phase grid-tie Photovoltaic (PV) power system is considered in this paper. The state-of-the-art controller requires precise conceptual models and sophisticated optimization principles based on the derived models. However, such processes are limited to known system models, which are uncertain in future systems. Here, the controller for 3-phase qZS-CMI is modeled based on two phases, and the source PV voltage and output grid current are controlled. In Phase I, optimized Proportional Integral (PI) controller is used for finding out the total PV voltage, and Phase II utilizes the optimized Proportional Resonant (PR) controller enabled with the Artificial Neural Network (ANN) for controlling the grid current. For two phases, the modified optimization algorithm called Fitness Enabled-Rider Optimization Algorithm (FE-ROA) is used. Moreover, in Phase II, ANN is trained in an offline mode with the exact dataset arranged by the proposed FE-ROA, and it guarantees the control of grid current. The two phases plan to optimize the gain of both PI and PR controllers respectively using the same proposed algorithm. The main objective of phase I is to lessen the error among the reference PV voltage, and measured voltage, and phase II is to lessen the error among the reference and measured grid current. Hence, the grid-tie current injection is achieved by the developed module, and system-level control offers independent Maximum Power Point Tracking (MPPT). Lastly, the performance of the proposed controller for qZS-CMI is compared over the other controllers and substantiates the efficacy of the proposed one.

Modified whale optimisation-based optimal location of UPQC in distribution system for power quality improvement

A number of researchers has studied the unified power quality conditioner (UPQC) as an eventual solution to enhance the power quality in the electrical distribution system. However, due to the high cost, the location or position of UPQC in distribution system needs to be decided with immense care and must preferably be solved as the optimisation problem. This paper presents a power quality improvement model based on a new modified optimisation algorithm, namely worst solution linked whale optimisation algorithm update (WS-WU). The proposed algorithm finds the optimal location of the UPQC device concerning the power system losses, UPQC cost, and voltage stability index as well. The experimentation is carried out in both IEEE 33 and 69 bus systems. The performance of the proposed model is compared over other conventional methods in terms of performance and convergence analysis, and proves the superiority of the proposed method.

Application of chaotic Fish School Search optimization algorithm with exponential step decay in neural network loss function optimization

The Fish School Search (FSS) algorithm is a heuristic technique for finding globally optimal solutions. This algorithm is characterized by its simplicity in implementation, and high performance. Since the first mention of FSS, this effective optimization algorithm has been of a great interest among researches and practitioners around the globe. Modifications of FSS exist, applied to solve practical problems, including image reconstruction in electrical impedance tomography, finding optimal solutions in assembly line balancing problems, neural network structure optimization. In this paper, we consider a modification of the FSS algorithm, which uses chaos theory to generate uniformly distributed pseudorandom numbers, and incorporates exponential step decay. The described modified optimization algorithm is known as ETFSS, and is characterized by faster convergence speed and better performance. In order to further investigate the performance of the novel optimization algorithm, we apply ETFSS to neural network loss function optimization. In addition, we compare the described approach with other machine learning techniques, such as the support vector machine (SVM) algorithm, k-nearest neighbors (KNN) algorithm and back propagation-based neural network, trained using the adaptive moment estimation (Adam) optimizer. We visualize classification results using T-distributed stochastic neighbor embedding (TSNE) method, and uniform manifold approximation and projection (UMAP) method, in order to provide more details considering classification performance and dataset shape. The obtained results confirm, that ETFSS can produce slightly more accurate classifications when compared to backpropagation.