Golden Search Research Articles

Random-guided optimizer: a metaheuristic that shifts random search to guided search through iteration

This study offers a new swarm-based metaheuristic: random-guided optimizer (RGO). RGO has novel mechanics in shifting the random motion into a guided motion strategy during the iteration. In RGO, the iteration is divided into three equal size phases. In the first phase, the unit walks randomly inside the search space to tackle the local optimal problem earlier. In the second phase, each unit uses a unit selected randomly among the population as a reference in conducting the guided motion. In the third phase, each unit conducts guided motion toward or surpasses the best unit. Through simulation, RGO successfully finds the acceptable solution for 23 benchmark functions. Moreover, RGO successfully finds the global optimal solution for four functions: Branin, Goldstein-Price, Six Hump Camel, and Schwefel 2.22. RGO also outperforms slime mold algorithm (SMA), pelican optimization algorithm (POA), golden search optimizer (GSO), and northern goshawk optimizer (NGO) in solving 12, 20, 12, and 1 function consecutively. In the future, improvement can be made by transforming RGO into solid multiple-phase strategy without losing its identity as a metaheuristic with multiple strategy in every iteration.

Enhancing natural disaster analysis and waste classification: a novel VGG-FL approach.

The study of natural disasters is a crucial field that involves analyzing the occurrence, impact, and aftermath of various natural hazards that can cause significant harm to communities and the environment. Efficient waste management and environmental protection require proper classification of waste. Analyzing natural disasters and categorizing waste can be a time-consuming task, and conventional methods often struggle with it. However, a new approach called Visual Geometry Group with Federated Learning (VGG-FL) has been introduced to address these challenges. This methodology uses the golden search optimization (GSO) algorithm for feature selection and leverages VGG with federated learning for feature extraction and classification. To test the effectiveness of this method, a disaster image dataset was used to train the VGG-FL model. The results showed that the VGG-FL model attained exceptional accuracy in discerning and categorizing various disaster scenarios. The waste classification dataset simultaneously trains the VGG-FL model to categorize waste based on its characteristics and potential hazards. To measure the model's performance, several evaluation metrics such as accuracy, specificity, precision, F1-score, and recall are utilized to assess the effectiveness of the proposed VGG-FL method. These results are then compared with existing methodologies. The VGG-FL method performs exceptionally well, achieving 98.52% accuracy, 97.48% precision, 97.83% recall, 97.58% F1-score, and 97.12% specificity. These experimental findings demonstrate the efficacy of the VGG-FL method in analyzing natural disasters and classifying waste materials.

Performance evaluation of Non‐Orthogonal multiple access system based on outage probability and power consumption

SummaryIn this growing world, the field of communication is developing quickly. The research community's attention is shifting to the design of networks beyond fifth generation (5G) and the sixth generation (6G) due to the rapid spread of the 5G mobile networks. Due to that, there will be a lot of traffic in the area, which will interfere with the channels and maybe produce noise. Nonorthogonal multiple access (NOMA) contributes to streamlined communication and a decrease in the possibility of interference. The communication will be encoded and decoded from transmitter to receiver utilizing the superposition coding (SC) and successive interference cancelation (SIC) approaches, ensuring the message's secrecy. This paper considers a simultaneous wireless information and power transfer (SWIPT)‐based cooperative NOMA system, where the nearby user to the source node acts as a relay to assist the far user. To further enhance the system performance, an improved golden search optimization (IGSO) is used to determine the best power allocation and power splitting coefficients by decreasing the outage probability. The existing models are compared with the proposed model in the results section regarding outage probability and throughput, where the proposed model outperformed all other existing models.

Optimization of off-grid renewable energy systems using a hybrid version of golden search algorithm

Currently, the development of HRESs (hybrid renewable energy systems) in remote areas is of great importance and popularity. However, measuring and optimizing the capacity of these systems faces a difficult challenge. Multiple works had been reported in the literature to optimize such systems, all of which aim to achieve an optimal configuration with minimum annual net cost. Therefore, the significance of providing off-grid electrification to remote areas through HRESs can be highlighted as a crucial case for sustainable growth. Accordingly, the study proposes a modified metaheuristic approach, known as the Hybrid Golden Search Algorithm (HGSA), for long-term application planning and optimization of the off-grid HRES. The aim of this algorithm is to minimize the amount of net cost which is used annually; to reduce the probability of power supply interruption. In order to assess the effectiveness of the proposed algorithm, a simulation study over a long period on a remote area was conducted. From the results, increasing the reliability level from 1 % to 3 % causes a decrease in the total net annual cost by around 7.3 % under the proposed HGSA and also results in a reduction in component size (around 6 % and 21 % reductions for the wind turbine area and storage tanks, respectively). Further, the HGSA technique obtains the lowest value of fitness function for the hybrid system at a reliability level of 3 %, which is 31,539,810$. This result demonstrates that the efficiency of HGSA outperforms Fuzzy Logic and Optimization, Artificial Bee Colony (ABC), and GSA techniques. Based on this, the proposed HGSA could lead to more promising results than the other comparative algorithms. Hence, the proposed HGSA can be a valuable tool for long-term application planning and optimization of off-grid HRES, which can contribute significantly to achieving sustainable development goals.

Enhancing Vocal Performance using Variational Onsager Neural Network and Optimized with Golden Search Optimization Algorithm

ABSTRACT The Singing Muscle Ability Training System (SMATS) represents a cutting-edge technological solution dedicated to honing an individual’s vocal prowess. By employing a sophisticated combination of techniques, exercises, and methodologies, this system strategically targets and cultivates the muscle groups integral to singing. In its innovative approach, SMATS integrates Artificial Intelligence (AI) to elevate vocal capabilities further. The proposed SMATS-AI-VONN-GSOA stands out by incorporating the Variational Onsager Neural Network (VONN) alongside the Golden Search Optimization Algorithm (GSOA). This amalgamation tailors training routines based on the user’s progress and preferences, emphasizing the development of muscle memory and control for enhanced vocal performance. Noteworthy is the system’s capacity to analyze data through AI, enabling the creation of personalized training plans. In comparative evaluations, the SMATS-AI-VONN-GSOA method demonstrates a significant performance boost, surpassing existing methods like SMATS-AI-CNN, SMATS-AI-DNN, and SMATS-AI-DNN-HSV by 26.78%, 29.55%, and 21.41% in accuracy, respectively.

Taylor Shepherd Golden Optimization-Enabled ResUNet for Forest Change Detection Using Satellite Images

The pivotal task of remote sensing image (RSI) processing change detection (CD) highly aims to accurately detect changes in land cover based on multi-temporal images. With the advent of deep learning, technology has delivered remarkable results in the last years in the detection of variations in forest land cover data. Some of the conventional CD techniques are weak and are highly susceptible to errors and can result even in inaccurate outcomes. Thus, certain techniques are not desirable for real-time CD applications. To abridge this gap, this research introduces an innovative work for forest CD utilizing the proposed Taylor Shepherd Golden Optimization_ResUNet (TSGO_ResUNet) and Fuzzy Neural network (Fuzzy NN) for segment mapping. Here, the segmentation process is accomplished using ResUNet to determine the exact boundary or shape of each object for every pixel in the image. Furthermore, TSGO is achieved by consolidating Taylor Shuffled Shepherd Optimization (TSSO) with Golden Search Optimization (GSO). In addition, the devised TSGO_ResUNet + Fuzzy NN has gained maximum accuracy and kappa coefficient of 0.952 and 0.785, and minimum error rate of 0.051.

Adam golden search optimization enabled DCNN for classification of breast cancer using histopathological image

Breast Cancer (BC) is a killing disorder, every year it kills millions of human beings. Early diagnosis is the only way to mitigate the mortality rate. Among all kinds of screening methods, medical imaging is an essential method for screening BC. Existing medical imaging alters the tissue structure and cell morphology. To overcome these limitations, histopathology image is used because it can support the decision of pathologists about the closeness or the non-appearance of a disease, as well as it can help in infection development estimation. Hence, this research develops an efficient method for BC classification using the proposed Adam Golden Search Optimization-based Deep Convolutional Neural Network (AGSO-DCNN). Initially, Gaussian filter-enabled pre-processing is utilized for mitigating the noises composed in the input images. Afterwards, k-means clustering is used to feed the input images into the segmentation phase to reduce the complexity of the image. Then, to extract features like shape features, statistical features, Local Vector Patterns (LVP), and Pyramid Histogram of Oriented Gradients (PHOG) feature extraction is performed. Thereafter, the obtained features are forwarded to the multi-grade BC classification stage, where DCNN is employed for classifying the image into six categories, such as apoptosis, tubule, mitosis, non-tubule, tumour nuclei, and non-tumor nuclei. DCNN is trained by the formulated AGSO mechanism, which is obtained by incorporating the Adam Optimizer and Golden Search Optimization (GSO) algorithm. Moreover, the AGSO-based DCNN technique achieved better accuracy, TPR and TNR with the values of 97.90%, 98.00%, and 98.30%, respectively.

Swarm flip-crossover algorithm: a new swarm-based metaheuristic enriched with a crossover strategy

A new swarm-based metaheuristic that is also enriched with the crossover technique called swarm flip-crossover algorithm (SFCA) is introduced in this work. SFCA uses swarm intelligence as its primary technique and the crossover as its secondary one. It consists of three searches in every iteration. The swarm member walks toward the best member as the first search. The central point of the swarm becomes the target in the second search. There are two walks in the second search. The first walk is getting closer to the target, while the second is avoiding the target. The better result between these two walks becomes the candidate for the replacement. In the third search, the swarm member performs balance arithmetic crossover with the central point of the space or jumps to the opposite location within the area (flipping). The assessment is taken by confronting SFCA with five new metaheuristics: slime mold algorithm (SMA), golden search optimization (GSO), osprey optimization algorithm (OOA), coati optimization algorithm (COA), and walrus optimization algorithm (WaOA) in handling the set of 23 functions. The result shows that SFCA performs consecutively better than SMA, GSO, OOA, COA, and WaOA in 20, 23, 17, 17, and 17 functions.

Golden Search Optimization based adaptive and diagonal kernel convolution neural network for disease prediction and securing IoT data in cloud

Due to their open architecture, Internet of Things (IoT) devices have limited user access and are susceptible to various security vulnerabilities. When healthcare data is stored in the cloud, it often becomes vulnerable to side-channel attacks, Denial of Service (DoS) attacks, and eavesdropping. Consequently, there is a need for an efficient access control scheme to enhance the data-sharing processes of IoT devices. Different machine learning and rule-based decision support systems have been proposed to improve the security of cloud-based data storage. However, these models have exhibited increased false alarm rates, complex processing, and longer response times. To address this research gap, we propose a Fuzzy TOPSIS model based on Golden Search Optimization (GSO) to enhance the security of patient information at both IoT and cloud server levels. Additionally, we introduce a revolutionary Diagonal Kernel Convolution Neural Network with an adaptable kernel (DKCNN-AK) to enhance the accuracy of lung disease diagnosis. The GSO algorithm categorizes the security ranks determined by the fuzzy TOPSIS model into the best and worst security principles. We conducted simulations using the COVID-19 X-Ray detection dataset, the lung cancer dataset, and the data collected from IoT devices for analysis.The experiments were conducted to assess various aspects, including computational time, security level analysis, information leakage, privacy level, prediction accuracy, sensitivity, specificity, and confusion matrix. The improved prediction accuracy and security provided by our proposed approach demonstrate its efficiency

Best-worst northern goshawk optimizer: a new stochastic optimization method

<span lang="EN-US">This study introduces a new metaheuristic method: the best-worst northern goshawk optimizer (BW-NGO). This algorithm is an enhanced version of the northern goshawk optimizer (NGO). Every BW-NGO iteration consists of four phases. First, each agent advances toward the best agent and away from the worst agent. Second, each agent moves relatively to the agent selected at random. Third, each agent conducts a local search. Fourth, each agent traces the space at random. The first three phases are mandatory, while the fourth phase is optional. Simulation is performed to assess the performance of BW-NGO. In this simulation, BW-NGO is confronted with four algorithms: particle swarm optimization (PSO), pelican optimization algorithm (POA), golden search optimizer (GSO), and northern goshawk optimizer (NGO). The result exhibits that BW-NGO discovers an acceptable solution for the 23 benchmark functions. BW-NGO is better than PSO, POA, GSO, and NGO in consecutively optimizing 22, 20, 15, and 11 functions. BW-NGO can discover the global optimal solution for three functions.</span>

A new optimization method for accurate anterior cruciate ligament tear diagnosis using convolutional neural network and modified golden search algorithm

Anterior cruciate ligament (ACL) tear is a sports-related trauma that can have a significant impact on the performance and long-term health of athletes. Timely and accurate identification of such injuries is essential to facilitate effective treatment and recovery. In this research, a new approach for the diagnosis of tears in the anterior cruciate ligament (ACL) is presented through the use of a Convolutional Neural Network (CNN), which is optimized using the Modified Golden Search Algorithm (MGSA). The proposed method is capable of extracting significant features from Magnetic Resonance Imaging (MRI) images of the knee using an optimized CNN architecture. The hybrid golden search algorithm showed a superior accuracy of 99.6% compared to other methods. These findings demonstrate the efficacy of this strategy in optimizing the CNN structure for the provided objective. The findings of the study show that CNN optimized using MGSA model can serve as an effective mechanism for detecting ACL tears in knee MRI images. This has the potential to improve the accuracy and efficiency of ACL injury diagnosis and encourage timely action and excellent rehabilitation results.

Skin cancer classification using improved transfer learning model‐based random forest classifier and golden search optimization

AbstractThe two complex skin cancer type is melanoma and non‐melanoma. However, the classification of skin cancer is not performed accurately due to the requirement of a huge time and the presence of noisy images. The severity level is not detected and this leads to generate serious issues. To overcome these entire issues GSO‐optimized kernel random forest (RF)‐based transfer learning (TL) is proposed to perform the classification of 12 different skin cancer types by using ImageNet and CNN architectures. The kernel decision tree is taken into account by the RF classifier throughout the classification process, and the RF parameters are modified using the GSO algorithm. The proposed algorithm not only effectively enhances the detection results based on previous adaptive thresholding segmentation techniques but also extends the skin cancer detection process. Compared to existing MMCL, SVM, Mask‐RCNN, and CNN methods the proposed technique attained better performance in classification of skin cancer.

Tomato Plant Leaf Disease Segmentation and Multiclass Disease Detection Using Hybrid Optimization Enabled Deep Learning.

Production of crops is increasing day by day in agriculture sectors. The insecurity of food is a main reason of plant disease and is a main global issue that humans face these days. With the design of contemporary environmental agriculture, more focus is devised for yielding the crop and elevating its quality. The occurrence of crops has elevated in years and the kind of disease has become more and more complex. The disease in plants and the pernicious insects are the major risks in agriculture field. Thus, earlier discovery and treatment of this disease are imperative. The major design of Deep Learning (DL) model helped in detecting the plant disease and grants a dynamic tool with accurate results. This paper presents DL-assisted technique for detecting and classifying the tomato disease and used deep batch-normalized eLu Alex Net (DbneAlexnet) for classifying the tomato plant leaves. Initially, tomato plant leaf images are taken as an input from specific dataset represented and it is subjected to preprocessing phase to eliminate unwanted distortions using anisotropic filtering. Then, the segmentation is carried out using U-net, which is trained by Gradient-Golden search optimization (Gradient-GSO) Algorithm and it is incorporation of both Golden search optimization (GSO) and Gradient concept. Thereafter the segmented image is given to image augmentation process, where position augmentation and color augmentation are considered. Finally, the multiclass plant leaf disease is classified using DbneAlexnet and is trained using proposed Gradient Jaya- Golden search optimization (GJ-GSO). Here, the GJ-GSO is devised with the integration of Gradient concept, Jaya algorithm, and GSO algorithm. The proposed GJ-GSO-based DbneAlexnet outperformed highest accuracy of 92.4%, True positive rate (TPR) of 91.9%, True negative rate (TNR) of 92.2% and smallest False Positive Rate (FPR) of 0.078. Hence, the technique with unified segmentation and classification is effectual for identifying the plant disease and the empirical research verifies the benefits of the developed model.

Throughput performance optimization of NOMA-assisted cooperative relay system with realistic impairments

Abstract This work aims to investigate the outage and throughput performance of non-orthogonal multiple access assisted cooperative relay system (CRS-NOMA) considering the realistic impairments caused due to in-phase and quadrature-phase imbalance (IQI), channel estimation errors (CEE), and successive interference cancellation (SIC) errors. More specifically, we investigate a model in which two-phase downlink transmission is carried out in two different modes: (i) CRS-NOMA without direct links and (ii) CRS-NOMA with direct links (CRS-DLNOMA). In CRS-NOMA mode, the source broadcasts a composite NOMA signal to destination users with the assistance of a decode-and-forward (DF) relay. In contrast, in CRS-DLNOMA, direct and cooperative links are available for transmission. We derive the analytical expressions of outage probability and throughput for both the NOMA destinations to evaluate the system performance of both CRS-NOMA and CRS-DLNOMA modes of transmission. Furthermore, numerical simulations also study and validate the influence of IQI, CEE, and SIC errors on the outage and throughput performance. The simulation results verify that realistic impairments degrade the system performance, but the presence of direct link has a positive impact on outage and throughput. Additionally, we use the golden search method to optimize the power allocation factor (PAF) and transmission rate to maximize the throughput at the near user while ensuring the throughput constraint at the far user.

Simultaneous wireless information and power transmission‐based power transfer and energy prediction for efficient communication with golden Taylor sea lion optimization in wireless sensor network

SummaryWireless Sensor Network (WSN) is an emerging lower cost and resourceful solution, which enables controlled observation of the environment. The high amount of energy is required in wireless networks during the transmission of data. Here, Golden Ant Lion Whale Optimization (GALWO) and Golden Taylor Sea Lion Optimization (GTSLnO) techniques are presented for cluster head (CH) selection and prediction of neighbor nodes' age. The six stages performed in this work are setup, steady‐state, prediction, power transfer, communication or route discovery, and route maintenance stages. In the setup level, CH selection is carried out by GALWO, which is the combination of Ant Lion Whale Optimization (ALWO) with Golden Search Optimization (GSO). Moreover, ALWO is an integration of Ant Lion Optimizer (ALO) with the Whale Optimization Algorithm (WOA). In the steady state, the distance, energy, delay, throughput, and trust update are considered as objective functions. In the prediction stage, the Deep Convolutional Neural Network (Deep CNN) is utilized for age prediction of neighbor nodes, wherein Deep CNN is tuned by employing GTSLnO. The GTSLnO is an incorporation of GSO and Taylor series with Sea Lion Optimization (SLnO). Then, the power transfer stage is done utilizing simultaneous wireless information and power transmission (SWIPT). Thereafter, the communication/route discovery stage is conducted for path selection through neighbor node, and lastly, the route maintenance stage is carried out. The GTSLnO–Deep CNN achieved a minimal delay of 0.089 s, maximal residual energy, throughput, and trust of 0.500 J, 98,843 kbps, and 0.452 for DoS attack.

Chronological golden search optimization-based deep learning for classification of heartbeat using ECG signal

ABSTRACT Electrocardiogram (ECG) is the simplest test, used for checking the heart’s rhythm and measuring the heart’s electrical activity. This method is the most probably used technique for identifying heart diseases because of its non-invasive nature. The patient’s status is identified by handling the irregularities in the ECG signals. For computation in the existing system, they use signal processing methods which result in time consumption in real time. In existing system, it is hard to predict the detection of heartbeat early and accurately. In this work, the heartbeat is classified using ECG signal by proposed Chronological Golden Search Optimisation (CGSO)-based Deep Learning (DL). Here, the CGSO algorithm is developed by hybridising the Chronological concept with the Golden search optimisation (GSO) algorithm, which is utilised to train SqueezeNet. After the acquisition of ECG signals, pre-processing is done by a median filter. Then, the features are extracted, and this tends to the data augmentation process. Finally, the heartbeat is classified using SqueezeNet-enabled CGSO. The performance is analysed using ECG heartbeat categorisation data set. The proposed model obtained a specificity of 93.5%, sensitivity of 93.1% and precision of 89.8%. From the results, it is known that the proposed system offers more accurate results.

Securing Multiuser Communications via an Energy Harvesting Node: Jammer or Relay?

In this paper, we study the secure communication problem of a wireless multiuser network consisting of a base station (BS), multiple users, and an energy harvesting (EH) node in the face of an eavesdropper. We focus on the question that which role the EH node plays, as a friendly jammer or as a cooperative decode-and-forward (DF) relay. Accordingly, we propose two EH node aided multiuser scheduling schemes to help the secure communications between BS and multiple users, i.e., the EH jammer aided transmission (EHJT) scheme where the EH node acts as a friendly jammer and the EH relay aided transmission (EHRT) scheme where the EH node acts as a cooperative DF relay. For comparison purposes, the direct transmission (DT) scheme is also presented. We analyze the security-reliability trade-off (SRT) performance of the proposed EHJT and EHRT schemes as well as the DT scheme in terms of deriving their exact and asymptotic expressions of outage and intercept probabilities. Additionally, we propose an iterative algorithm based on the golden search to determine the optimal time allocation factor (OTAF) for the sake of minimizing the sum of outage and intercept probabilities for the proposed EHJT and EHRT schemes. Numerical results show that the EH node can improve the SRT performance of the multiuser network more significantly as a friendly jammer than as a cooperative DF relay. It is also shown that the best SRT performance of the EHJT and EHRT schemes can be achieved by adjusting the signal-to-noise ratio (SNR) and time allocation factor.

Development of Slime Mold Optimizer with Application for Tuning Cascaded PD-PI Controller to Enhance Frequency Stability in Power Systems

Multi-area power systems (MAPSs) are highly complex non-linear systems facing a fundamental issue in real-world engineering problems called frequency stability problems (FSP). This paper develops an enhanced slime mold optimization algorithm (ESMOA) to optimize the tuning parameters for a cascaded proportional derivative-proportional integral (PD-PI) controller. The novel ESMOA proposal includes a new system that combines basic SMO, chaotic dynamics, and an elite group. The motion update incorporates the chaotic technique, and the exploitation procedure is enhanced by searching for a select group rather than merely the best solution overall. The proposed cascaded PD-PI controller based on the ESMOA is employed for solving the FSP in MAPSs with two area non-reheat thermal systems to keep the balance between the electrical power load and the generation and provide power system security, reliability, and quality. The proposed cascaded PD-PI controller based on the ESMOA is evaluated using time domain simulation to minimize the integral time-multiplied absolute error (ITAE). It is evaluated in four different test situations with various sets of perturbations. For tuning the cascaded PD-PI controller, the proposed ESMOA is compared to the golden search optimizer (GSO) and circle optimizer (CO), where the proposed ESMOA provides the best performance. Furthermore, the findings of the proposed cascaded PD-PI controller based on the ESMOA outperform previous published PID and PI controllers adjusted using numerous contemporary techniques.

Optimal Allocation of PV-STATCOM Devices in Distribution Systems for Energy Losses Minimization and Voltage Profile Improvement via Hunter-Prey-Based Algorithm

Incorporating photovoltaic (PV) inverters in power distribution systems via static synchronous compensators (PV-STATCOM) during the nighttime has lately been described as a solution to improve network performance. Hunter prey optimization (HPO) is introduced in this study for efficient PV-STATCOM device allocation in distribution systems. HPO generates numerous scenarios for how animals could act when hunting, some of which have been expanded into stochastic optimization. The PV-STATCOM device allocation issue in distribution networks is structured to simultaneously minimize the electrical energy losses and improve the voltage profile while accounting for variable 24 h loadings. The impacts of varying the number of installed PV-STATCOM devices are investigated in distribution systems. It is tested on two IEEE 33-node and 69-node distribution networks. The effectiveness of the proposed HPO is demonstrated in comparison to the differential evolution (DE) algorithm, particle swarm optimization (PSO), artificial rabbits algorithm (ARA), and golden search optimizer (GSO). The simulation results demonstrate the efficiency of the proposed HPO in adequately allocating the PV-STATCOM devices in distribution systems. For the IEEE 33-node distribution network, the energy losses are considerably decreased by 57.77%, and the voltages variance sum is significantly reduced by 42.84%. The energy losses in the IEEE 69-node distribution network decreased by 57.89%, while voltage variations are reduced by 44.69%. Additionally, the suggested HPO is highly consistent than the DE, PSO, ARA, and GSO. Furthermore, throughout the day, the voltage profile at all distribution nodes surpasses the minimum requirement of 95%.

Distribution Network Reconfiguration Based on Hybrid Golden Flower Algorithm for Smart Cities Evolution

Power losses (PL) are one of the most—if not the most—vital concerns in power distribution networks (DN). With respect to sustainability, distribution network reconfiguration (DNR) is an effective course of action to minimize power losses. However, the optimal DNR is usually a non-convex optimization process that necessitates the employment of powerful global optimization methods. This paper proposes a novel hybrid metaheuristic optimization (MO) method called the chaotic golden flower algorithm (CGFA) for PL minimization. As the name implies, the proposed method combines the golden search method with the flower pollination algorithm to multiply their benefits, guarantee the best solution, and reduce convergence time. The performance of the algorithm has been evaluated under different test systems, including the IEEE 33-bus, IEEE 69-bus, and IEEE 119-bus systems and the smart city (SC) network, each of which includes distributed-generation (DG) units and energy storage systems (ESS). In addition, the locations of tie-switches in the DN, which used to be considered as given information in previous studies, are assumed to be variable, and a branch-exchange adaption is included in the reconfiguration process. Furthermore, uncertainty analysis, such as bus and/or line fault conditions, are studied, and the performance of the proposed method is compared with other pioneering MO algorithms with minimal standard deviations ranging from 0.0012 to 0.0101. The case study of SC is considered and the obtained simulation results show the superiority of the algorithm in finding higher PL reduction under different scenarios, with the lowest standard deviations ranging from 0.012 to 0.0432.