Search Optimization Algorithm Research Articles

Image Multi-threshold Segmentation Based on an Ameliorated Harmony Search Optimization Algorithm

Image segmentation is the basis and premise of image processing, though traditional multi-threshold image segmentation methods are simple and effective, they suffer the problems of low accuracy and slow convergence rate. For that reason, this paper introduces the multi-threshold image segmentation scheme by combining the harmony search (HS) optimization algorithm and the maximum between-class variance (Otsu) to solve them. Firstly, to further improve the performance of the basic HS, an ameliorated harmony search (AHS) is put forward by modifying the generation method of the new harmony improvisation and introducing a convergence coefficient. Secondly, the AHS algorithm, which takes the maximum between-class variance as its objective function, namely AHS-Otsu, is applied to image multi-level threshold segmentation. Finally, six test images are selected to verify the multilevel segmentation performance of AHS-Otsu. Peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) are two commonly used metrics for evaluating the effectiveness of image segmentation, which are both used in this article. Comprehensive experimental results indicate that the AHS-Otsu does not only has fast segmentation processing speed, but also can obtain more accurate segmentation performance than others, which prove the effectiveness and potential of the AHS-Otsu algorithm in the field of image segmentation especially for the multi-threshold.

Comparative analysis of optimization algorithm-based training algorithms for a few shot continual learning

Incremental learning relies on the availability of ample training data for novel classes, a requirement that is often unfeasible in various application scenarios, particularly when new classes are rare groups that are pricey or challenging to attain. The main focus of incremental learning is on the tricky task of continuously learning to classify new classes in incoming data with no erasing knowledge of old classes. The research intends to develop a comparative analysis of optimization algorithms in training few-shot continual learning models to conquer catastrophic forgetting. The presented mechanism integrates various steps: pre-processing and classification. Images are initially pre-processed through contrast enhancement to elevate their quality. Pre-processed outputs are then classified by employing Continually Evolved Classifiers, generated to address a matter of catastrophic forgetting. Furthermore, to further enhance performance, Serial Exponential Sand Cat Swarm optimization algorithm (SE-SCSO) is employed and compared against ten other algorithms, containing Grey Wolf Optimization (GWO) algorithm, Moth flame optimization (MFO), cuckoo Search Optimization Algorithm (CSOA), Elephant Search Algorithm (ESA), Whale Optimization Algorithm (WOA), Artificial Algae Algorithm (AAA), Cat Swarm Optimization (CSO), Fish Swarm Algorithm (FSA), Genetic Bee Colony (GBC) Algorithm, and Particle swarm optimization (PSO). From the experiment results, SE-SCSO had attained the maximum performance with an accuracy of 89.6%, specificity of 86%, precision of 83%, recall of 92.3% and f-measure of 87.4%.

How Rare Are TESS Free-floating Planets?

Recently, Kunimoto et al. claimed that a short-lived signal in the Transiting Exoplanet Survey Satellite (TESS) Sector 61 database was possibly caused by a microlensing event with a terrestrial-mass free-floating planet (FFP) lens. In this study, we investigate TESS’s ability to detect microlensing FFPs by considering the detailed source information (e.g., distance and radius), the TESS photometric accuracy, and finite-source effects. Using the FFP mass function from microlensing surveys toward the Galactic bulge, we find that only 0.0018 microlensing events are expected to be detected in TESS Sector 61 for the entire planetary mass range. The reported signal is unlikely to be a real microlensing event, which is consistent with the evidence from the long-term Optical Gravitational Lensing Experiment data that the signal was likely due to a stellar flare. By extrapolating our result to fainter stars until T = 16 mag and adopting a possible optimized search algorithm, we find that only ∼1 FFP event can be detected in the entire TESS mission within the first 7 yr. Significant improvements in our understanding of FFPs still require future satellite missions, such as Roman and Earth 2.0, which can detect thousands of FFPs.

Enhanced brain tumor classification using EfficientNetB0 and SVM with pareto search algorithm optimization

Classifying tumors by type, grade, and stage is crucial for treatment decisions and predicting outcomes. Deep learning, especially Convolutional Neural Networks (CNNs), has significantly advanced tumor classification by effectively analyzing complex patterns in magnetic resonance (MR) images. This work presents a hybrid image classification method using the EfficientNetB0 model and Support Vector Machine (SVM) to categorize brain MR images into pituitary tumor, glioma tumor, meningioma tumor, and normal brain. EfficientNetB0 model extracts deep features from the images, which are then classified by a linear SVM. To significantly enhance classification accuracy for brain images, we use the Pareto algorithm to determine the penalty parameter C for the linear SVM. The testing results showed that the proposed system achieved a classification accuracy of 99.30%, recall of 99.30%, precision of 99.30%, and F1-score of 99.30%, with a high specificity of 99.77%. These results demonstrate that the combination of the Pareto algorithm and SVM significantly contributes to improved classification accuracy for brain images.

Computational design of residential units’ floor layout: A heuristic algorithm

Computer-aided intelligent design can improve the quality and efficiency of residential building design. However, the design conditions are diverse, and numerous combinations of spatial elements exist. We propose a heuristic algorithm—the combined Monte Carlo tree search (MCTS) and particle swarm optimization (PSO) algorithm (PSO-MCTS)—for the automated design of residential floor layouts. The MCTS algorithm deals with discrete variables (room locations). It considers architectural design experience and design standards during node pruning to compress the search space and simplify the computation. The PSO algorithm deals with continuous variables (room sizes) and accelerates the computing speed due to parallel processing. The PSO-MCTS algorithm achieves a good balance between computational efficiency and accuracy. It is suitable for various design conditions and requirements. The input parameters include the daylight façade, boundary scope, entrance location, room type and size, and relationships with adjacent rooms. The PSO-MCTS provides 100 %, 80 %, and 75 % better results than heuristic algorithms, designs by architects, and a state-of-the-art deep learning model, respectively.

Analysis Hybrid Approach for Multi-Objective Optimization-based segmentation of fuzzy contours in mammogram using artificial plant optimization algorithm

It is observed that importance is given to the identification of tumor in case of medical image diagnosis. It has been found that in women breast cancer is increasing and due to which it has an impact in the death rate also. In medical domain radiologist generally uses the computer aided detection methods (CAD ). Medical image often contains the noise and some fuzziness in the picture border so the identification of the tumor is having problem and it becomes challenging task. by Owing to the noise and fuzziness of the picture border, identifying tumors on mammograms is challenging. Even though Active contour is an effective method for the segmentation but It is having problem related to local minima. The proposed method is a hybrid which is consist of multi objective optimization and Gravitational search algorithm (GSA) used for segmentation after that results are given to an Artificial plant optimization algorithm (APOA). The operations involve in this process includes some subprocesses primarily contour initialization is done for ROI followed by segmentation through GSA and multi-objective optimization and then APOA is applied. To evaluate the results two different databases are used mini-MIAS and DDSM databases. Results are also compared with other research work carried out earlier. It is observed that our proposed method gives better output then other.

Bus Route Sketching: A Multimetric Analysis from the User’s and Operator’s Perspectives

The purpose of this study is to develop an optimal bus route search algorithm that considers both the user’s and supplier’s perspectives. The process of providing bus route service involves route network design, route allocation, and operation and management in sequence. Among these, establishing the optimal rationality for route network design in practical applications is challenging, and route modifications often occur during the operation process. To minimize these practical difficulties, this study proposes the Bus Route Sketch (BRS) methodology. This methodology, designed for network-level optimization, distinguishes itself from existing bus route setting methodologies by minimizing travel costs while taking user needs into account. This study yielded positive results, with the evaluation score improving from 8.83 to 9.50 from the supplier’s perspective and from 7.13 to 9.89 from the user’s perspective. This BRS methodology, developed to suit both route planning and operation processes, is expected to be utilized in the practical evaluation, adjustment, and design of bus routes.

Improved photovoltaic energy production under partial shading using an innovative MPPT controller based on Flying Squirrel Search Optimization algorithm

This paper explores the issue of partial shading (PS) in photovoltaic systems and proposes a solution using the Flying Squirrel Search Optimization (FSSO) algorithm. PS results in power losses, and our study aims to enhance photovoltaic energy production by introducing a new Maximum Power Point Tracking (MPPT) controller based on the FSSO algorithm. This paper presents a controller designed to efficiently track the maximum power in the presence of partial shading. The controller uses the Modified ODD-EVEN (MOE) configuration, the Modified Symmetrical Array (MSA), and the Total-Cross-Tied (TCT). The performance of the FSSO algorithm was compared with that of GWO and PSO. A bidirectional DC-DC converter was integrated to connect the PV system to a battery. The proposed methods were also applied to an electric vehicle powered by a PV battery. The FSSO proposed in this study demonstrates an efficiency of over 99%, outperforming other methods in tracking the maximum power point and converging faster to the overall maximum power point. The results indicate that convergence time can be improved from 16% to over 60%. Furthermore, the proposed MPPT technique effectively minimizes over 80% of random oscillations. The proposed method reduces losses and maximizes fill factor compared to alternative algorithms, based on two observed shading cases. When connecting the system to a battery, the FSSO algorithm outperforms the PSO by more than 34.78% and the GWO by 49.02%. Although slightly inferior to the PSO, the FSSO still performs significantly in the context of an electric vehicle powered by a photovoltaic battery.

A novel reduced local search algorithm for optimization of digital filter coefficients

Digital Filtering is an important operation in DSP. Most of the DSP applications demand low power implementations. In CMOS digital circuits, switching from logic 0 to logic 1 and vice-versa decides the dynamic power consumption. This paper proposes and develops a novel algorithm based on mixed integer linear programming to minimize the number of ones (filter cost) in filter coefficients, which in turn minimizes the switching factor of CMOS circuits and the number of additional adders needed for hardware implementation. The search space for finding the optimal value of the coefficient is reduced thereby increasing the speed of execution of the algorithm. The average reduction of 18.02 % in the number of transitions is obtained with the proposed algorithm. The suggested technique results in an 18.72 % decrease in the typical price of digital filters. Also the proposed algorithm works faster than the previously proposed algorithm used for finding the optimized coefficients.

Prediction model of moisture content in spray drying of ceramic slurry based on IMFO-BPNN

The final particle moisture content of ceramic slurry spray drying affects the processing, product quality, and process energy consumption. While the real-time moisture content determination is very difficult for spray drying process due to nonlinear, severe lag, interference factors, and complexity of the systems, the high temperature, high humidity drying environment and short drying process. In current work, an improved moth optimization (IMFO) algorithm combined with backpropagation neural network (BPNN) was proposed to predict the moisture content of ceramic slurry particles during spray drying. The performance of the model is trained and tested. Results demonstrate that IMFO has better convergence ability and speed compared to other algorithms such as particle swarm optimization (PSO) and gravitational search algorithm (GSA). The IMFO-BPNN model achieves a MAE of 0.0293, RMSE of 0.0383, and R2 value of 0.9113, outperforming the prediction performance of BPNN and MFO-BPNN models. The absolute error rate of the IMFO-BPNN model (0–5%) is lower compared to BPNN (5–10%) and MFO-BPNN model (>10%), showcasing superior accuracy in predicting moisture content. This mathematical model established in the study provides an efficient, accurate, and nondestructive method for predicting the drying endpoint of ceramic slurry spray drying.

Eco-environmental dispatch of power system with high penetration wind farms considering demand/source side uncertainties

In this paper, a stochastic economic-emission dispatch (EE-D) of a power system with wind farms and flexible loads is proposed. The EE-D is defined as a stochastic multi-objective optimization problem, and a heuristic hybrid multi-objective algorithm is also proposed to solve it. The proposed algorithm is a combination of the lightning search algorithm and multi-objective particle swarm optimization (hLSA-MOPSO). The simulations are conducted in two sections. In the first section, the performance of the proposed algorithm is evaluated by optimizing standard benchmark functions. In the second section, the EE-D problem is solved optimally, considering the impact of uncertainties and the participation of flexible loads, as well as without considering them. Simulations are conducted on a standard 40-unit power system that includes two wind farms. Additionally, a sensitivity analysis is performed to examine the effects of uncertainties and flexible loads on cost and emissions. The sensitivity analysis demonstrates that increasing the penetration rate of wind farms results in reduced costs and emissions. However, it also reveals that the impact of this increase diminishes as the penetration rate continues to rise. Also, the uncertainty analysis reveals that the simultaneous uncertainty of load and wind has the most significant impact on costs and emissions.

Energy-exergy-economic-environmental (4E) analysis and multi-objective optimization of a cascade refrigeration system

The present work focusses on 4E analysis of a 50 kW cooling capacity cascade refrigeration cycle covering the aspects of energy, exergy, economic and environment analysis. The numerical investigation and the multi-objective optimization is carried out for the system using the refrigerant pair R170-R600a and R41-R600a. The refrigerant pair is selected based on the environmental implications in terms of GWP and ODP. Multi-objective optimization of the objective functions is carried out using a heat transfer search optimization algorithm to evaluate the optimal performance of the system. The effect of evaporator temperature, condenser temperature, LTC condenser temperature and LTC condenser temperature difference on the exergy efficiency and total cost of the system is studied. A set of multiple optimal solutions is presented using the Pareto optimal curve and TOPSIS criteria is employed to select the optimal operating condition. Compared to the refrigerant pair R170-R600a, the system with R41-R600a operates at better exergy efficiency and lower total cost. At the TOPSIS selected optimal condition, exergy efficiency and the total cost of the CRS is 63.5 % and 65,228 $/year for the refrigerant pair R41-R600a and 62.6 % and 67,690 $/year for R170-R600a, respectively. The distribution of variables shows that the effect of the evaporation temperature, condensation temperature and the LTC condenser temperature is profound in obtaining the optimal solution.

Research on heliostat field design based on mesh search optimization algorithm

Research on heliostat field design based on mesh search optimization algorithm

Hybridizing flower pollination algorithm with particle swarm optimization for enhancing the performance of IPv6 intrusion detection system

Internet Protocol version 6 (IPv6) is the most recent iteration of IP, designed to accommodate hundreds of thousands of devices with unique IP addresses. IPv6 introduces new features, such as the Neighbor Discovery Protocol (NDP) and Address Auto-configuration Scheme. For its effective operation, IPv6 relies on several protocols, including ICMPv6, which carries significant responsibilities. Similar to IPv4, IPv6 is susceptible to various attacks, including newer types like DDoS attacks executed via ICMPv6 messages, posing serious security and financial threats. Consequently, an Intrusion Detection System (IDS) is essential to safeguard against these attacks. IDS continuously evolve to incorporate features that can accurately detect such threats. However, feature selection strategies, particularly bio-inspired algorithms, often yield incorrect subsets of features. During machine learning processes, these inaccuracies impede the detection accuracy of DDoS attacks using ICMPv6. Many Optimization Search Algorithms become trapped in local minima and fail to consider multi-objective approaches, resulting in suboptimal feature selection. To address this, optimizing a bio-inspired algorithm within an IPv6 network has been proposed. Specifically, hybridizing the MFPA algorithm with the PSO algorithm is suggested to enhance detection accuracy. The selected features are used to train the dataset with a Support Vector Machine (SVM) classifier. The proposed approach is evaluated using the ICMPv6 dataset on various attacks, demonstrating superior classification accuracy of 97.99 %. It also reduced the number of features from 19 to 8, showcasing its efficiency.

Optimized brain tumor identification via graph sample and aggregate-attention network with Artificial Lizard Search Algorithm

A brain tumour is an abnormal growth of brain nerves that interferes with normal brain function.It causes a great deal of deaths. Timely detection and treatment are essential for saving the lives from this illness. Locating tumor-affected brain cells is a tedious, time-consuming process. In this manuscript, a Graph Sample and Aggregate-Attention Network with Artificial Lizard Search Optimization Algorithm for identification of brain tumour (GSAAN-ALSOA-BTI) is proposed. Initially, the input imageries are obtained from Figshare Brain Tumor Dataset. The images are preprocessed using Data-Adaptive Gaussian Average Filtering (DAGAF) to eliminate the unnecessary noise from image frames. The pre-processed image is given into the Multi kernel k-Means clustering (MKKMC) for image segmentation to identify boundaries in an image. Finally, the extracted feature features are given to Graph Sample with Aggregate-Attention Network (GSAAN) for categorizing the brain tumour identification as benign and malignant tumors. In general, GSAAN does not express any adaption of optimization techniques for determining the ideal parameters to assure exact classification of brain tumour identification. Thus, the Artificial Lizard Search Optimization Algorithm (ALSOA) is proposed to improve the weight parameter of GSAAN, which accurately categorizes the brain tumour identification of brain tumor. The proposed model is executed in Python and its efficacy is evaluated utilizing performance metrics like accuracy, precision, recall, FI-score, error rate, computation time and ROC. The GSAAN-ALSOA-BTI method provides higher accuracy, higher precision, higher recall compared with existing methods.

Pipeline leak location method based on SSA-VMD with generalized quadratic cross-correlation*

Abstract Natural gas pipelines are an essential part of the economy. Natural gas pipelines may leak after aging, strong vibration signals may be generated in the pipeline when leakage occurs, and vibration signals may be noisy. Traditional variational mode decomposition (VMD) noise reduction methods need to set parameters in advance, and so may not achieve the best decomposition effect. To solve this problem, this paper proposes a method for pipeline leakage location based on the sparrow search algorithm (SSA) optimization of VMD combined with generalized quadratic cross-correlation. The method first calculates the original signal-to-noise ratio, and if the signal-to-noise ratio is low, wavelet threshold denoising is used to process the signal. Then, SSA optimization is used to refine the two key parameters of VMD (penalty parameter α and mode decomposition number K) based on sample entropy. Subsequently, the signal undergoes decomposition into K intrinsic mode function (IMF) components through VMD according to the obtained analysis parameter combination. Then, the IMF components are screened to obtain the reconstructed signal. Finally, the noise reduction signal is obtained. The signal delay after noise reduction is obtained through a generalized quadratic cross-correlation and the accurate leakage position is obtained using the delay. Experiments showed that the minimum relative error of this method could reach 0.6%, which was more accurate than the traditional VMD method, and effectively improved the accuracy of noisy signals in pipeline leakage locations.

Multi-objective discrete Cuckoo search algorithm for optimization of bag-of-tasks scheduling in fog computing environment

Internet of Things (IoT) applications are mostly time-sensitive and require reliable executions. The majority of IoT applications experience low quality of service (QoS) because of the intrinsic delay in utilizing the far-distant cloud infrastructures. To experience better QoS, fog computing as a complementary facility for cloud infrastructure is extended near the IoT devices. The bag of tasks (BoTs) IoT applications include a number of independent tasks each of which needs a certain amount of processing time so it is favorable to experience the lowest total execution time, makespan, for all applications. In addition, reliable execution guarantees the business continuity of IoT applications. Therefore, this paper presents a fog computing scheduler for the execution of BoT IoT applications in the fog platforms to minimize makespan and maximize the reliability execution at the same time. To this end, a new reliability model is presented. Then, the BoTs scheduling issue is formulated into a multi-objective discrete optimization problem with makespan and reliability optimality viewpoints. To solve this combinatorial problem, a multi-objective discrete cuckoo search algorithm (MoDCSA) is extended. The proposed MoDCSA optimization algorithm takes the benefit of several discrete operators each of which is smartly invoked. The quality of gained results from simulations of extensive scenarios verifies the effectiveness of the proposed MoDCSA scheduling algorithm which has 2.27%, 3.89%, 5.45%, 5.40%, and 4.55% improvement in terms of makespan and 1.99%, 5.11%, 6.39%, 7.95%, and 7.78% improvement in terms of reliability against MoBLA, MoGA, MoGWO, MoSA, and MoGSA comparative algorithms respectively.

Calibrating Tropical Forest Coexistence in Ecosystem Demography Models Using Multi‐Objective Optimization Through Population‐Based Parallel Surrogate Search

AbstractTropical forest diversity governs forest structures, compositions, and influences the ecosystem response to environmental changes. Better representation of forest diversity in ecosystem demography (ED) models within Earth system models is thus necessary to accurately capture and predict how tropical forests affect Earth system dynamics subject to climate changes. However, achieving forest coexistence in ED models is challenging due to their computational expense and limited understanding of the mechanisms governing forest functional diversity. This study applies the advanced Multi‐Objective Population‐based Parallel Local Surrogate‐assisted search (MOPLS) optimization algorithm to simultaneously calibrate ecosystem fluxes and coexistence of two physiologically distinct tropical forest species in a size‐ and age‐structured ED model with realistic representation of wood harvest. MOPLS exhibits satisfactory model performance, capturing hydrological and biogeochemical dynamics observed in Barro Colorado Island, Panama, and robustly achieving coexistence for the two representative forest species. This demonstrates its effectiveness in calibrating tropical forest coexistence. The optimal solution is applied to investigate the recovery trajectories of forest biomass after various intensities of clear‐cut deforestation. We find that a 20% selective logging can take approximately 40 years for aboveground biomass to return to the initial level. This is due to the slow recovery rate of late successional trees, which only increases by 4% over the 40‐year period. This study lays the foundation to calibrate coexistence in ED models. MOPLS can be an effective tool to help better represent tropical forest diversity in Earth system models and inform forest management practices.

Cam displacement curve optimization for minimal jerk using search and rescue optimization algorithm

Polynomials of a degree higher than 8th used for the rise phase of displacement curve of a cam profile were optimized for gaining minimal jerk using the search and rescue optimization algorithm. The algorithm was used to obtain the polynomial coefficients for the polynomials of the higher degree that cannot be calculated by using the analytical approach without adding additional boundary conditions other than displacement, velocity and acceleration in characteristic positions. The degree of the polynomial from which the optimal value of jerk using the optimization algorithm can be obtained was determined, as well as the degree after which the results of the optimization give worse values of the jerk.

An improved transient search optimization algorithm for building energy optimization and hybrid energy sizing applications

In this paper, a new algorithm named the improved transient search optimization algorithm (ITSOA) is utilized to solve classical test functions, optimize the consumption of building energy, and optimize hybrid energy system production. The conventional TSOA draws inspiration from the fleeting behavior of electrical circuits with energy storage components. Rosenbrock’s direct rotation technique is used to improve the traditional TSOA performance against exploration and exploitation unbalance. First, the ITSOA performance is investigated in solving 23 classical benchmark functions, and the outcomes have shown the superior capability of the recommended algorithm in comparison with the conventional TSOA, DMO, SHO, GA, MRFO, and PSO methods. Also, the ITSOA proficiency is verified in solving the building energy optimization (BEO) problem for minimizing the energy usage of two simple and detailed buildings. The optimization results showed that the optimized solutions of ITSOA in single and multi-objective optimizations compared to conventional TSOA, DMO, SHO, GA, MRFO, and PSO obtained a lower value of the cost function. Also, the superiority of ITSOA has been confirmed to solve the BEO compared to previous methods. Moreover, the multi-objective optimization results have shown that ITSOA is able to determine the ultimate solution among the Pareto front set based on the fuzzy decision-making approach and building energy utilization decisions.