Search Optimization Algorithm Research Articles

Development of AVR controller performance using exponential distribution and transit search optimization techniques

This paper attempts to obtain the optimal solution to enhance the performance of the Automatic Voltage Regulator (AVR) Controller, as it is an essential tool to control the synchronous generator output voltage. The controller improves AVR system stability and response time; moreover, it is demonstrated that the Proportional Integral Derivative (PID) controller achieves the goal by applying two artificial intelligence techniques to design the optimal values of the Automatic Voltage Regulator (AVR) PID controller for a single area model. The first is the Exponential Distribution Optimization Algorithm (EDO), and the second is the Transit Search Optimization Algorithm (TS). EDO and TS are used to determine the best PID controller parameters and have also recently been developed in the breadth of optimization problems and associated computational complexities field. The objective function, Integrated Square Error (ISE), minimizes the error voltage for improved stability and response. The outcomes are compared to various optimization techniques to prove the validation of the two proposed methods. The results show that the EDO and TS proved their superiority through their stability level to the AVR system and their steady-state error improvement. Moreover, the dominant effect of damping frequency decreases the oscillation and the reduced maximum overshoot that protects the loads from being subjected to non-permissible over-voltage levels. Finally, a robustness test is applied to the two proposed optimization methods to prove their reliability and effectiveness.

CSADF: ingesting cuckoo search optimization algorithm enabled with fitness function for effective model transformation pertaining to ADF

CSADF: ingesting cuckoo search optimization algorithm enabled with fitness function for effective model transformation pertaining to ADF

PEMFC model identification using a squeezenet developed by modified transient search optimization algorithm

Proton Exchange Membrane Fuel Cells (PEMFCs) are promising sources of clean and renewable energy, but their performance and efficiency depend on an accurate modeling and identification of their system parameters. However, existing methods for PEMFC modeling suffer from drawbacks, such as slow convergence, high computational cost, and low accuracy. To address these challenges, this research work proposes an enhanced approach that combines a modified version of the SqueezeNet model, a deep learning architecture that reduces the number of parameters and computations, and a new optimization algorithm called the Modified Transient Search Optimization (MTSO) Algorithm, which improves the exploration and exploitation abilities of the search process. The proposed approach is applied to model the output voltage of the PEMFC under different operating conditions, and the results are compared with empirical data and two other state-of-the-art methods: Gated Recurrent Unit and Improved Manta Ray Foraging Optimization (GRU/IMRFO) and Grey Neural Network Model integrated with Particle Swarm Optimization (GNNM/PSO). The comparison shows that the proposed approach achieves the lowest Sum of Squared Errors (SSE) and the highest accuracy, demonstrating its superiority and effectiveness in PEMFC modeling. The proposed approach can facilitate the optimal design, control, and monitoring of PEMFC systems in various applications.

EOSA-Net: A deep learning framework for enhanced multi-class skin cancer classification using optimized convolutional neural networks

Most cancer diagnoses are inevitably fatal, and an increasing number of genetic and metabolic abnormalities are being identified by experts as the disease’s cause. Every organ in the body is susceptible to the invasion and spread of cancerous cells, which pose a major threat to health. So, considering the frequency of skin cancer is rising quickly, it is recommended to undergo a screening. The multi-class approach of deep learning (DL) may provide more comprehensive data and insights into the underlying causes and risk factors of all different skin cancer classes, which can aid in developing fresh therapies or preventative measures. DL is one of the best methods for rapidly and reliably detecting skin cancer. The ISIC 2018 and 2019 public datasets were used in the present research to identify and detect multi-class skin cancer by employing an Improved canny edge for detection and an optimized deep learning technique convolution neural network (CNN) for classification. To enhance the detection of the affected area from the input image, the pre-processing stage is conducted using an improved canny edge detector. The control parameters for the canny edge detector are selected using the coronavirus optimization algorithm (CVOA), and the proposed model has three discrete hidden layers, each with a corresponding channel size (16, 32, and 64). A learning version of the Ebola Optimization Search Algorithm (EOSA) with a learning rate of 0.001 is included in the suggested model. Both the mathematical and propagation models were modified to create the new metaheuristic technique. The proposed EOSA algorithm has been brought towards as a robust mechanism for achieving a balance between exploration and exploitation to determine the optimal solution to problems. With an accuracy of 99%, the proposed model performs the best when classifying the skin lesions in the ISIC dataset into eight categories. The results obtained are superior to the present system of skin cancer categorization.

Joint optimization of load balancing and resource allocation in cloud environment using optimal container management strategy

SummaryDue to the high performance of cloud computing‐based microservices, a wide range of industries and fields rely on them. In a containerized cloud, traditional resource management strategies are typically used to allocate and migrate virtual machines. A major problem for cloud service providers is resource allocation for containers, which directly affects system performance and resource consumption. In this paper, we propose a joint optimization of load balancing and resource allocation in the cloud using an optimal container management strategy. We aim to enhance scheduling efficiency and reduce costs by improving the container's schedule requested digitally by users. An improved backtracking search optimization (IBSO) algorithm is used to allocate resources between end‐users/IoT devices and the cloud under the consideration of service‐level agreements. Mechanic quantum recurrent neural networks (MQ‐RNNs) are designed to allocate, consolidate, and migrate containers in cloud environments. The various simulation measures used to validate the proposed strategy are energy consumption, number of active servers, number of interruptions, total cost, runtime, and statistical measures.

Kötü Amaçlı Yazılım Algılaması için Hiperparametre Ayarlama ve Özellik Seçim Yöntemleri

Smartphones have started to take an essential place in every aspect of our lives with the developing technology. All kinds of transactions, from daily routine work to business meetings, payments, and personal transactions, started to be done via smartphones. Therefore, there is a significant amount of very important user information stored in these devices which makes them a target for malware developers. For these reasons, machine learning (ML) methods have been used to detect malicious software on android devices quickly and reliably. In this study, a machine learning-based Android malware detection system has been developed, optimized, and tested. To this end, firstly, the data in the dataset has been balanced with 3 different methods namely SMOTE, SMOTETomek and ClusterCentroids. Afterward, the obtained results have been tried to be optimized by using different feature selection approaches including mRMR, Mutual Information, Select From Model, and Select k Best. Finally, the most two successful methods from the five tested ML algorithms (i.e. RF, SVM, LR, XGBoost, and ETC) have been tuned using GridSearch, Random Search, and Bayesian Optimization algorithms in order to investigate the effects of hyperparameter tuning on the performance of ML algorithms.

Optimal overcurrent relay coordination: Balancing costs, time performance and generator placement in fault current limiter optimization

AbstractThe integration of synchronous generators (SG) to the grid will present serious problems in some instances. The increase in fault current level is one of the most crucial issues, which causes miscoordination between directional over current relays (DOCRs), in addition to putting excessive pressure on the circuit breakers. This research uses the Fault Current Limiters (FCL) to restore coordination, without changing the settings of DOCRs, by reducing the level of fault current in the grid. The main goal of this research is to find the optimal size, location, and number of FCLs at the minimum cost and with the least operation time for the relays, using Jellyfish Search Optimisation (JSO) algorithm. This can be achieved through three different approaches. The first one is optimizing the location, number, and impedance size of the FCL by minimising a cost function. The second approach involves optimizing the backup relay operation time. A combination of the two previous approaches is used as the third approach. The outstanding feature of this research is that it considers the optimal location of DG and FCL while optimizing the operation times of DOCRs, which is a new framework.

Fuzzy evaluation model for physical education teaching methods in colleges and universities using artificial intelligence.

The evaluation of Physical Education Teaching Methods in Colleges and Universities faces two main challenges: an excess of evaluating elements and a lack of assessment framework. Hence, the research proposes the multi-feature fuzzy evaluation model based on artificial intelligence to streamline the evaluation process and provide an efficient framework for accessing teaching methods. The framework integrates natural/human language using fuzzy instructions considering three evaluation perspectives, including the management stage, instructors, and students and employs the enhanced cuckoo search optimization algorithm. After the teaching expert has determined each parameter's ratings, they are fed into the improved cuckoo search algorithm and solved using an unbiased function to assess the assessment's final result. It incorporates the students' mobility mechanism and movement vector deconstruction designed based on functional criteria. A system for evaluating the quality of instruction has been developed utilizing the proposed model with enhanced cuckoo search optimization. The results indicate that the proposed algorithm has achieved the highest scores across multiple assessment categories, average skill performances of 97.01%, learning progress of 87.36%, physical fitness of 93.49%, participation rate of 95.04%, student satisfaction of 95.49%, and physical education of 96.8% teaching efficiency. The usefulness of the proposed framework in enhancing physical education teaching methods has been demonstrated by comparing the results with traditional methods. It contributes to advancing pedagogical practices in the field.

Sensor placement algorithm for faults detection in electrical secondary distribution network using dynamic programming method: focusing on dynamic change and expansion of the network configurations

Modern power grids are developing toward smartness through the use of sensors in gathering data for situation awareness, visibility, and fault detection. In most developing countries, fault detection in the electrical secondary distribution network (SDN) is very challenging due to the lack of automated systems for network monitoring. Systems for monitoring faults require sensor placement on each node, which is not economically feasible. Hence, optimal placement algorithms are required to ensure that the network is observable with few sensors possible. The existing sensor placement methods based on mathematical and heuristic approaches are efficient for transmission and primary distribution networks which are mostly static in size and layout. Such methods may not be efficient in SDN which is dynamic in size and have a relatively large number of nodes. This study proposes an enhanced dynamic programming method for sensor placement to enhance fault detection in SDN. The proposed algorithm employs the depth search concepts and the parent–children relationship between nodes to determine sensor types and locations considering the optimal cost. The proposed algorithm was compared with other methods including particle swarm optimization, genetic algorithm, and chaotic crow search algorithm using different network configurations. The results revealed that the proposed algorithm suggested the minimum number of sensors and shortest convergence time of 1.27 min. The results also revealed that, on network expansion, maintaining the location of the existing sensors is more cost-effective by 20% than reallocating the existing sensors. Furthermore, the results revealed that an average of 30% of nodes, need sensors to observe the entire network, hence cost optimization.

Performance improvement for the CMOS rail-to-rail amplifier via APSO-based design and SNN’s training

This paper proposes an adaptive Particle Swarm Optimization(APSO) global search algorithm and Spiking Neural Network(SNN) surrogate model-based optimization design method for analog integrated circuits performance enhancement. The initial design parameters are calculated by numerical computation. The global design space is extended and explored using APSO in the main loop, with constraint testing ensuring the transistor works in the right conditions. A Spiking Neural Network (SNN) model aids in predicting circuit performance during local searches, in which training data for the local design space is generated through parallel SPICE simulations. The optimization framework is applied to rail-to-rail amplifiers of two topologies. The results show that the proposed method accelerates the optimization process, achieving up to a 35.4x speed enhancement compared to Particle Swarm Optimization(PSO) based on the SPICE simulation. Moreover, Compared with traditional approaches based on multiobjective optimization, the proposed method provides an advantage in performance improvement and is valid for different CMOS processes.

Jellyfish Search Optimized Gated Recurrent Unit Based FOPID/PID for Enhanced Automatic Voltage Regulator

The most essential mechanism for regulation in power systems is the Automatic Voltage Regulator (AVR) device, which maintains generator terminal voltage within predefined parameters. This research paper introduces an innovative approach to enhance AVR system performance through the ideal setting for a fractional-order PID (FOPID) controller that leverages Bidirectional Gate Recurrent Unit (BiGRU)-based deep learning techniques in conjunction with the Jellyfish Search Optimization (JSO) algorithm. To enhance flexibility and precision in control, the conventional proportional integral derivative (PID) controller is replaced with a FOPID controller, whose parameters are optimized by means of the JSO algorithm. The effectiveness of the proposed Jellyfish Search Optimization-based most advanced controllers (JSO-FOPID/PID) is compared with the existing FOPID controller implemented using the Seagull Optimization Algorithm (SOA-FOPID). Comprehensive analyses, including transient time, robustness, stability, and convergence analysis, are conducted subject to applying both stable and alternating load situations to validate the proposed approach. Experimental validation is performed using the MATLAB R2022a application with FOMCON tools and a laboratory prototype for testing. The simulations and experiments demonstrate that the projected AVR consistently delivers optimum dynamic responses and enhances overall stability when compared to existing methods, underscoring its efficacy in voltage regulation and power system control.

Research on Automatic Scoring Method of Intelligent Translation System Based on TSO Optimized LSTM Networks

INTRODCTION: The study of automatic marking methods in the Department of Language Translation is conducive to the fairness and rationality of marking by examining the comprehensive level of the students' language, as well as sharing the objectivity and pressure of the marking teachers in marking the scripts.OBJECTIVES: Aiming at the current automatic scoring methods of translation systems, which have the problems of not considering the global nature of influence features and low precision.METHODS: This paper proposes an automatic scoring method for translation system based on intelligent optimization algorithm to improve the deep network. First, by analyzing the language translation scoring problem, selecting the key scoring influencing factors and analyzing the correlation and principal components; then, improving the long and short-term memory network through the triangle search optimization algorithm and constructing the automatic scoring model of the translation system; finally, the high efficiency of the proposed method is verified through the analysis of simulation experiments.RESULTS: The proposed method is effective and improves the accuracy of the scoring model.CONCLUSION: solves the problem of inefficient scoring in the automatic scoring method of the translation system.

Inversion of large-scale citrus soil moisture using multi-temporal Sentinel-1 and Landsat-8 data

Soil moisture is a significant variable in agricultural study and precision irrigation decision-making. It determines the soil water availability for plants, directly influencing plant growth, yield and quality. Owing to the variations in regional microclimate, landform difference, soil type and vegetation coverage, the soil moisture has strong spatial-temporal heterogeneity on a large regional scale. Micro-wave remote sensing can be used to invert soil moisture based on the dielectric constant under different weather conditions, while optical remote sensing utilizes spectral characteristics to estimate the physiological and ecological information of vegetation. In this study, two new hybrid models (ACO-RF and SSA-RF) were structured by optimizing the standalone random forest (RF) based on the ant colony optimization algorithm (ACO) and sparrow search algorithm (SSA), and six input combinations based on the multi-temporal Sentinel-1 and Landsat-8 remote sensing data from different sensors (optical, thermal and radar sensors) were used. The standalone RF, ACO-RF, and SSA-RF models with different combinations of inputs were employed to predict the soil moisture at different depths (5 cm, 10 cm, 20 cm, 40 cm) in a large-scale drip-irrigated citrus orchard. The results showed that the ACO-RF and SSA-RF outperformed the standalone RF model in terms of prediction accuracy at a depth of 0–40 cm, with R2 of 0.800–0.921 and 0.504–0.798, RRMSE of 7.214–16.284% and 11.124–22.214%, respectively. In the hybrid model, the ACO-RF model had better prediction accuracy than the SSA-RF model, with R2 of 0.805–0.921 and 0.800–0.911, RRMSE of 7.214–13.244% and 8.274–16.284%, respectively. At depths of 5 cm, 10 cm and 20 cm, the inversion accuracy of the model with microwave inputs was higher than that with multispectral inputs, with R2 of 0.556–0.888 and 0.541–0.886, RRMSE of 9.015–19.544% and 9.124–22.214%, respectively. However, at a depth of 40 cm, the inversion accuracy of the model with multispectral inputs was higher than that with microwave inputs, with R2 of 0.532–0.841 and 0.508–0.831, RRMSE of 9.124–21.021% and 9.142–21.214%, respectively. The model with multispectral, thermal, and microwave inputs exhibited the highest accuracy in predicting soil moisture, with R2 of 0.635–0.921, RRMSE of 7.214−18.564%, respectively. Therefore, the ACO-RF with multisource remote sensing data is recommended to predict the soil moisture in the drip-irrigated citrus orchard. This approach can provide data support for making intelligent irrigation decisions on a large-scale grid land lots.

Enhancing heart disease detection and classification using fuzzy logic and metaheuristic algorithms in a blockchain assisted healthcare environment

Heart disease (HD) is a leading cause of mortality worldwide, emphasizing the need for accurate and efficient detection and classification methods. Recently, Blockchain (BC) provides seamless and secure sharing of heart disease data amongst healthcare providers, specialists, and researchers. This allows collaborative efforts, data exchange, and integration of diverse datasets, leading to a more comprehensive analysis and accurate detection of heart diseases. BC provides a decentralized and tamper-proof platform for storing sensitive patient data related to heart disease. This ensures the integrity and security of the data, reducing the risk of unauthorized access or data manipulation. Therefore, this study presents a new blockchain-assisted heart disease detection and classification model with feature selection with optimal fuzzy logic (BHDDC-FSOFL) technique. The presented BHDDC-FSOFL technique uses BC technology to store healthcare data securely. In addition, the disease detection module encompasses the design of biogeography teaching and learning-based optimization (BTLBO) algorithm for feature selection (FS) procedure. Moreover, an adaptive neuro-fuzzy inference system (ANFIS) classifier can be exploited for HD detection and classification. Furthermore, the ebola search optimization (ESO) algorithm is used for the parameter tuning of the ANFIS classifier. The integration of ANFIS classifier enables the modeling of uncertainty and imprecision in HD data, while metaheuristic algorithms aid in optimizing the classification process. Additionally, the utilization of BC technology ensures secure and transparent storage and sharing of healthcare data. To demonstrate the enhanced HD classification results of the BHDDC-FSOFL technique, a detailed experimental analysis was made on the HD dataset. The extensive result analysis pointed out the improved performance of the BHDDC-FSOFL technique compared to recent approaches in terms of different measures. Therefore, the proposed model offers a reliable and privacy-enhancing solution for healthcare providers and patients in a BC-assisted healthcare environment.

Hybrid whale optimized crow search algorithm and multi-SVM classifier for effective system level test case selection

A novel approach to enhance software testing through intelligent test case selection is proposed in this work. The proposed method combines feature extraction, clustering, and a hybrid optimization algorithm to improve testing effectiveness while reducing resource overhead. It employs a context encoder to extract relevant features from software code, enhancing the accuracy of subsequent testing. Through the use of Fuzzy C-means (FCM) clustering, the test cases are classified into groups, streamlining the testing process by identifying similar cases. To optimize feature selection, a Hybrid Whale Optimized Crow Search Algorithm (HWOCSA), which intelligently combines the strengths of both Whale Optimization Algorithm (WOA) and Crow Search Algorithm (CSA) is introduced. This hybrid approach mitigates limitations while maximizing the selection of pertinent features for testing. The ultimate contribution of this work lies in the proposal of a multi-SVM classifier, which refines the test case selection process. Each classifier learns specific problem domains, generating predictions that guide the selection of test cases with unprecedented precision. Experimental results demonstrate that the proposed method achieves remarkable improvements in testing outcomes, including enhanced performance metrics, reduced computation time, and minimized training data requirements. By significantly streamlining the testing process and accurately selecting relevant test cases, this work paves the way for higher quality software updates at a reduced cost.

Power harvesting enhancement from PV array and power quality improvement in grid connected PV interleaved inverter using hybrid LSE-WHO approach

In this manuscript, a hybrid approach is proposed for multi-functional grid connected photovoltaic (PV) interleaved inverter using power quality(PQ) enhancement. The proposed method is the integration of Spherical Evolution Search Algorithm (LSE) and Wild Horse Optimizer (WHO), thus it is called LSE-WHO method. The key objective of the proposed method lessens the DC voltage fluctuation and enhances the PQ. At the grid side, the interleaved inverter is used and it consists of 4 legs and every leg has a power electronic switch and a diode. Because of the structure of interleaved inverter, the shoot-through effect overcomes. The system performance is improved by the utilization of interleaved inverter. The operation of proposed method is divided into 2 parts, like harmonics reduction and power harvesting. The LSE method is used to improve the maximal power of photovoltaic and the WHO method is used to lessen the harmonics distortion and eliminated the DC-link voltage fluctuation by double band hysteresis current controller (DBHCC). The switching losses are low because the DBHCC gives lesser switching frequency. Then, the LSE-WHO method is done in MATLAB, and its performance is compared to the existing methods. From the simulation, it conclude that the LSE-WHO method provides the THD as 2.12% and improves the PQ.

A New Approach for Software Cost Estimation with a Hybrid Tabu Search and Invasive Weed Optimization Algorithms

Due to the ever-increasing progress of software projects and their widespread impact on all industries, models must be designed and implemented to analyze and estimate costs and time. Until now, most of the software cost estimation (SCE) has been based on the analyst’s experiences and similar projects and these models are often inaccurate and inappropriate. The project will not be finished in the specified time and will include additional costs. Algorithmic models such as COCOMO are not very accurate in SCE. They are linear and the appropriate value for effort factors is not considered. On the other hand, artificial intelligence models have made significant progress in the cost estimation modeling of software projects in the past three decades. These models determine the correct value for effort factors through iteration and training, providing a more accurate estimate compared to algorithmic models. This paper employs a hybrid model incorporating the Tabu Search (TS) algorithm and the Invasive Weed Optimization (IWO) algorithm for SCE. IWO algorithm solutions are improved using the TS algorithm. The NASA60, NASA63, NASA93, KEMERER, and MAXWELL datasets are used for the evaluation. The proposed model has been able to reduce the MMRE rate compared to the IWO algorithm and the TS algorithm. The proposed model on the NASA60, NASA63, NASA93, KEMERER, and MAXWELL datasets obtained values of MMRE of 15.43, 17.05, 28.75, 58.43, and 22.46, respectively.

An optimal multipath routing protocol using hybrid gravitational search particle swarm optimization for secure communication

SummaryCommunicating large amounts of data requires an encryption solution that ensures fast and secure data transfer. Unfortunately, it faced privacy and security issues, resulting in higher power consumption, data scarcity, energy, diversity, data reliability, low security, and packet loss in the communication process. The goal of this research is to ensure data transfer while conserving energy and protecting data transmission in WSNs. Therefore, this paper proposes a multipath routing protocol using the HGSPSO algorithm, which is a combination of hybrid gravitational search algorithm (HGSA) and particle swarm optimization (PSO), to securely transmit data from one node to another node under public and private key cryptography. The PSO approach is utilized to resolve the energy hole problem by selecting the cluster head in the sink coverage area. The RSA can be employed for data encryption, data decryption, key generation, and sharing. The metrics like acceleration constants and inertial weights are used for the HGSPSO algorithm. The result of this implementation demonstrated that the scheme had a low energy consumption of 36%, a maximum packet delivery ratio (PDR) of 96%, a higher throughput rate of 88%, and a low packet loss of 39%.

Non-sweep DC component estimation method for a virtual-carrier assisted Kramers-Kronig receiver

The Kramers-Kronig (KK) receiver has attracted much attention in short-range optical interconnection because of its ability to recover the phase of the signal from the intensity information through KK algorithm. In high-speed KK systems, such as virtual-carrier (VC) assisted ones, an alternating current (AC) coupled photo-detector (PD) is preferred due to relaxing the requirements of analog-to-digital converter (ADC) and electronic amplifier by filtering direct current (DC) component. However, the loss of the DC component will cause the KK algorithm to break down, so it is necessary to accurately recover DC value in the digital domain with multiple-sweep. In this paper, we propose what we believe is a novel non-sweep DC component estimation scheme based on optimized digital carrier-to-signal power ratio (OD-CSPR) method, which can accurately estimate the DC component with only 3-4 iterations in the scenario of VC-assisted KK receiver optical transmission. The scheme utilizes the one-dimensional search optimization algorithm based on golden section search and parabolic interpolation without sweeping. The simulation and experimental results of the proposed non-sweep OD-CSPR method show that the DC component can be estimated accurately in a large CSPR range, and the system performance is close to that of the conventional DC-sweep method. Compared with the typical defined digital CSPR (DD-CSPR) based optimization method, the proposed one can realize optical signal-to-noise ratio (OSNR) gains of 0.9 dB in the back-to-back (B2B) and 0.7 dB under 80 km fiber transmission scenarios respectively with a total bit rate of 160Gb/s.

Multi-Objective Stochastic Paint Optimizer for Solving Dynamic Economic Emission Dispatch with Transmission Loss Prediction Using Random Forest Machine Learning Model

Dynamic economic emission dispatch problems are complex optimization tasks in power systems that aim to simultaneously minimize both fuel costs and pollutant emissions while satisfying various system constraints. Traditional methods often involve solving intricate nonlinear load flow equations or employing approximate loss formulas to account for transmission losses. These methods can be computationally expensive and may not accurately represent the actual transmission losses, affecting the overall optimization results. To address these limitations, this study proposes a novel approach that integrates transmission loss prediction into the dynamic economic emission dispatch (DEED) problem. A Random Forest machine learning model was offline-trained to predict transmission losses accurately, eliminating the need for repeated calculations during each iteration of the optimization process. This significantly reduced the computational burden of the algorithm and improved its efficiency. The proposed method utilizes a powerful multi-objective stochastic paint optimizer to solve the highly constrained and complex dynamic economic emission dispatch problem integrated with random forest-based loss prediction. A fuzzy membership-based approach was employed to determine the best compromise Pareto-optimal solution. The proposed algorithm integrated with loss prediction was validated on widely used five and ten-unit power systems with B-loss coefficients. The results obtained using the proposed algorithm were compared with seventeen algorithms available in the literature, demonstrating that the multi-objective stochastic paint optimizer (MOSPO) outperforms most existing algorithms. Notably, for the Institute of Electrical and Electronics Engineers (IEEE) thirty bus system, the proposed algorithm achieves yearly fuel cost savings of USD 37,339.5 and USD 3423.7 compared to the existing group search optimizer algorithm with multiple producers (GSOMP) and multi-objective multi-verse optimization (MOMVO) algorithms.