Performance Of Particle Swarm Optimization Research Articles

Enhancing microgrid production through particle swarm optimization and genetic algorithm

<p>The growing demand for sustainable and efficient energy solutions has led to research on optimizing renewable energy sources within microgrid systems. This study presents a comparative analysis of two prominent optimization techniques, particle swarm optimization (PSO) and genetic algorithm (GA), to enhance solar photovoltaic PV and wind production in microgrids. The aim is to achieve a balanced and efficient energy generation that closely matches the load demand, thereby minimizing energy wastage and ensuring a reliable energy supply. The two algorithms are employed using data representing PV and wind production, as well as load consumption, over a 24-hour period. The results are evaluated based on their ability to reduce the gap between energy production and load demand. Our findings reveal compelling insights into the performance of GA and PSO in the context of microgrid optimization. To validate the results obtained from the simulation, the PSO algorithm is implemented on an actual cart Digital Signal Processor DSP platform, using a processor-in-the-loop (PIL). This successful real-world application highlights the practical viability of utilizing PSO to improve solar PV and wind energy generation within microgrids.</p>

LOAD FREQUENCY CONTROL STRATEGY FOR MICROGRID SYSTEMS USING COMPUTATIONAL INTELLIGENCE OPTIMIZED PID CONTROLLER

This paper presents Load Frequency Control (LFC) strategy for Microgrid (MG) systems, proposing a strategy based on a PID optimized controller using computational intelligence (CI) technique. The MG system, relying solely on Renewable Energy Sources(RES) is modeled in SIMULINK. A novel multi-objective function comprising of Weighted Integral Square Error, WISE and Weighted Integral Absolute Square Error WIASE (WISE+WIASE) is proposed to enhance system performance. Using Particle Swarm Optimization (PSO) and Accelerated Particle Swarm Optimization (APSO), the Proportional, Integral and Derivative controller parameters are tuned. Simulation results revealed the superiority of the PSO-PID with the least steady state error of 4.1560e-07 and overshoot 0.0611 respectively over the APSO-PID with steady state error value of 4.8144e-07 and overshoot of 0.1334 respectively. PSO-PID also outperformed APSO-PID across other indices (ITAE, IAE, ITSE and ISE) making the PSO-PID to excel over the APSO-PID in terms of steady state error and overshoot. The controllers proved to be robust in various conditions with the PSO-PID controller exhibiting more robustness and stability than the APSO-PID controller. Comparing PSO and APSO algorithms, PSO outperformed APSO in terms of robustness because for five number of runs in both algorithms, the PSO algorithm presents a more quality and consistent results and also display a better convergence than the APSO algorithm . Conversely, APSO surpasses PSO in fast computational time and convergence speed. These findings underscore the effectiveness of the proposed LFC strategy for RES based Microgrid systems, providing valuable insights into comparative performance of PSO and APSO algorithms.

Performance analysis of an optimized PID-P controller for the position control of a magnetic levitation system using recent optimization algorithms

As industrial technology advances, there is an increasing need for very precise position control mechanisms for highly integrated and accurate products. Therefore, high precision positioning systems play an essential part in today's manufacturing processes. Piezoelectric actuators offer the requisite stiffness and positioning precision, but they have a limited traveling range. The combination of a linear motor with air bearings is a typical method for achieving long stroke movement at high speeds. However, to accomplish extensive and precise motion in several degrees of freedom with a linear motor and non-contact bearing, a sophisticated system setup is required. In this paper, a proportional-integral-derivative-proportional (PID-P) controller is presented for magnetic levitation system position control. Particle swarm optimization (PSO) and Black window optimization (BWO) algorithms are proposed for tuning the PID-P controller parameters. The PSO and BWO algorithms are employed by considering Integral Time Absolute Error (ITAE) as an objective function with rise-time and percentage peak overshoot as constraints. Furthermore, the performance of PSO and BWOA tuned PID-P controllers is compared to conventional PID-P and PID controllers using time response specifications such as rise time, settling time, and percentage peak overshoot. The graphical and numerical simulation results show that the PSO and BWOA tuned PID-P controller outperforms the conventional PID-P and PID controllers. The BWOA tuned PID-P controller outperformed the PSO tuned PID controller and the classical PID-P controller by 8.5 % in rise time, 46.77 % in settling time, and 86.6 % in percentage peak overshoot.

Particle Swarm Optimization (PSO) And Evolutionary Programming (EP) Technique for Optimal Placement and Sizing DG for Minimizing Loss in IEEE-30 Bus System

In recent years, most growing nations worldwide have experienced an unavoidably rising demand for electricity, which may result in a dropping of the voltage profile and a reduction in system stability, both of which have the potential to overload the power generation system significantly. One of the ways to reduce loss system network in this study is by distributed generation (DG) allocation, which the DG optimal location and sizing are the two most significant considerations for integration of DG. Improper placement and DG sizing in the power system not only result in increased total power losses but also affect the electrical system’s capability to be function properly. This paper provides a comparative analysis of two meta- heuristic optimization techniques, that is, Particle Swarm Optimization (PSO) and Evolutionary Programming (EP), to examine the optimal placement and sizing of distributed-generation (DG) for photovoltaic systems with the aim of minimizing the losses in interconnected distributed generation, IEEE-30 bus system. The objective function of this study is minimization the power loss with considered some constraints which are voltage limits and DG sizing limits are being covers in this study. The optimization issue has been solved by working with MATLAB/m-files software. The outcomes derived from the simulation results in this study demonstrated that PSO performance of two units of DG is better compared to EP algorithm. The voltage profile was improved drastically, and the power loss was reduced as lowest as 17.4486MW in the first case, 17.4368MW in the second case, and 17.4179MW in the third case.

Improving with Hybrid Feature Selection in Software Defect Prediction

Software defect prediction (SDP) is used to identify defects in software modules that can be a challenge in software development. This research focuses on the problems that occur in Particle Swarm Optimization (PSO), such as the problem of noisy attributes, high-dimensional data, and premature convergence. So this research focuses on improving PSO performance by using feature selection methods with hybrid techniques to overcome these problems. The feature selection techniques used are Filter and Wrapper. The methods used are Chi-Square (CS), Correlation-Based Feature Selection (CFS), and Forward Selection (FS) because feature selection methods have been proven to overcome data dimensionality problems and eliminate noisy attributes. Feature selection is often used by some researchers to overcome these problems, because these methods have an important function in the process of reducing data dimensions and eliminating uncorrelated attributes that can cause noisy. Naive Bayes algorithm is used to support the process of determining the most optimal class. Performance evaluation will use AUC with an alpha value of 0.050. This hybrid feature selection technique brings significant improvement to PSO performance with a much lower AUC value of 0.00342. Comparison of the significance of AUC with other combinations shows the value of FS PSO of 0.02535, CFS FS PSO of 0.00180, and CS FS PSO of 0.01186. The method in this study contributes to improving PSO in the SDP domain by significantly increasing the AUC value. Therefore, this study highlights the potential of feature selection with hybrid techniques to improve PSO performance in SDP.

Comparison of the PSO Algorithm and JAYA Algorithm for Economic Load Dispatch

The electrical power industry has undergone the significant changes, leading to a competitive market and the need for optimal economic dispatch solutions. As energy resources becoming scarce, prices rise, environmental concerns intensify, and electricity consumption increases, classical optimization techniques struggle to handle these complex issues. This project compares particle swarm optimization (PSO) and JAYA optimization method for economic load dispatch (ELD) problems in the electrical power industry. PSO has gained recognition as an effective solution for ELD problems in the last decade. The aim of this study is to show that Jaya optimization is the best optimization methodology for ELD problems. By comparing the performance, convergence speed, and accuracy of PSO and JAYA optimization, this paper aims to provide insights into the superiority of JAYA optimization. Key Words: Economic Load dispatch, Optimization method, PSO algorithm, JAYA algorithm, Incremental fuel cost

Design and multi-objective optimization of combined air separation and ORC system for harnessing LNG cold energy considering variable regasification rates

Regasification of liquefied natural gas (LNG) releases significant cooling potential, but improper usage can easily lead to wastage of resources and environmental pollution. The study developed an integrated air separation system and Organic Rankine Cycle (ORC) power generation system to effectively harness the inherent cold energy within LNG. Two distinct optimization algorithms, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and the Particle Swarm Optimization (PSO), were employed to determine the optimal operational parameters. The results showed the superior performance of PSO over NSGA-II, as demonstrated by the exergy efficiency, net output power, and levelized cost of energy at 78.12 %, 2.29*1011 kW, and 0.0665 $/kWh, respectively. Additionally, an assessment of various factors influencing the system's performance was conducted. The performance of both conventional and proposed systems was studied under varying regasification rates. The findings indicated that incorporating the cold recovery and storage unit enhanced the efficient utilization of LNG cold energy, resulting in an increased valley-to-peak ratio of the total power output from 0.03 in the conventional system to 0.5. The proposed system exhibits alignment with the energy consumption demands of users, and this study is anticipated to furnish a theoretical foundation for harnessing the cooling capacity of liquid hydrogen.

TPPSO: A Novel Two-Phase Particle Swarm Optimization

Particle swarm optimization (PSO) is a stout and rapid searching algorithm that has been used in various applications. Nevertheless, its major drawback is the stagnation problem that arises in the later phases of the search process. To solve this problem, a proper balance between investigation and manipulation throughout the search process should be maintained. This article proposes a new PSO variant named two-phases PSO (TPPSO). The concept of TPPSO is to split the search process into two phases. The first phase performs the original PSO operations with linearly decreasing inertia weight, and its objective is to focus on exploration. The second phase focuses on exploitation by generating two random positions in each iteration that are close to the global best position. The two generated positions are compared with the global best position sequentially. If a generated position performs better than the global best position, then it replaces the global best position. To prove the effectiveness of the proposed algorithm, sixteen popular unimodal, multimodal, shifted, and rotated benchmarking functions have been used to compare its performance with other existing well-known PSO variants and non-PSO algorithms. Simulation results show that TPPSO outperforms the other modified and hybrid PSO variants regarding solution quality, convergence speed, and robustness. The convergence speed of TPPSO is extremely fast, making it a suitable optimizer for real-world optimization problems.

A Deadline-Budget Constrained Task Admission Control for a Software-as-a-Service Provider in Cloud Computing

Cloud computing is a pay-as-you-go model that provides users with on-demand access to services or computing resources. It is a challenging issue to maximize the service provider's profit and, on the other hand, meet the Quality of Service (QoS) requirements of users. Therefore, this paper proposes an admission control heuristic (ACH) approach that selects or rejects the requests based on budget, deadline, and penalty cost, i.e., those given by the user. Then a service level agreement (SLA) is created for each selected request. The proposed work uses Particle Swarm Optimization (PSO) and the Salp Swarm Algorithm (SSA) to schedule the selected requests under budget and deadline constraints. Performances of PSO and SSA with and without ACH are evaluated and compared using CloudSim. The simulation results prove that admission control maximizes profit by minimizing the number of task rejections and SLA violations. It also improves resource utilization while balancing makespan.

LSTM-based framework with metaheuristic optimizer for manufacturing process monitoring

Quick process shift detection and lower out-of-control run length are essential for monitoring the production process, especially in modern smart manufacturing. Specifically, the out-of-control run length is one of the most critical performance measures to evaluate the manufacturing process monitoring (MPM) model. The sooner the out-of-control is detected, the better the model is. However, developing a monitoring model which can provide quick shift detection for various data dimensions and volumes is challenging. In this research, single (1_LSTM) and stacked (S_LSTM) long-short-term memory (LSTM) based models with metaheuristic optimizer were proposed to detect process shifts quickly in the manufacturing domain. Based on the literature, three metaheuristic methods: Clustering-based organism search (CSOS), Particle Swarm Optimization (PSO), and Simulated Annealing (SA) that are suitable for high-dimensional optimization were utilized in the proposed method to optimize weights in the LSTM-based network. The proposed models were evaluated based on average out-of-control run length (ARL1) against benchmark methods on various synthesized multivariate normal and real-world datasets. Also, the performances of CSOS, PSO, and SA were compared. The results show that CSOS_S_LSTM outperforms other methods with lower ARL1. The result also confirmed the effectiveness and applicability of the proposed models for real-world problems. The experimental results showed that the response time of detection can be improved by 33.19% and 38.77% on average using the proposed 1_LSTM and CSOS LSTM-based metaheuristics models, respectively.

A Comparative Study of Particle Swarm Optimization and Artificial Bee Colony Algorithm for Numerical Analysis of Fisher’s Equation

The aim of this research work is to obtain the numerical solution of Fisher’s equation using the radial basis function (RBF) with pseudospectral method (RBF-PS). The two optimization techniques, namely, particle swarm optimization (PSO) and artificial bee colony (ABC), have been compared for the numerical results in terms of errors, which are employed to find the shape parameter of the RBF. Two problems of Fisher’s equation are presented to test the accuracy of the method, and the obtained numerical results are compared to verify the effectiveness of this novel approach. The calculation of the error norms leads to the conclusion that the performance of PSO is better than the ABC algorithm to minimize the error for the shape parameter in a given range.

Evaluating Alternative Metaheuristic Algorithms for Procedural Content Generation in Game Design

Procedural Content Generation (PCG) has emerged as a powerful approach for automating game content creation, offering significant benefits in terms of cost reduction and time efficiency compared to traditional game design and development processes (Zhang, Zhang, & Huang, 2022). While Genetic Algorithms (GAs) have been widely used in PCG, alternative metaheuristic algorithms such as Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) have demonstrated their effectiveness in delivering high-quality solutions and efficient optimization capabilities across different problem domains (Amato, 2017). However, their application in PCG remains limited. I aim to evaluate the performance of PSO and ABC in map layout generation, challenging the conventional use of GAs. By comparing three metaheuristic algorithms (GA, ABC, and PSO) I seek to assess the effectiveness of these approaches in generating game levels and identify any obvious differences in their performance characteristics. Comprehensive experiments are conducted, applying GA, ABC, and PSO to a map layout generation. Metrics like convergence speed and content quality are used to evaluate the generated game content. My findings reveal that both ABC and PSO demonstrate advantages over traditional GA implementations when generating game levels, indicating their potential for enhancing PCG. In this presentation, I will share the results of comparing three metaheuristic algorithms (GA, PSO, and ABC) in map layout generation for game levels, emphasizing the potential benefits of leveraging diverse algorithmic approaches to create more captivating and immersive game worlds. Also, I will conclude with a call for further research in this area to expose new possibilities in content generation. By considering varied metaheuristic approaches, game developers can improve content generation techniques and create more captivating and interactive player experiences.

A Novel Particle Swarm Optimization for Channel Allocation in OFDM Based Cognitive Radio Networks

It has become increasingly apparent that bandwidth scarcity is an issue as wireless communications advance. Alternatively, spectrum sensing techniques are used to detect licensed users. A spectrum sensor can detect energy, matched filters, and cyclostationary features. There are, however, some drawbacks to these methods. Energy detector performance is affected by noise power uncertainty. Every primary user needs a dedicated receiver for matched filter spectrum sensing. Computational effort and observation time are required for cyclo-stationary feature detection. Spectrum use is determined using particle swarm optimization (PSO), an algorithm for determining the best frequency allocation and highest accuracy. Using PSO operations, this paper proposes an improved energy detection method compared to conventional energy detection methods. Detecting energy and using the PSO channel allocation technique to detect fading channels is also mathematically described.

Particle Swarm Optimization (PSO) of High-Quality Magnetic Data of the Obudu Basement Complex, Nigeria

The particle swamp optimization procedure was applied to high-quality magnetic data acquired from the Precambrian Obudu basement complex in Nigeria with the object of estimating the distinctive body parameters (depth (z), index angle (θ), amplitude coefficient (K), shape factor (Sf), and location of the origin (x0)) of magnetic models. The magnetic models were obtained from four profiles that ran perpendicular to the observed magnetic anomalies within the study area. Profile A–A’ with a length of 2600 m is characterized by inverted model parameters of K = 315.67 nT, z = 425.34 m, θ = 43°, Sf = 1.15, and x0 = 1554.86 m, while profile B–B’ with a length of 5600 m is described by K = 257.71 nT, z = 543.75 m, θ = 54°, Sf = 0.96, and x0 = 3645.42 m model parameters. Similarly, profile C–C’ with a length of 3000 m is defined by K = 189.53 nT, z = 560.87 m, θ = 48, Sf = 1.2, and x0 = 1950 m. Profile D–D’, which is well-defined by a 2500 m length, started at the crest of the observed magnetic anomaly and displays inverted model parameters of 247.23 nT, 394.16 m, 39°, 1.26, and 165.41 m. Correlatively, the estimated shape factor of the four models (Sf = 1.15, 0.96, 1.2, and 1.26) shows that the magnetic models are linked to thin sheets. Furthermore, quantitative interpretations of the models show that the PSO operation is rapid and proficient.

Optimized design of patrol path for offshore wind farms based on genetic algorithm and particle swarm optimization with traveling salesman problem

SummaryWith the rapid expansion of global offshore wind power market, the research on improving the full life cycle income and reducing the construction and operation and maintenance costs has attracted the attention of scholars in the industry. In view of the different aging degree and maintenance cycle of wind turbines, this paper studies the optimized design of patrol path for offshore wind farms based on genetic algorithm (GA) and particle swarm optimization (PSO) with traveling salesman problem (TSP). Firstly, the problem of patrol routing planning in offshore wind farms is described as the traveling salesman problem of shortest route optimization. Secondly, the GA and PSO algorithms are simulated and verified separately, and the patrol path distance is taken as the objective function. Finally, through simulation experiments, the optimized patrol path performances of PSO and GA are compared, which can help to find a shortest route and reduce the operation and maintenance costs.

PSO with Mixed Strategy for Global Optimization

Particle swarm optimization (PSO) is an evolutionary algorithm for solving global optimization problems. PSO has a fast convergence speed and does not require the optimization function to be differentiable and continuous. In recent two decades, a lot of researches have been working on improving the performance of PSO, and numerous PSO variants have been presented. According to a recent theory, no optimization algorithm can perform better than any other algorithm on all types of optimization problems. Thus, PSO with mixed strategies might be more efficient than pure strategy algorithms. A mixed strategy PSO algorithm (MSPSO) which integrates five different PSO variants was proposed. In MSPSO, an adaptive selection strategy is used to adjust the probability of selecting different variants according to the rate of the fitness value change between offspring generated by each variant and the personal best position of particles to guide the selection probabilities of variants. The rate of the fitness value change is a more effective indicator of good strategies than the number of previous successes and failures of each variant. In order to improve the exploitation ability of MSPSO, a Nelder–Mead variant method is proposed. The combination of these two methods further improves the performance of MSPSO. The proposed algorithm is tested on CEC 2014 benchmark suites with 10 and 30 variables and CEC 2010 with 1000 variables and is also conducted to solve the hydrothermal scheduling problem. Experimental results demonstrate that the solution accuracy of the proposed algorithm is overall better than that of comparative algorithms.

Hyperparameter Optimization for 1D-CNN-Based Network Intrusion Detection Using GA and PSO

This study presents a comprehensive exploration of the hyperparameter optimization in one-dimensional (1D) convolutional neural networks (CNNs) for network intrusion detection. The increasing frequency and complexity of cyberattacks have prompted an urgent need for effective intrusion-detection systems (IDSs). Herein, we focus on optimizing nine hyperparameters within a 1D-CNN model, using two well-established evolutionary computation methods—genetic algorithm (GA) and particle swarm optimization (PSO). The performances of these methods are assessed using three major datasets—UNSW-NB15, CIC-IDS2017, and NSL-KDD. The key performance metrics considered in this study include the accuracy, loss, precision, recall, and F1-score. The results demonstrate considerable improvements in all metrics across all datasets, for both GA- and PSO-optimized models, when compared to those of the original nonoptimized 1D-CNN model. For instance, on the UNSW-NB15 dataset, GA and PSO achieve accuracies of 99.31 and 99.28%, respectively. Both algorithms yield equivalent results in terms of the precision, recall, and F1-score. Similarly, the performances of GA and PSO vary on the CIC-IDS2017 and NSL-KDD datasets, indicating that the efficacy of the optimization algorithm is context-specific and dependent on the nature of the dataset. The findings of this study demonstrate the importance and effects of efficient hyperparameter optimization, greatly contributing to the field of network security. This study serves as a crucial step toward developing advanced, robust, and adaptable IDSs capable of addressing the evolving landscape of cyber threats.

A Particle Swarm Optimization With Adaptive Learning Weights Tuned by a Multiple-Input Multiple-Output Fuzzy Logic Controller

In a canonical particle swarm optimization (PSO) algorithm, the fitness is a widely accepted criterion when selecting exemplars for a particle, which exhibits promising performance in simple unimodal functions. To improve a PSO's performance on complicated multimodal functions, various selection strategies based on the fitness value are introduced in PSO community. However, the inherent defects of the fitness-based selections still remain. In this paper, a novelty of a particle is treated as an additional criterion when choosing exemplars for a particle. In each generation, a few of elites and mavericks who have better fitness and novelty values are selected, and saved in two archives, respectively. Hence, in each generation, a particle randomly selects its own learning exemplars from the two archives, respectively. To strengthen a particle's adaptive capability, a multipleinput multiple-output fuzzy logic controller is used to adjust two parameters of the particle, i.e., an acceleration coefficient and a selection proportion of elites. The experimental results and comparisons between our new proposed PSO, named as MFCPSO in this paper, and other 6 PSO variants on CEC2017 test suite with 4 different dimension cases suggest that MFCPSO exhibits very promising characteristics on different types of functions, especially on large scale complicated functions. Furthermore, the effectiveness and efficiency of the fuzzy controlled parameters are discussed based on extensive experiments.

Optimize location tower crane and supply facilities on construction site by discrete PSO algorithm

The optimal positioning of tower crane locations and material supply points on the construction site is an important task and is considered a complex combinatorial problem. Metaheuristics are popular and effective techniques for solving such problems. This paper introduces the application of the standard Particle Swarm Optimization (PSO) algorithm with discrete integer variables to solve the problem of optimizing the position of tower cranes and material supply points. A calculation program was built and numerical tests with 2 scenarios was conducted. The results show the performance of PSO and the applicability of the algorithm in the class of technical problems similar to the one under consideration.

Sepsis Prediction by Using a Hybrid Metaheuristic Algorithm: A Novel Approach for Optimizing Deep Neural Networks.

The early diagnosis of sepsis reduces the risk of the patient's death. Gradient-based algorithms are applied to the neural network models used in the estimation of sepsis in the literature. However, these algorithms become stuck at the local minimum in solution space. In recent years, swarm intelligence and an evolutionary approach have shown proper results. In this study, a novel hybrid metaheuristic algorithm was proposed for optimization with regard to the weights of the deep neural network and applied for the early diagnosis of sepsis. The proposed algorithm aims to reach the global minimum with a local search strategy capable of exploring and exploiting particles in Particle Swarm Optimization (PSO) and using the mental search operator of the Human Mental Search algorithm (HMS). The benchmark functions utilized to compare the performance of HMS, PSO, and HMS-PSO revealed that the proposed approach is more reliable, durable, and adjustable than other applied algorithms. HMS-PSO is integrated with a deep neural network (HMS-PSO-DNN). The study focused on predicting sepsis with HMS-PSO-DNN, utilizing a dataset of 640 patients aged 18 to 60. The HMS-PSO-DNN model gave a better mean squared error (MSE) result than other algorithms in terms of accuracy, robustness, and performance. We obtained the MSE value of 0.22 with 30 independent runs.