Standard Particle Swarm Optimization Research Articles

Particle Swarm Optimization Algorithm Using Velocity Pausing and Adaptive Strategy

Particle swarm optimization (PSO) as a swarm intelligence-based optimization algorithm has been widely applied to solve various real-world optimization problems. However, traditional PSO algorithms encounter issues such as premature convergence and an imbalance between global exploration and local exploitation capabilities when dealing with complex optimization tasks. To address these shortcomings, an enhanced PSO algorithm incorporating velocity pausing and adaptive strategies is proposed. By leveraging the search characteristics of velocity pausing and the terminal replacement mechanism, the problem of premature convergence inherent in standard PSO algorithms is mitigated. The algorithm further refines and controls the search space of the particle swarm through time-varying inertia coefficients, symmetric cooperative swarms concepts, and adaptive strategies, balancing global search and local exploitation. The performance of VASPSO was validated on 29 standard functions from Cec2017, comparing it against five PSO variants and seven swarm intelligence algorithms. Experimental results demonstrate that VASPSO exhibits considerable competitiveness when compared with 12 algorithms. The relevant code can be found on our project homepage.

Optimizing low-power task scheduling for multiple users and servers in mobile edge computing by the MUMS framework

In today's increasingly popular Internet of Things (IoT) technology, its energy consumption issue is also becoming increasingly prominent. Currently, the application of Mobile Edge Computing (MEC) in IoT is becoming increasingly important, and scheduling its tasks to save energy is imperative. To address the aforementioned issues, we propose a Multi-User Multi-Server (MUMS) scheduling framework aimed at reducing the energy consumption in MEC. The framework starts with a model definition phase, detailing multi-user multi-server systems through four fundamental models: communication, offloading, energy, and delay. Then, these models are integrated to construct an energy consumption optimization model for MUMS. The final step involves utilizing the proposed L1_PSO (an enhanced version of the standard particle swarm optimization algorithm) to solve the optimization problem. Experimental results demonstrate that, compared to typical scheduling algorithms, the MUMS framework is both reasonable and feasible. Notably, the L1_PSO algorithm reduces energy consumption by 4.6 % compared to Random Assignment and by 2.3 % compared to the conventional Particle Swarm Optimization algorithm.

Design of health information management model for elderly care using an advanced higher-order hybrid clustering algorithm from the perspective of sports and medicine integration.

In the context of integrating sports and medicine domains, the urgent resolution of elderly health supervision requires effective data clustering algorithms. This paper introduces a novel higher-order hybrid clustering algorithm that combines density values and the particle swarm optimization (PSO) algorithm. Initially, the traditional PSO algorithm is enhanced by integrating the Global Evolution Dynamic Model (GEDM) into the Distribution Estimation Algorithm (EDA), constructing a weighted covariance matrix-based GEDM. This adapted PSO algorithm dynamically selects between the Global Evolution Dynamic Model and the standard PSO algorithm to update population information, significantly enhancing convergence speed while mitigating the risk of local optima entrapment. Subsequently, the higher-order hybrid clustering algorithm is formulated based on the density value and the refined PSO algorithm. The PSO clustering algorithm is adopted in the initial clustering phase, culminating in class clusters after a finite number of iterations. These clusters then undergo the application of the density peak search algorithm to identify candidate centroids. The final centroids are determined through a fusion of the initial class clusters and the identified candidate centroids. Results showcase remarkable improvements: achieving 99.13%, 82.22%, and 99.22% for F-measure, recall, and precision on dataset S1, and 75.22%, 64.0%, and 64.4% on dataset CMC. Notably, the proposed algorithm yields a 75.22%, 64.4%, and 64.6% rate on dataset S, significantly surpassing the comparative schemes' performance. Moreover, employing the text vector representation of the LDA topic vector model underscores the efficacy of the higher-order hybrid clustering algorithm in efficiently clustering text information. This innovative approach facilitates swift and accurate clustering of elderly health data from the perspective of sports and medicine integration. It enables the identification of patterns and regularities within the data, facilitating the formulation of personalized health management strategies and addressing latent health concerns among the elderly population.

Reentry trajectory optimization of hypersonic glide vehicle based on improved particle swarm algorithm

The reentry process of hypersonic glide vehicle presents a host of intricate constraint issues. A method for optimizing the reentry trajectory of hypersonic glide vehicle is presented to achieve the shortest possible reentry time. The approach utilizes the improved particle swarm optimization (IPSO) algorithm. Firstly, population initialization is performed using the quasi-oppositional differential evolution (QODE) strategy to promote diversity within the population. Then an adaptive adjustment algorithm is designed for the inertia weight coefficient and learning factor to achieve a balance between global and local search capabilities. Finally, utilizing the IPSO algorithm for optimizing the flight parameters of the reentry trajectory, the reentry time is reduced from 338 seconds to 335 seconds. The simulations results indicate that the IPSO algorithm outperforms both the standard PSO algorithm and genetic algorithm (GA) in terms of optimization performance index.

Colour Image Enhancement Model of Retinal Fundus Image for Diabetic Retinopathy Recognition

Diabetic retinopathy (DR) features are typically identified through ophthalmologist eye examinations, but these images often facechallenges like low contrast, non-uniform illumination, and colour inconsistency, affecting the diagnosis accuracy. Therefore, this studyintroduces two novel techniques to improve image quality. One is applying colour image processing techniques to original retinalfundus images, overcoming existing algorithm limitations. Firstly, a new colour correction algorithm was proposed based on TunedBrightness Controlled Single-Scale Retinex (TBCSSR) named Fuzzy TBCSSR Histogram Matching (fTBCSSRhm) to address the issue of colour inconsistency in the dataset. Secondly, based on hybrid particle swarm optimisation-contrast stretch (HPSOCS), the hybridof TBCSSR and HPSOCS named eTBCSSR-HPSOCS algorithm is introduced to tackle the limitations of the standard Particle SwarmOptimisation (PSO) algorithm in HPSOCS, which is prone to local optima and exhibits low convergence rates. This technique combinesthe L-component of the LAB colour model with an enhanced velocity mechanism in PSO and contrast stretching (lavHPSOCS). Its goalis to fine-tune parameters automatically, reduce over-enhancement, avoid unwanted artefacts, and preserve intricate details. This approach improves optimisation by balancing exploration and exploitation and refining velocity control. The proposed algorithm underwent both qualitative and quantitative evaluations. Tests on 100 retinal fundus images from primary datasets were performed to benchmark the algorithm against three existing approaches. The results show that the qualitative performance of the proposed enhancement is more favourable to ophthalmologist specialists than other images. Quantitatively, eTBCSSR-HPSOCS outperforms others with the lowest mean squared error (MSE) of 42.72859, the highest peak signal-to-noise ratio (PSNR) of 32.768, and entropy of 0.977.

Multi-swarm surrogate model assisted PSO algorithm to minimize distribution network energy losses

The computational analysis of real-world engineering problems often relies on time-consuming simulation calculations. This presents challenges in balancing computational burden and precision when applying traditional metaheuristic algorithms to large-scale complex problems. While there have been numerous surrogate-assisted metaheuristic optimization algorithms proposed, most of them are designed for solving expensive problems within 30–50 dimensions. In this paper, a novel approach called Parallel-multi-swarm Gaussian Process Regression surrogate-based particle swarm optimization (Parallel-MS-GPRS-PSO) is proposed for high-dimensional problems. This approach combines a standard particle swarm optimization (PSO) algorithm, multi-swarm cooperative PSO (MSCPSO), and a GPR surrogate-based PSO (PSO-GPR). By integrating PSO-GPR and MSCPSO, the proposed method aims to effectively explore and exploit the search space while enhancing the global and local performance of the surrogate model. To validate the effectiveness of the proposed algorithm, it is compared with several existing PSO algorithms on seven benchmark functions and an engineering problem. The results illustrate that the Parallel-MS-GPRS-PSO approach outperforms the existing algorithms. Moreover, the method shows promising performance in computationally expensive engineering optimization problems with 288 variables.

Parallel Hybrid Particle Swarm-Grey Wolf Algorithms for Optimal Load-Shedding in An Isolated Network

In distribution networks integrated with distributed generation (DG), disconnection from the main grid reduces the power supply significantly. The power imbalance between DG generation and load degrades network stability. This paper proposes a hybrid parallel Particle Swarm Optimization - Grey Wolf Optimizer (PSGWO) algorithm for load shedding optimization. This optimization aims to reduce the DG power not absorbed by the remaining loads and maintain the voltage within the specified limits. The performance of PSGWO is tested on an IEEE 33 bus radial distribution system, considering loading levels of 80% to 140% of the baseload. At a 100% loading level, PSGWO showed the best performance, with a load shedding of 2.2297 MW and a voltage deviation of 0.0049. These values are the smallest compared to the results of the standard PSO and GWO algorithms. The PSGWO algorithm remains superior and converges faster than standard PSO and GWO at all loading levels.

Optimization of Installation Position for Complex Space Curve Weldments in Robotic Friction Stir Welding Based on Dynamic Dual Particle Swarm Optimization

Robotic friction stir welding (RFSW), with its wide application range, ample working space, and task flexibility, has emerged as a vital development in friction stir welding (FSW) technology. However, the low stiffness of serial industrial robots can lead to end-effector deviations and vibrations during FSW tasks, adversely affecting the weld quality. This paper proposes a dynamic dual particle swarm optimization (DDPSO) algorithm through a new comprehensive stability index that considers both the stiffness and vibration stability of the robot to optimize the installation position of complex space curve weldments, thereby enhancing the robot’s stability during the FSW process. The algorithm employs two independent particle swarms for exploration and exploitation tasks and dynamically adjusts task allocation and particle numbers based on current results to fully utilize computational resources and enhance search efficiency. Compared to the standard particle swarm optimization (PSO) algorithm, the DDPSO approach demonstrated superior search capabilities and stability of optimization results. The maximum fitness value improved by 4.2%, the average value increased by 12.74%, and the concentration level of optimization results rose by 72.91% on average. The new optimization method pioneers fresh perspectives for optimizing the stability of RFSW, providing significant grounds for the process optimization and offline programming of complex spatial curve weldments.

Design and optimization of driving mode control strategies for front-and-rear-axle-independent-drive-type electric vehicle

Design and optimization of driving mode control strategies for front-and-rear-axle-independent-drive-type electric vehicle

Design and modelling of an AI governed type-2 Fuzzy tilt control strategy for AGC of a multi-source power grid in constraint to optimal dispatch

The robust design and application of a novel type-2 fuzzy tilt controller (Type2-FTC) has been well demonstrated in this manuscript. This controller is anticipated to build automatic generation control (AGC) of a multi-area hybrid power system in concern to the optimal power generation scheduling. The combined action of AGC and optimal scheduling of a power system is referred as economic AGC or commonly ELD-AGC. At the initial stage of the analysis, the concept of only AGC has been discussed through various robust approaches. Then the extensive study on ELD-AGC of the proposed power system has been progressed under various controller actions. Further, the proposed Type2-FTC controller is designed ideally with employing an innovative quassi-opposition path finder algorithm (QO-PFA) in various operating conditions. The effectiveness and the viability of the proposed Type2-FTC controller has been verified over type-I fuzzy PID and conventional PID controllers for AGC as well as ELD-AGC of the multi area power system. In controller comparison study, it has been revealed that proposed Type2-FTC is a potential candidate to improve frequency stability of the system. Further, the analysis over technique study justifies supremacy of the suggested QO-FPA algorithm over standard PFA and PSO algorithms for optimal designing all implemented controllers.

A study of home energy management considering carbon quota

The household energy management system (HEMS) has become an important system for energy conservation and emission reduction. In this study, home energy management considering carbon quota has been established. Firstly, the household photovoltaic output model, load model of various electrical appliances, battery load model, and charging and discharging of electric vehicles (EVs) model are established. Secondly, the carbon emission and carbon quota of household appliances and EVs are considered in these models. Thirdly, the energy optimization model of minimum the household user’s total comprehensive operation cost with the minimum total electricity consumption, carbon trading cost, battery degradation cost, and carbon quota income are proposed, taking into account constraints such as the comfort of users’ energy use time. Subsequently, the improved particle swarm optimization (IPSO) algorithm is used to tackle the problem. Compared to the standard particle swarm optimization (PSO), the IPSO has significantly improved the optimization effect. By comparing the optimization results in different scenarios, the effectiveness of the strategy is verified, and the influence of different carbon trading prices on optimal energy scheduling has been analyzed. The result shows that the comprehensive consideration of carbon trading cost and total electricity cost can reduce the household carbon emissions and the total electricity cost of the household user. By increasing the carbon trading price, the user’s carbon trading income and the EV carbon quota income increase, and the overall operating cost decreases; the guidance and regulation of carbon trading price can make a valuable contribution to HEMS optimization. Compared to the original situation, the household carbon emissions are reduced by 14.58 kg, a decrease of over 21.47%, while the total comprehensive operation cost are reduced by 14.12%. Carbon quota trading can guide household users to use electricity reasonably, reducing household carbon emissions and the total cost of household electricity.

Techno-economic and environmental assessments of optimal planning of waste-to-energy based CHP-DG considering load growth on a power distribution network

Load growth puts pressure on existing electric infrastructure and impacts on the system's performance parameters which may necessitate network expansion. Conventionally, electric network expansion is done by building new substations or reinforcing the existing ones with new transformers and upgrading the network feeders. However, optimal allocation of combined heat and power distributed generators (CHP-DGs) on distribution networks (DNs) can be adopted for network expansion planning problem. Optimal DG allocation is an optimisation problem which requires an efficient optimisation approach. In this paper, an improved particle swarm optimisation (IPSO) based on weighted randomised acceleration coefficient and adaptive inertia weight is proposed for optimal DG allocation problem. The considered CHP-DGs include internal combustion engine (ICE) and fuel cells (FCs) powered by biogas obtained from anaerobic digestion of food wastes. The proposed IPSO is tested on IEEE 69 bus radial distribution network under single and multi-objectives considering constant and mixed seasonal voltage-dependent load models. Some of the key findings show that integrating ICE-based CHP-DGs operating at optimal power factor in winter day mixed voltage dependent load in base year achieves 97.63 % active power loss reduction in comparison to 77.14 % loss reduction for unity power factor operating FC-based CHP-DG. Economic and environmental evaluation indicate that FC-based CHP-DG records a net present value of over 29.29 million $, levelised cost of energy of 0.0493 $/kWh and zero pollutant emission in comparison with 28.40 million $, 0.0501 $/kWh and 0.2817 million kg pollutant emission for ICE-based CHP-DG over the planning horizon. In comparison with the standard PSO, the proposed IPSO performs better in terms of solution quality, convergence speed and statistical results.

Path tracking control of automated vehicles based on adaptive MPC in variable scenarios

AbstractFor complex and dynamic high‐speed driving scenarios, an adaptive model predictive control (MPC) controller is designed to ensure effective path tracking for automated vehicles. Firstly, in order to prevent model mismatch in the MPC controller, a tire cornering stiffness estimation algorithm is designed and a soft constraint on slip angle is added to further enhance the controller's precision in tracking trajectories and the vehicle's driving stability. Secondly, the improved particle swarm optimization (IPSO) method with dynamic weights and penalty functions is suggested to address the issue of insufficient accuracy in solving quadratic programming. Additionally, the standard particle swarm optimization (PSO) algorithm is used to seek the most suitable time horizon parameters offline to obtain the best time horizon data set under different vehicle speeds and adhesion coefficients, and then it is optimized online by an adaptive network‐based fuzzy inference system (ANFIS) to enhance the model predictive controller's adaptability in different operating conditions. Finally, simulation experiments are conducted under three different operating conditions: docked roads, split roads, and variable vehicle speeds. The results indicate that the designed adaptive MPC controller can accurately and stably track the reference trajectory in various scenarios.

An Enhanced Particle Swarm Optimization Algorithm via Adaptive Dynamic Inertia Weight and Acceleration Coefficients

The particle swarm optimization (PSO) algorithm counts among the most popular metaheuristic algorithms based on swarm intelligence. Since the publication of the first article on this optimization technique, researchers have developed many PSO variants with some improvement in its performance. The PSO optimization performance hinges on its ability to achieve a good exploration-exploitation balance. The most common method that helps to improve exploration-exploitation balance is modifying the PSO three controlling parameters, namely the inertia weight and two acceleration coefficients. In this paper a PSO variant that combines adaptive dynamic inertia weight and adaptive dynamic acceleration coefficients to enhance the exploration-exploitation balance of the PSO is proposed. The enhanced PSO algorithm called Adaptive Dynamic Inertia Weight and Acceleration Coefficient Optimization (ADIWACO) algorithm is tested on seven well-known standard test functions comprising four unimodal and three multimodal ones. The performance of the PSO is then compared with that of the standard PSO (SPSO) and four existing PSO variants. The experimental results comprising optimum value, runtime, mean value, standard deviation and convergence rate, and confirmed by the results of ranking statistics and Wilcoxon signed rank test conducted on the experimental results, indicate significantly better performance by the proposed PSO algorithm.

Prediction of Transformer Oil Temperature Based on an Improved PSO Neural Network Algorithm

Abstract: In addressing the issue of power transformer oil temperature prediction, traditional back propagation (BP) neural network algorithms have been found to suffer from local optimization and slow convergence. This study proposes an oil temperature prediction model based on an improved particle swarm optimization (PSO) neural network algorithm, which introduces an asymmetric adjustment learning factor and a mutation operator. The BP neural network, genetic algorithm (GA) optimization neural network, and the improved PSO neural network are compared by considering various factors, such as ambient temperature, load changes, and the number of cooler groups under different working conditions. Results show that the proposed algorithm improves the actual change trend of oil surface temperature and makes the transformer operation more stable to a certain extent. Background: The mathematical model for predicting transformer oil temperature is clear, but the parameters in the model are uncertain and vary with time. When subjected to different operating conditions, such as ambient temperature, load changes, and the number of cooler groups acting independently or in combination, the prediction results of the oil temperature model vary with different system parameters. Objective: This paper aims to enhance the accuracy of transformer temperature prediction. In order to optimize the oil temperature prediction model, asymmetric adjustment learning factors and mutant operators are added to meet diverse system parameter requirements. Methods: The paper utilizes an oil temperature prediction model based on an improved PSO neural network algorithm, which introduces an asymmetric adjustment learning factor and a mutation operator to address the limitations of the standard PSO algorithm. Results: This paper has employed a fusion algorithm of the genetic algorithm of the BP neural network and the PSO algorithm, and conducted simulation and experimental analysis. The simulation and experimental results demonstrate the accuracy and effectiveness of the fusion algorithm. Conclusion: This study demonstrates enhanced prediction accuracy of transformer oil surface temperature using the improved particle swarm optimization neural network algorithm. This algorithm has less prediction error under different working conditions compared to other algorithms. By increasing population diversity and combining inertia weights, the algorithm not only greatly improves its search performance but also avoids local optimization.

Enhanced PSO Optimized Leader in Cloud-Fog Task Scheduling for IoT and Mobile Crowdsensing Environments

The data generated by the IoT needs a powerful platform such as cloud computing for data processing. However, the cloud faces challenges when dealing with various types of resources, high delay, and cost, this represents a substantial challenge in scheduling tasks. Therefore, the need appeared to introduce the concept of fog. To address these limitations, optimization algorithms such as PSO were used. In traditional PSO, all particles in the swarm are influenced by a single global best particle (Gbest), if it becomes stuck in a local optimum, all the particles will move closer to it, thus, the PSO may easily get trapped in premature convergence. This paper proposed an adaptive cloud-fog integrated approach based on modified PSO called PSO Optimized Leader (PSO-OL). These modifications on four stages: Firstly, a method to ensure swarm diversity in the initialization phase is introduced. Secondly, to reduce the chance of the population getting trapped in a local optimum, the farthest-best particle is introduced. Third, in addition to the primary Gbest, a second Gbest represents a different good particle presented to explore multiple promising regions. Finally proposed a new crossover operator to get an optimized leader. The PSO-OL approach was evaluated and the results show the effectiveness of the enhanced leader by 40% with farthest-best, 45% with second-Gbest when compared to standard PSO, and when compared to scheduling algorithms where outperforms the other algorithms by minimizing makespan by 34%, cost by 14%, and increasing throughput by 75%, in comparison to existing load balancing and scheduling methods: RR, BLA, MPSO, ETS, and TCaS.

Research on system of ultra-flat carrying robot based on improved PSO algorithm

Ultra-flat carrying robots (UCR) are used to carry soft targets for functional safety road tests of intelligent driving vehicles and should have superior control performance. For the sake of analyzing and upgrading the motion control performance of the ultra-flat carrying robot, this paper develops the mathematical model of its motion control system on the basis of the test data and the system identification method. Aiming at ameliorating the defects of the standard particle swarm optimization (PSO) algorithm, namely, low accuracy, being susceptible to being caught in a local optimum, and slow convergence when dealing with the parameter identification problems of complex systems, this paper proposes a refined PSO algorithm with inertia weight cosine adjustment and introduction of natural selection principle (IWCNS-PSO), and verifies the superiority of the algorithm by test functions. Based on the IWCNS-PSO algorithm, the identification of transfer functions in the motion control system of the ultra-flat carrying robot was completed. In comparison with the identification results of the standard PSO and linear decreasing inertia weight (LDIW)-PSO algorithms, it indicated that the IWCNS-PSO has the optimal performance, with the number of iterations it takes to reach convergence being only 95 and the fitness value being only 0.117. The interactive simulation model was constructed in MATLAB/Simulink, and the critical proportioning method and the IWCNS-PSO algorithm were employed respectively to complete the tuning and optimization of the Proportional-Integral (PI) controller parameters. The results of simulation indicated that the PI parameters optimized by the IWCNS-PSO algorithm reduce the adjustment time to 7.99 s and the overshoot to 13.41% of the system, and the system is significantly improved with regard to the control performance, which basically meets the performance requirements of speed, stability, and accuracy for the control system. In conclusion, the IWCNS-PSO algorithm presented in this paper represents an efficient system identification method, as well as a system optimization method.

Improved particle swarm optimisation tuned PID position control of voice coil semi-active electrohydraulic damper

Control on the variable damping coefficients in the semi-active damper enhances ride comfort and vehicle stability. The semi-active electrohydraulic damper (SEH) presented in this study incorporates voice coil-based robust valve control to modify the damping coefficients. However, the position tracking accuracy of the voice coil actuator (VCA) gets negatively influenced by the nonlinearities such as load variations due to external disturbances, back emf, and dynamic damper characteristics. Therefore, to achieve excellent position control, a restart particle swarm optimisation with the Gompertz function (PSO-RG) is presented to obtain optimal tuning control gain for the proportional integral derivative controller. The PSO-RG algorithm applies Gompertz function-based particle coefficients and restarting technique. These techniques allow the PSO-RG to overcome the limitations of standard PSO, such as premature convergence and particle entrapment in local minima. The algorithm is tested in five selected benchmark functions for 10, 30 and 50 dimensions. The test data conveys that PSO-RG outperforms the standard PSO in mean, standard deviation, the best solution, and convergence speed. The transient response on identified and validated model of VCA demonstrated a 75.21% reduction in integral absolute error value than conventional PSO. The obtained maximum sensitivity and total variation values proved the robustness and smoothness of PSO-RG tuned PID control. Furthermore, the experimental analysis on the quarter car model shows that PSO-RG tuned PID precisely tracked the reference position with a 21.66% decrease in root mean square error to conventional PSO tuned PID. The CarSim co-simulation test results in the large van model indicate the need for precise VCA position control in enhancing ride comfort and improving the roll stability of the vehicle.

A Hybrid Particle Whale Optimization Algorithm with application to workflow scheduling in cloud–fog environment

Workflow scheduling aims to optimize task allocation and execution in cloud–fog​ computing environments. Finding an optimal algorithm for workflow scheduling poses a significant challenge due to the complexity and variability of tasks and resources involved. Although meta-heuristic algorithms have been proposed to address these challenges, they often suffer from being trapped in local optima, failing to achieve the global optimal solution. Our work used a hybrid approach “Particle Whale Optimization Algorithm” (PWOA) to overcome the above-mentioned problem by combining Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA).PSO’s limitation in high-dimensional search spaces, characterized by slow convergence toward the global optimum, and the population-based WOA algorithm’s limited exploration of search spaces are overcome by the proposed algorithm PWOA. The proposed algorithm PWOA overcomes the limitations of PSO and WOA.The objective of the PWOA algorithm is to minimize the Total Execution Time (TET) and Total Execution Cost (TEC) associated with dependent tasks in a cloud–fog computing environment. To evaluate the performance of the PWOA algorithm, extensive simulations are conducted using test cases (30, 50, 100 and 1000) from five different scientific workflows: Cybershake, Epigenomics, Inspiral, Montage and Sipht. These workflows encompassed varying numbers of assigned tasks. The simulation results demonstrated that the proposed PWOA algorithm outperformed the standard PSO and WOA algorithms to improve TET and TEC across all the diverse workflows. This study also provides a comparative view of the effectiveness of the PWOA algorithm in enhancing workflow scheduling performance.

Optimal Power Flow Using Particle Swarm Optimization Hybrid Inertia Weight and Constriction Factor Algorithm (PSOHIC) Case Study: Thermal Generator System of 150 kV Sulbagsel

The increasing use of electricity encourages electricity scientists to create or build mathematical models to improve the quality of electric power. This study uses IEEE 26 bus system data to validate the method and 150 kV thermal generator data from the South Sulawesi (Sulbagsel) system as a case study. The method used is PSOHIC. The simulation results for the 150 kV Sulbagsel system data show that PSOHIC converges more quickly, namely in the 8th iteration. The standard PSO converges at the 25th iteration. The IPSO algorithm converges at the 20th iteration. At the same time, the MIPSO algorithm converges at the 12th iteration. The power flow simulation results show that with PSOHIC, the power loss of 16.48 MW is smaller than the current system of 19.10 MW, that is, the power loss is reduced by 0.1613%. The production cost with PSOHIC is IDR 281,860.91/hour, cheaper than MIPSO, IPSO and PSO.