Particle Swarm Optimization Search Research Articles

Comparative analysis of meta heuristics algorithm for differential amplifier design

The design of analog circuits is very essential for the development of system design based on electronics as the world is analog in nature. Furthermore, performance of emerging products depends on analog circuits for reduction of power dissipation and improvement of speed. Though in the system on chip (SoC) analog circuit consumes less, it is more complex to design it due to the analog circuit nature complexity. The job of the evolutionary algorithm (EA) in the process of optimization of an analog circuit based on complementary metal oxide semiconductor (CMOS) is to get the appropriate values that can be used for the design parameters of the given circuit in such a way such that all the targeted specification of the given circuit can be obtained. The chaotic serial particle swarm optimization (CSPSO) and cuckoo search (CS) algorithms are lower than to those found through the particle swarm optimization (PSO) and differential evolution (DE) algorithms. The CS algorithm obtained all the desired specifications with less power dissipation and the least total transistor area compared to those attained by the DE and PSO algorithms.

Parameter estimation of ECM model for Li-Ion battery using the weighted mean of vectors algorithm

Accurate parameter estimation of the equivalent circuit model (ECM) for Li-Ion batteries (LiBs) allows for better behavior modeling and understanding. This is crucial for various applications, such as battery management systems (BMSs) and renewable energy systems, as it enables more precise performance prediction and optimization. This article introduces a novel artificial intelligence (AI) approach to estimate the ECM parameters of LiBs. The proposed method leverages the innovative weIghted meaN oF vectOrs (INFO) algorithm to optimize ECM parameter values. INFO combines multiple vectors, considering their relative importance, to generate a single vector. The algorithm minimizes the disparity between the ECM’s estimated voltage and the battery’s measured voltage. The effectiveness of the proposed method is assessed using a test profile based on real-world driving data. Furthermore, A comparative analysis is conducted with other state-of-the-art optimization algorithms, including Artificial Ecosystem Optimizer (AEO), Grey Wolf Optimizer (GWO), Artificial Hummingbird Algorithm (AHA), Harris Hawks Optimization (HHO), Leader Harris Hawks Optimization (LHHO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Adolescent Identity Search Algorithm (AISA). Results demonstrate that the INFO outperforms these methods in terms of accuracy and convergence speed. The proposed INFO algorithm offers a promising approach for accurate parameter estimation in LIB modeling, contributing to improved BMS and enhanced performance prediction.

Extraction of maximum power from PV system based on horse herd optimization MPPT technique under various weather conditions

This paper introduces a novel approach that extracts the maximum power from photovoltaic (PV) system utilizing the Horse Herd Optimization (HHO) algorithm under different weather conditions, including fast changes in solar radiation (FCSR) and partial shading conditions (PSCs). The HHO algorithm is a technique for optimization that mimics the movement behavior exhibited by horses within a herd. The proposed MPPT controller has been tested and compared with other recognized metaheuristic algorithms, including the Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Flower Pollination (FP), Deterministic PSO (DPSO), and Cuckoo Search (CS). According to the simulation results, the proposed HHO-based MPPT method is found to outperform other considered metaheuristic methods in terms of the maximum power extraction, fast-tracking, and settling times under various weather conditions. Additionally, the suggested MPPT controller is robust and can continuously track the MPP under the FCSR. The performance of the proposed HHO controller is validated experimentally on a real PV system. It is demonstrated that the proposed HHO algorithm is robust and capable of outperforming the other counterpart metaheuristic algorithms as well as offering the highest MPPT efficiency of about 98.81 % with a rapid tracking time of 2.2 s. Furthermore, it has the lowest power oscillations of about 1.91 %, ensuring stable and consistent power output.

Optimal energy trading in cooperative microgrids considering hybrid renewable energy systems

This study presents a novel method for optimal energy trading within microgrids considering renewable energy (RE) integration. The proposed approach uses the hybridization of particle swarm optimization and gravitational search algorithms (PSO-GSA) with Nash Bargaining to optimize power flow and energy trading between interconnected MGs and the main utility grid. Unlike existing solutions, the proposed model promotes cooperative energy trading among MGs and the main grid, considering network constraints and RE's inherent uncertainty; consequently, it addresses key challenges related to model design, pricing fairness, power flow optimization, and network constraints. The proposed method is implemented in MATLAB® considering the interconnection of four different MGs, called cooperative MGs, which optimally enable energy trading within the cooperative MGs and main utility. Simulation results, case studies and comparisons demonstrate the relevance of the proposed hybrid PSO-GSA in terms of maximizing the RE utilization, reducing load burden on the main grid and substantial cost reductions, with monthly energy costs decreased by $60,720 compared to the base case of $94,551. Also, the robustness and efficiency of the proposed PSOGSA algorithm are evaluated by comparing it with other well-established metaheuristic optimization methods under the same system data, control variables, and constraints. This research significantly contributes to MG energy trading by proactively optimizing economic operations and increasing RE utilization while adapting cooperation among interconnected MGs.

MULTI-RESOURCE ALLOCATION AND LEVELING IN MULTI-PROJECT SCHEDULING PROBLEM WITH HYBRID-CHROMOSOME NON-DOMINATED SORTING GENETIC ALGORITHM II

Multi-resource allocation and leveling in multi-project (MR-AL-MP) scheduling refers to the attempt of producing a project schedule with minimum project duration and maximum resource utilization while complying with all precedence and resource availability constraints in a multi-project environment involving multiple resources. This study proposes a model that integrates both resource allocation and leveling models into a unified framework. This study develops a modified version of the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), called Hybrid-Chromosome NSGA-II, as the optimization algorithm. For validation purposes, the performance of Hybrid-Chromosome NSGA-II is compared with two benchmark metaheuristic algorithms which are Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Symbiotic Organisms Search (MOSOS) in optimizing a case study. It is shown that the proposed model and algorithm are able to produce a set of non-dominated solutions that represent the feasible trade-off relationships between the objectives. Furthermore, the Hybrid-Chromosome NSGA-II is superior to MOPSO and MOSOS in terms of the quality, spread, and diversity of the solutions.

Synergistic Optimization of Unit Commitment Using PSO and Random Search

Optimizing the order of thermal units for power generation plays a pivotal role in meeting load demand while minimizing fuel consumption. This paper introduces an enhanced hybrid method designed to schedule generating units with the simultaneous objectives of cost and emission reduction, which often pose a trade-off challenge. The hybrid approach integrates the parametric adaptation of particle swarm optimization (PSO) with the randomness of a random search algorithm. The introduction of intermediate variables enhances the performance of particles in the PSO framework, contributing to more effective optimization. To update the individual population's locations within the particle swarm optimization process, randomness is judiciously introduced using a random search method. To assess the potential of the proposed method, it is applied to the IEEE-39 bus system and a four-unit thermal system. The results obtained through the proposed approach are compared with those achieved by existing methods, demonstrating its effectiveness in achieving optimal solutions for the unit commitment problem.

Forecasting of fine particulate matter based on LSTM and optimization algorithm

Accurate air pollution forecasting may provide valuable information for urban planning to maintain environmental sustainability and reduce mortality risk due to health problems. The city with higher industrial activities, traffic congestion, population density, and energy consumption is most likely to produce higher pollution than the other cities. Therefore, this study uses hybrid deep learning models to forecast air pollution based on the concentration of particulate matter with diameter size of less than 2.5 μm (PM2.5) for two air quality monitoring stations in Kuala Lumpur, Malaysia. The proposed models predict the hourly air pollutant concentration based on 4-h historical input based on six air pollutant data, meteorology parameters, and PM2.5 concentration data from the neighboring air quality monitoring stations. Long short-term memory based on metaheuristic algorithms, namely particle swarm optimization and sparrow search algorithm (PSO-LSTM and SSA-LSTM), are first developed and applied to determine the significance input combination to the changes of PM2.5 concentration at respective target stations. Then, the input configuration that gives the best forecasting accuracy was selected for subsequent experiments using enhanced approaches based on ensemble empirical mode decomposition (EEMD-PSO-LSTM and EEMD-SSA-LSTM). Subsequently, this study also analyzed the contributions of the neighboring PM2.5 dataset to the fluctuation of PM2.5 concentration at target stations. It is found that EEMD-SSA-LSTM model of M5 improves other models in Batu Muda and Cheras by 2.65% and 20.00% for RMSE and 9.31% and 25.30% for MAE, respectively. The results indicate that the proposed model yields the highest forecasting accuracy compared to the other models, and additional information on neighboring PM2.5 significantly improves the forecasting accuracy at both target stations. Besides that, comparing the performance of the two optimization approaches, SSA provides better performance compared to PSO in optimizing LSTM hyperparameters to forecast PM2.5 concentration.

Distributed intelligence for consensus-based frequency control of multi-microgrid network with energy storage system

Multi-Microgrids (MMGs) provide a highly promising approach for accommodating diverse distributed energy resources and improving the overall system performance. This enhanced performance encompasses aspects such as reliability, resilience, flexibility, and energy efficiency. As MMGs become more prevalent, the conventional centralized control approach struggles to adequately address the coordinated operation of system. Therefore, this article proposes a leader-follower-based distributed control strategy (DCS) for coordinated frequency control of numerous heterogeneous and geographically dispersed DERs in MMG network. The DCS employs a robust nonlinear fractional-order proportional integral derivative controller for frequency control of MMG. To optimize the parameters of the controller, an adaptive hybrid chaotic atom search-particle swarm optimization (ACAS-PSO)-based distributed intelligent technique is proposed. Initially, the proposed control strategy efficacy has been tested against various single/multidimensional benchmark functions. Then, it is applied to optimize the parameters of the controller for frequency regulation of MMG system by minimizing the integral absolute error (IAE). The findings obtained illustrate that the proposed ACAS-PSO control technique has an IAE improvement of 42.21 % to 65.3 % in MG 1, 55 % to 96 % in MG 2 and 42.2 % to 84.5 % in MG 3 compared to state-of-the-art. Moreover, the proposed method enhances the frequency response in terms of settling time by 23.4 % to 68.7 % in MG 1, 45.2 % to 68.3 % in MG 2 and 36.35 % to 48 % in MG 3, and the control effort is enhanced by 31.2 % to 87.6 % in MG 1, 31.3 % to 87.05 % in MG 2 and 31.25 % to 73.2 %, in MG 3 compared to other techniques presented. In addition, the robustness of the proposed DCS is validated against plug-and-play ability, communication failure, and delays. Further, the proffered controller is validated by implementing in real-time digital simulation using OPAL-RT.

A Bi-level model for district-fairness participatory budgeting: Decomposition methods and application

Participatory budgeting is one of the most well-known and widespread participatory programs implemented in many municipalities worldwide. Targeting poorer regions to receive a greater per capita amount of spending than wealthier regions is the most important transformational aspect of participatory budgeting. However, current approaches do not provide a precise method for achieving social justice through participatory budgeting. This paper proposes a bi-level mixed-integer non-linear optimization framework under the partial cooperation assumption to promote social justice in participatory budgeting programs. In addition, single-level reformulation and linearization techniques are presented, along with valid inequalities that speed up their resolution procedure. The single-level linear problem is solved using the Benders decomposition algorithm to find global optimality. To improve the computational performance of the proposed model on large-scale instances, a hierarchical iterative, evolutionary algorithm is proposed based on the hybrid binary particle swarm optimization and gravitational search algorithm. To illustrate the capability of the proposed model, computational experiments were conducted on both adapted examples from the literature and real-world, large-scale cases implemented in recent years in Warsaw, the capital of Poland. The results show that the proposed model is significantly more efficient, affordable, and faster than other methods presented in the literature, such as the Greedy rule and ε-district-fair lottery. In addition, the proposed model is fully operational for real-world societal problems.

Recuperation based multi-objective generation expansion planning for a real-world power system

This paper proposed a multi-objective-based Generation Expansion Planning (GEP) for the real-word power generation system of Tamil Nadu, an Indian state. GEP aims to solve numerous conflicting problems for constructing new power plants. The proposed approaches are Multi-Objective Comprehensive Learning Particle Swarm Optimization (MOCLPSO) and Circle Search algorithm. The key objectives of the proposed method is to reduce budget, to maximize reliability and to minimize the pollutant discharge. Therefore, the apt formulations are modeled and solved to establish the conflicting facets of GEP problem. This paper implements MOCLPSO algorithm to solve Multi-Objective GEP (MOGEP) problem for 7-year and 14-year planning horizon. By then, the proposed model is implemented at MATLAB/Simulink platform and the implementation is calculated. The proposed method shows better results in all approaches like Seagull Optimization Algorithm (SOA), Particle Swarm Optimization (PSO) and Cuckoo Search algorithm. The outcomes establish the competence of MOCLPSO and Circle Search Algorithm to offer good-ranged Pareto optimal non-dominated solutions.

Assessing the Potential of Hybrid-Based Metaheuristic Algorithms Integrated with ANNs for Accurate Reference Evapotranspiration Forecasting

Evapotranspiration (ETo) is one of the most important processes in the hydrologic cycle, with specific application to sustainable water resource management. As such, this study aims to evaluate the predictive ability of a novel method for monthly ETo estimation, using a hybrid model comprising data pre-processing and an artificial neural network (ANN), integrated with the hybrid particle swarm optimisation–grey wolf optimiser algorithm (PSOGWO). Monthly data from Al-Kut City, Iraq, over the period 1990 to 2020, were used for model training, testing, and validation. The predictive accuracy of the proposed model was compared with other cutting-edge algorithms, including the slime mould algorithm (SMA), the marine predators algorithm (MPA), and the constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithm (CPSOCGSA). A number of graphical methods and statistical criteria were used to evaluate the models, including root mean squared error (RMSE), Nash–Sutcliffe model efficiency (NSE), coefficient of determination (R2), maximum absolute error (MAE), and normalised mean standard error (NMSE). The results revealed that all the models are efficient, with high simulation levels. The PSOGWO–ANN model is slightly better than the other approaches, with an R2 = 0.977, MAE = 0.1445, and RMSE = 0.078. Due to its high predictive accuracy and low error, the proposed hybrid model can be considered a promising technique.

Application of improved multi objective particle swarm optimization and harmony search in highway engineering

Highway engineering construction is a complex multi-objective management project, and the traditional multi-objective task management approach has been difficult to cope with the uniqueness of highway engineering construction management. In order to improve the efficiency of multi-objective management in highway engineering, the study proposes to construct a multi-objective management model using particle swarm and harmony search algorithms for highway engineering management. First of all, the study first explores the management indexes in highway engineering, and constructs the functional relationship according to the selected indexes. Then the particle swarm algorithm is used to optimize the database of the harmonic search algorithm, and the harmony search algorithm can be based on the optimized database to find the optimal solution for objective management. The experimental results show that the improved particle swarm and harmony search algorithm has good distribution and convergence. After the optimization of the proposed model, the multi-objective highway project management scheme is 39% ahead of schedule, 17% savings in input funds, and 2.56% and 3.56% improvement in highway quality indexes and ecological environment management indexes, respectively, compared with the original scheme. Therefore, the model has a good application prospect to formulate the optimal multi-objective management scheme with excellent performance to improve the management efficiency of highway engineering.

Examination of Single- and Hybrid-Based Metaheuristic Algorithms in ANN Reference Evapotranspiration Estimating

Hydrological resource management, including crop watering and irrigation scheduling, relies on reliable estimates of reference evapotranspiration (ETo). However, previous studies of forecasting ETo have not dealt with comparing single and hybrid metaheuristic algorithms in much detail. This study aims to assess the efficiency of a novel methodology to simulate univariate monthly ETo estimates using an artificial neural network (ANN) integrated with the hybrid particle swarm optimisation–grey wolf optimiser algorithm (PSOGWO). Several state-of-the-art algorithms, including constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithms (CPSOCGSA), the slime mould algorithm (SMA), the marine predators algorithm (MPA) and the modified PSO algorithm were used to evaluate PSOGWO’s prediction accuracy. Monthly meteorological data were collected in Al-Kut City (1990 to 2020) and used for model training, testing and validation. The results indicate that pre-processing techniques can improve raw data quality and may also suggest the best predictors scenario. That said, all models can be considered efficient with acceptable simulation levels. However, the PSOGWO-ANN model slightly outperformed the other techniques based on several statistical tests (e.g., a coefficient of determination of 0.99). The findings can contribute to better management of water resources in Al-Kut City, an agricultural region that produces wheat in Iraq and is under the stress of climate change.

Implementation of Evolutionary Algorithms to Parametric Identification of Gradient Flexible Plate Structure

This paper focused on modelling of a gradient flexible plate system utilizing an evolutionary algorithm, namely particle swarm optimization (PSO) and cuckoo search (CS) algorithm. A square aluminium plate experimental rig with a gradient of 30° and all edges clamped were designed and fabricated to acquire input-output vibration data experimentally. This input-output data was then applied in a system identification method, which used an evolutionary algorithm with a linear autoregressive with exogenous (ARX) model structure to generate a dynamic model of the system. The obtained results were then compared with the conventional method that is recursive least square (RLS). The developed models were evaluated based on the lowest mean square error (MSE), within the 95% confidence level of both auto and cross-correlation tests as well as high stability in the pole-zero diagram. Investigation of results indicates that both evolutionary algorithms provide lower MSE than RLS. It is demonstrated that intelligence algorithms, PSO and CS outperformed the conventional algorithm by 85% and 89%, respectively. However, in terms of the overall assessment, model order 4 by the CS algorithm was selected to be the ideal model in representing the dynamic modelling of the system since it had the lowest MSE value, which fell inside the 95% confidence threshold, indicating unbiasedness and stability.

Band selection using hybridization of particle swarm optimization and crow search algorithm for hyperspectral data classification

Band selection using hybridization of particle swarm optimization and crow search algorithm for hyperspectral data classification

Optimizing Photovoltaic Power Production in Partial Shading Conditions Using Dandelion Optimizer (DO)-Based MPPT Method

This research proposes the dandelion optimizer (DO), a bioinspired stochastic optimization technique, as a solution for achieving maximum power point tracking (MPPT) in photovoltaic (PV) arrays under partial shading (PS) conditions. In such scenarios, the overall power output of the PV array is adversely affected, with shaded cells generating less power and consuming power themselves, resulting in reduced efficiency and local hotspots. While bypass diodes can be employed to mitigate these effects by redirecting current around shaded cells, they may cause multiple peaks, making MPPT challenging. Therefore, metaheuristic algorithms are suggested to effectively optimize power output and handle multiple peaks. The DO algorithm draws inspiration from the long-distance movement of a dandelion seed, which relies on the force of the wind. By utilizing this bioinspired approach, the DO algorithm can successfully capture the maximum power point (MPP) under different partial shading scenarios, where traditional MPPT algorithms often struggle. An essential contribution of this research lies in the examination of the performance of the proposed algorithm through simulation and real-time hardware-in-the-loop (HIL) results. Comparing the DO algorithm with the state-of-the-art algorithms, including particle swarm optimization (PSO) and cuckoo search (CS), the DO algorithm outperforms them in terms of power tracking efficiency, tracking duration, and the maximum power tracked. Based on the real-time HIL results, the DO algorithm achieves the highest average efficiency at 99.60%, surpassing CS at 96.46% and PSO at 94.74%. These findings demonstrate the effectiveness of the DO algorithm in enhancing the performance of MPPT in PV arrays, particularly in challenging partial shading conditions.

A Comparative Analysis of OLSR Routing Protocol based on PSO and Cuckoo Search Optimization (CSO) in Manets

New developments in wireless communication have enabled the use of highly efficient and inexpensive wireless receivers in a variety of portable applications. Each node in a mobile network is a mobile device that independently organizes its own connection to the others and manages its own data transmissions. The adaptability, scalability, and cost reduction of mobile networks have attracted considerable attention. Because mobile networks are constantly changing, problems with routing and power usage are common. High error rates, energy limitations, and inadequate bandwidth are just a few of the issues plaguing mobile ad hoc networks. The relevance of routing protocols in dynamic multi-hop networks like Mobile Ad hoc Networks (MANET) has drawn the attention of many scholars. In this paper, we focus on implementing an OLSR(Optimised Link State Routing) protocol and evaluates its performance using two optmisation algorithm: Particle Swarm Optimization(OLSR) and Cuckoo Search Optimization (CSO). The simulation result suggests that PSO is superior to both CSO and the conventional OLSR routing technique. We implemented using NS-2 simulator for simulation and NAM for network animation.

A swarming neural network computing approach to solve the Zika virus model

In this work, a swarming computational procedure is presented for the numerical treatment of the dynamical model of the susceptible, exposed, infected, and recovered (SEIR) classes that portrayed the spreading of Zika virus. The artificial neural network procedures (ANNPs) have been applied to solve the SEIR mathematical model for spreading of the Zika virus together with the hybridization efficiency of global swarming and local search schemes. The global particle swarm optimization (PSO) and local search active-set algorithm (ASA) have been proposed to solve the model. An error based objective function is presented for the SEIR differential model and then optimized by the hybrid computing efficiency of PSO-ASA. Five neurons, fifteen variables of each class and ten numbers of trials have been used to solve the SEIR mathematical model for spreading of the Zika virus. The correctness of the proposed computing ANNPs-PSO-ASA is observed by using the comparison of the obtained and reference solutions along with the performances of the absolute error, ranges around 10−06 to 10−08. The reliability of the designed computing ANNPs-PSO-ASA technique is observed by using the statistical operator performances on single/multiple trials for the SEIR system for spreading of the Zika virus dynamics.

Detection of Diabetes through Microarray Genes with Enhancement of Classifiers Performance.

Diabetes is a life-threatening, non-communicable disease. Diabetes mellitus is a prevalent chronic disease with a significant global impact. The timely detection of diabetes in patients is necessary for an effective treatment. The primary objective of this study is to propose a novel approach for identifying type II diabetes mellitus using microarray gene data. Specifically, our research focuses on the performance enhancement of methods for detecting diabetes. Four different Dimensionality Reduction techniques, Detrend Fluctuation Analysis (DFA), the Chi-square probability density function (Chi2pdf), the Firefly algorithm, and Cuckoo Search, are used to reduce high dimensional data. Metaheuristic algorithms like Particle Swarm Optimization (PSO) and Harmonic Search (HS) are used for feature selection. Seven classifiers, Non-Linear Regression (NLR), Linear Regression (LR), Logistics Regression (LoR), Gaussian Mixture Model (GMM), Bayesian Linear Discriminant Classifier (BLDC), Softmax Discriminant Classifier (SDC), and Support Vector Machine-Radial Basis Function (SVM-RBF), are utilized to classify the diabetic and non-diabetic classes. The classifiers' performances are analyzed through parameters such as accuracy, recall, precision, F1 score, error rate, Matthews Correlation Coefficient (MCC), Jaccard metric, and kappa. The SVM (RBF) classifier with the Chi2pdf Dimensionality Reduction technique with a PSO feature selection method attained a high accuracy of 91% with a Kappa of 0.7961, outperforming all of the other classifiers.

Evaluation and Exploration of Machine Learning and Convolutional Neural Network Classifiers in Detection of Lung Cancer from Microarray Gene-A Paradigm Shift.

Microarray gene expression-based detection and classification of medical conditions have been prominent in research studies over the past few decades. However, extracting relevant data from the high-volume microarray gene expression with inherent nonlinearity and inseparable noise components raises significant challenges during data classification and disease detection. The dataset used for the research is the Lung Harvard 2 Dataset (LH2) which consists of 150 Adenocarcinoma subjects and 31 Mesothelioma subjects. The paper proposes a two-level strategy involving feature extraction and selection methods before the classification step. The feature extraction step utilizes Short Term Fourier Transform (STFT), and the feature selection step employs Particle Swarm Optimization (PSO) and Harmonic Search (HS) metaheuristic methods. The classifiers employed are Nonlinear Regression, Gaussian Mixture Model, Softmax Discriminant, Naive Bayes, SVM (Linear), SVM (Polynomial), and SVM (RBF). The two-level extracted relevant features are compared with raw data classification results, including Convolutional Neural Network (CNN) methodology. Among the methods, STFT with PSO feature selection and SVM (RBF) classifier produced the highest accuracy of 94.47%.