Modified Particle Swarm Optimization Research Articles

Blockchain for urban governance: Enhancing trust in smart city systems with advanced techniques

The rapid urbanization and increasing energy demands necessitate innovative solutions for efficient energy governance in smart cities from the social science perspective. This paper introduces a comprehensive framework for Urban Energy Governance (UEG), focusing on the collaboration of three urban systems in energy exchange considering social and environmental impacts. To address the challenges of security, energy forecasting, and nonlinear optimization, we employ advanced techniques, including blockchain technology, Long Short-Term Memory (LSTM) networks for accurate energy forecasting, and a modified Particle Swarm Optimization (PSO) algorithm for solving the proposed nonlinear optimization problem. The utilization of blockchain enhances the security and transparency of energy transactions, fostering trust among stakeholders. Additionally, LSTM models leverage historical data to provide precise energy forecasts, optimizing resource utilization. The modified PSO algorithm tailors optimization to the specific nonlinearities of the proposed problem, ensuring efficient and effective decision-making in the context of Urban Energy Governance. Through a multi-faceted approach, our framework contributes to the advancement of smart city systems, providing a secure, forecast-driven, and optimized energy governance model for sustainable urban development.

Optimization of sustainable distributed power generation in distribution networks: a comprehensive analysis of location, capacity, and environmental impact

With the increasing integration of distributed generation (DG) into distribution networks, the impact of its output and configuration on the environment and overall benefits has become more pronounced. To enhance environmental benefits while minimizing costs, an optimization model for DG location and capacity considering environmental benefits (LCEB) is proposed. Initially, a mathematical model for DG location and capacity is established, with optimization objectives including voltage deviation, network loss, and environmental benefit. Subsequently, modified particle swarm optimization (MPSO) with a single-dimension search probability mutation is employed to solve this model, obtaining an optimized DG output and layout scheme. Finally, the feasibility and effectiveness of the proposed model are demonstrated through simulation and analysis by using the IEEE33 node system as an example. The results highlight the model’s capability to improve environmental and economic benefits, enhance voltage levels, and reduce network losses within the distribution network.

Efficient Resource Scheduling in Fog: A Multi-Objective Optimization Approach

Fog computing is a novel idea that extends cloud computing by offering services like processing, storage, analysis, and networking on fog devices closer to IoT devices. Numerous fog devices are required to process the ever-growing amount of data generated by IoT applications. The heterogeneous tasks from various IoT applications compete for a limited number of resources of these devices. The process of assigning this set of tasks to different available fog nodes according to QoS requirements for processing is resource scheduling. Resource scheduling aims to optimize resource utilization and performance metrics however, the dynamic nature of the Fog environment, resource-constrained, and heterogeneity in fog devices make resource scheduling a complex issue. This research presents the design and implementation of a multi-objective optimization-based resource scheduling algorithm using Modified Particle Swarm Optimization (MPSO) that addresses the application module placement and task allocation issues. This two-step MPSO-based resource scheduling model finds the optimal fog node to place each application module and assigns appropriate tasks to the most optimal fog nodes for execution. The proposed model unlocks the full potential of fog resources along with maximization of overall system performance in terms of optimization of cost, latency, energy consumption, and network usage. The simulation results indicate that using MPSO energy consumption is reduced by 53.94% and 43.58% as compared to First Come First Serve (FCFS) and Particle Swarm Optimization (PSO), respectively. The loop delay, network usage and cost using MPSO are reduced by 40.3%, 67.69% and 90.01% respectively, as compared to PSO algorithm.

A modified particle swarm optimization rat search algorithm and its engineering application.

Solar energy generation requires photovoltaic (PV) systems to be optimised, regulated, and simulated with efficiency. The performance of PV systems is greatly impacted by the fluctuation and occasionally restricted accessibility of model parameters, which makes it difficult to identify these characteristics over time. To extract the features of solar modules and build highly accurate models for PV system modelling, control, and optimisation, current-voltage data collecting is essential. To overcome these difficulties, the modified particle swarm optimization rat search algorithm is presented in this manuscript. The modified rat search algorithm is incorporated to increase the PSO algorithm's accuracy and efficiency, which leads to better outcomes. The RSA mechanism increases both the population's diversity and the quality of exploration. For triple diode model of both monocrystalline and polycrystalline, PSORSA has showed exceptional performance in comparison to other algorithm i.e. RMSE for monocrystalline is 3.21E-11 and for polycrystalline is 1.86E-11. Similar performance can be observed from the PSORSA for four diode model i.e. RMSE for monocrystalline is 4.14E-09 and for polycrystalline is 4.72E-09. The findings show that PSORSA outperforms the most advanced techniques in terms of output, accuracy, and dependability. As a result, PSORSA proves to be a trustworthy instrument for assessing solar cell and PV module data.

Efficient microgrid energy management with neural-fuzzy optimization

This research introduces a pioneering Energy Management System (EMS) for microgrids, integrating fuzzy neural networks and a modified particle swarm optimization (MPSO) algorithm. The key contribution lies in minimizing production costs while optimizing the use of renewable sources like photovoltaic (PV), wind turbines (WT), and energy storage. The novel approach considers time-dependent constraints, ensuring adaptability and superior system performance. Additionally, the study introduces an innovative demand response (DR) analysis using a neural-fuzzy network, enhancing customer response and energy cost dynamics. The MPSO algorithm addresses economic load distribution challenges, demonstrating superior performance in comparative analysis. This integrated approach offers a groundbreaking solution for sustainable and efficient energy planning in microgrids. The analysis demonstrates that the proposed method achieves higher energy savings (83%) compared to baseline levels of 72%, showcasing its superior efficiency. Comparative analysis with genetic and particle swarm optimization algorithms reveals consistently lower average expenses and increased cost-effectiveness with the proposed approach.

Prediction of multiple transverse cracks in a composite beam using hybrid RNN-mPSO technique

The presence of faults is common in engineering composite structures. Precise detection of faults prior to failure has been the focal point for researchers working in this domain. Detection of damage from the modal characteristics is an age-old promising technique besides the artificial intelligence techniques such as neural network (NN), Artificial neural network (ANN), recurrent neural network (RNN), and particle swarm optimization (PSO) methods. However, multiple detection techniques combined can give better results. In this regard, recurrent neural networks (RNN) and modified particle swarm optimization (mPSO) are combined to detect damages in a glass fiber reinforced polymer (GFRP) composite cantilever beam. The proposed hybrid model consists of two stages; indices of relative natural frequencies applied to locate crack parameters (position and severity) by recurrent neural network technique and optimization of the results (position and severity) by mPSO. The results illustrate this method can be implemented to predict multiple transverse cracks and their parameters in a composite beam, manifested in the change of natural frequencies where the error is limited to 0.97% only.

Investigation of Surface-Catalycity Effects on Hypersonic Glide Vehicle Trajectory Optimization

The optimization of a hypersonic glide vehicle’s reachable domain is studied using different models to constrain the stagnation-point heat transfer. The vehicle is modeled as a high-lift common aero vehicle that uses bank-angle modulation for crossrange maneuvering with a leading-edge thermal protection system made of an ultra-high-temperature ceramic. A constrained nonlinear optimization problem is formulated to determine the range of feasible trajectories that satisfy aerodynamic heating and loading constraints. The optimal control profile is determined using a modified particle swarm optimization routine that employs a penalty function approach to handle path and terminal constraints. Baseline trajectories are determined using an existing convective heat-flux correlation, and the effect of surface catalycity on the optimized trajectories is investigated by applying a correction factor, derived from high-fidelity computational fluid dynamics simulations, to the heat-flux correlation. Results demonstrate a high sensitivity of the optimized trajectories to the underlying aerothermodynamics model such that accounting for surface catalycity expands the reachability by an order of magnitude.

A Multiobjective Array Beamforming Method for Arrays of Flexible Shape

High-performance beamforming incorporating multiple objectives for large-scale antenna arrays becomes increasingly important to improve the capacity and efficiency of wireless communication systems. The speed of synthesizing a desired beam pattern is critical in wireless communications systems to adapt to highly dynamic wireless channels. A modified particle swarm optimization (PSO) algorithm for synthesizing array beam patterns is proposed in this study. The initial positions of particles in PSO are designated following a Taylor distribution instead of being given uniformly distributed random values as in the classical PSO algorithm. The fitness functions are defined to include multiple objectives represented by producing multiple main lobes with customized deep and broadened nulls. Several scenarios have been established to examine the feasibility of the proposed algorithm. Moreover, the performance of the proposed algorithm is compared with those of the ones based on the classical PSO. A significant performance improvement for obtaining beamforming coefficients has been achieved. The robustness of the proposed algorithm is demonstrated further by applying it to a finite array on a curved surface for beamforming.

Ensemble strategy using particle swarm optimisation variant and enhanced local search capability

Particle swarm optimisation is a population-based algorithm for evolutionary computation. A notable recent research direction has been to combine different effective mechanisms to enhance both exploration and exploitation capabilities while employing suitable mechanisms at appropriate instances in the evolutionary process. This study entailed the development of an ensemble strategy that uses a variant of the modified particle swarm optimisation algorithm with a covariance matrix adapted to the retreat phase and sequential quadratic programming. The modified particle swarm optimisation algorithm employs nonlinear population size reduction and uses the candidate elite best solution as the stochastic learning strategy and the fitness-distance balance as the terminal updating mechanism. The proposed algorithm was compared with the most recently proposed particle swarm optimisation-based variants through testing on CEC2017 benchmark functions. In the experimental results, the proposed method achieved the best ranking and exhibited excellent performance. Further effectiveness tests demonstrated that the proposed combination of algorithms and mechanisms exhibits tacit cooperation and significantly improves performance.

Impact of initialization of a modified particle swarm optimization on cooperative source searching

Swarm robotic is well known for its flexibility, scalability and robustness that make it suitable for solving many real-world problems. Source searching which is characterized by complex operation due to the spatial characteristic of the source intensity distribution, uncertain searching environments and rigid searching constraints is an example of application where swarm robotics can be applied. Particle swarm optimization (PSO) is one of the famous algorithms have been used for source searching where its effectiveness depends on several factors. Improper parameter selection may lead to a premature convergence and thus robots will fail (i.e., low success rate) to locate the source within the given searching constraints. Additionally, target overshooting and improper initialization strategies may lead to a nonoptimal (i.e., take longer time to converge) target searching. In this study, a modified PSO and three different initializations strategies (i.e., random, equidistant and centralized) were proposed. The findings shown that the proposed PSO model successfully reduce the target overshooting by choosing optimal PSO parameters and has better convergence rate and success rate compared to the benchmark algorithms. Additionally, the findings also indicate that the random initialization give better searching success compared to equidistant and centralize initialization.

Text Document Clustering Using Modified Particle Swarm Optimization with k-means Model

In the present digital era, vast amounts of data are generated by millions of Internet users in the form of unstructured text documents. The clustering and organizing of text documents play a crucial role in the applications of data analysis and market research. In this research manuscript, a new modified version of metaheuristic-based optimization technique is proposed with k-means for clustering the text documents. In the initial phase, the input data are acquired from the three-benchmark databases such as Reuters-21578, 20-Newsgroup and British Broadcasting Corporation (BBC)-sport. Further, the data denoising is accomplished by using the common techniques: stemming, lemmatization, tokenization, and stop word removal. In addition to this, the denoised data are transformed into feature vectors by utilizing Term Frequency (TF)-Inverse Document Frequency (IDF) technique. The computed feature vectors are given to the Modified Particle Swarm Optimization (MPSO) with k-means to group the closely related text documents by minimizing the similarity in different clusters. The experimental examination showed that the proposed MPSO with k-means model achieved accuracy of 0.85, 0.85 and 0.86 on the Reuters-21578, 20-Newsgroup and BBC-sport databases, which are superior to the comparative models.

A modified particle swarm optimization algorithm in a rolling horizon framework for the aggregate production planning problem: pharmaceutical industry case

A modified particle swarm optimization algorithm in a rolling horizon framework for the aggregate production planning problem: pharmaceutical industry case

Improved particle swarm optimization with reverse learning and neighbor adjustment for space surveillance network task scheduling

Space Surveillance Network (SSN) plays a crucial role in Space Domain Awareness (SDA) as it helps to maintain the catalog of Resident Space Objects (RSOs). Howe`ver, as the population of RSOs continues to grow, the contradiction between the increasing number of RSOs and limited sensor resources will be further apparent. How to effectively utilize existing sensor resources becomes a vital concern. This paper focuses on the Multi-sensor Collaborative Observation Scheduling (MCOS) in SSN. The workflow of SSN is summarized, and a Mathematical Programming Model (MPM) is established to maximize observation profit. Given the complexity of MPM, a modified Particle Swarm Optimization is proposed by combining Reverse learning, Neighbor adjustment, and Particle perturbation (RNP-PSO). In the MPM, multiple constraints such as observation time for each RSO, observation frequency, and sensor capability are considered, enabling a comprehensive description of the MCOS problem for the first time. The RNP-PSO incorporates a reverse learning algorithm to improve the quality of the initial population. Additionally, continuous mapping is added to the neighbor adjustment process to enhance the algorithm's search capabilities. Furthermore, a particle perturbation strategy is proposed to increase the diversity of solutions and avoid local optima. A series of experiments have been conducted to evaluate the performance of the RNP-PSO algorithm in solving the MCOS problem. The results demonstrate that compared to several other effective algorithms, the RNP-PSO algorithm significantly improves the Task Completion Rate (TCR) by approximately 17.01%, 19.1%, 13.53%, and 13.99%, while simultaneously reduces the Resource Consumption Rate (RCR) by about 55.48% and 36.38%. The experiments also include strategy and parameter sensitivity verification, which thoroughly validate the performance of the RNP-PSO algorithm in various aspects.

Decision-making for sustainable urban transportation: A statistical exploration of innovative mobility solutions and reduced emissions

This research aims to investigate sustainable urban transportation decision-making through innovative mobility solutions, aiming to curtail emissions. To this end, this research presents a smart stochastic architecture to optimize renewable energy-dependent systems, addressing challenges in networked microgrids. By integrating electric vehicle (EV) charging demands and harnessing the unpredictability of renewable energy resources into the urban networks, the research introduces a vehicle-to-grid layout and a novel machine learning-enabled probabilistic Technique. These advancements cover the way for stable, sustainable, and efficient urban transportation systems with reduced environmental impact. A smart stochastic architecture to optimize the operation and management of such systems that rely heavily on renewable energy is proposed in this study, taking into account the poor reliability and complicated energy management in networked microgrids (MGs). The stochastic and variable charging EVs and also the unpredictability of renewable energy resources (RERs) are incorporated in the suggested layout. A vehicle to grid layout, which has become suitable for the MG cost function, is being used for mitigating adverse impacts of vehicles on MGs. In order to support the use of RERs, this study proposes a new machine learning-enabled probabilistic method utilizing support vector machine and point estimation methods for keeping the system in a stable state taking into account their random behavior. Furthermore, this paper develops a novel optimization algorithm using modified particle swarm optimization to determine what is optimal. IEEE systems evaluate the suitability of the suggested smart layout.

Spectral reflectance reconstruction based on wideband multi-illuminant imaging and a modified particle swarm optimization algorithm.

A method for spectral reflectance factor reconstruction based on wideband multi-illuminant imaging was proposed, using a programmable LED lighting system and modified Bare Bones Particle Swarm Optimization algorithms. From a set of 16 LEDs with different spectral power distributions, nine light sources with correlated color temperatures in the range of 1924 K - 15746 K, most of them daylight simulators, were generated. Samples from three color charts (X-Rite ColorChecker Digital SG, SCOCIE ScoColor paint chart, and SCOCIE ScoColor textile chart), were captured by a color industrial camera under the nine light sources, and used in sequence as training and/or testing colors. The spectral reconstruction models achieved under multi-illuminant imaging were trained and tested using the canonical Bare Bones Particle Swarm Optimization and its proposed modifications, along with six additional and commonly used algorithms. The impacts of different illuminants, illuminant combinations, algorithms, and training colors on reconstruction accuracy were studied comprehensively. The results indicated that training colors covering larger regions of color space give more accurate reconstructions of spectral reflectance factors, and combinations of two illuminants with a large difference of correlated color temperature achieve more than twice the accuracy of that under a single illuminant. Specifically, the average reconstruction error by the method proposed in this paper for patches from two color charts under A + D90 light sources was 0.94 and 1.08 CIEDE2000 color difference units. The results of the experiment also confirmed that some reconstruction algorithms are unsuitable for predicting spectral reflectance factors from multi-illuminant images due to the complexity of optimization problems and insufficient accuracy. The proposed reconstruction method has many advantages, such as being simple in operation, with no requirement of prior knowledge, and easy to implement in non-contact color measurement and color reproduction devices.

Comparison of the MPPT command based on the PSO, P&O, and IncCond algorithm implemented in Dspace

Several algorithms have been offered to track the maximum power point when we have one maximum power point. In this paper, we will propose an improved maximum power point tracking method for the photovoltaic system that utilizes a modified PSO algorithm. The main advantage of the method is the decreasing of the steadystate oscillation (to practically zero) once the maximum power point is located. moreover, the proposed method has the ability to track the maximum power point under extreme environmental conditions. To evaluate the effectiveness of the proposed method, MATLAB simulations are carried out under very challenging circumstances, namely step changes in irradiance and step changes in load. Finally, its performance is compared with the «perturbation and observation” and incremental conductance. The results obtained were validated by a PIL (Processor In the Loop) co-simulation using the DSpace 1104 card.

Differential evolution for cleft lip and/or cleft palate patient treatment scheduling problems: a northern Thailand hospital case study

Cleft lip and/or cleft palate (CL/P) are the common birth defects that result when facial structures developing in an unborn baby do not close completely. To design for treatment schedule, some constraints including hospital eligibility constraints, capacity limitations, treatment age limitations, multi-hospital assignment, and multidisciplinary care team assignment should be determined. However, efficient treatment scheduling is difficult owing to the complicated conditions of specific treatment. This paper presents a multi-objective mathematical model of the CL/P patient treatment scheduling problem in order to minimize the makespan, travel distance, and total least preference assignment score. Since the problem is NP-hard, a solution method is developed based on differential evolution (DE) with particular encoding and decoding schemes for solving the CL/P patient treatment scheduling problem. The performance of DE is evaluated and compared the results with those obtained from the modified particle swarm optimization. The results show that DE is capable of finding high-quality solutions with fast convergence. To apply the proposed approach for a case study, the CL/P patient treatment scheduling program is formulated. The program can be a decision support system in helping the administrators to schedule the patients in order to identify a list of selected treatments, assign each operation of patients to the selected hospital, and intelligently identify the period of the selected treatments.

Optimized resource allocation in cloud computing for enhanced performance with modified particle swarm optimization

Cloud Computing (CC) offers abundant resources and diverse services for running a wide range of consumer applications, although it faces specific issues that need attention. Cloud customers aim to choose the most suitable resource that fulfills the requirements of consumers at a fair cost and within an acceptable timeframe; however, at times, they wind up paying more for a shorter duration. Many advanced algorithms focus on optimizing a single variable individually. Hence, an Optimized Resource Allocation in Cloud Computing (ORA-CC) Model is required to achieve equilibrium between opposing aims in Cloud Computing. The ORA-CC study aims to create a task processing structure with the decision-making ability to choose the best resource in real-time for handling diverse and complicated uses on Virtual Computers (VC). It will utilize a Modified Particle Swarm Optimization (MoPSO) method to meet a deadline set by the user. The fitness value is calculated by combining a base value with the enhanced estimation of resources based on the ORA-CC algorithm to create a robust arrangement. The ORA-CC technique's effectiveness is evaluated by contrasting it with a few current multi-objective restrictions applied to machine scheduling strategies utilizing the Cloudsim simulation. The comparison demonstrates that the suggested ORA-CC strategy offers more efficient resource allocation than other techniques.

Enhanced particle swarm optimization and P&O for MPPT of photovoltaic systems under partial shading conditions

Although Photovoltaic Technologies are largely deployed as a renewable energy source, several factors affect their performance. The major factors that affect PV performance are changes in irradiance and temperature. Maximum PowerPoint Tracking of PV output is essential in giving the maximum photovoltaic outputs at variable levels. Instantaneous variation in irradiance and temperature increases the complexity of tracking maximum power points. Partial shading conditions resulting from shade from trees, tall buildings, and Cloud formation amongst others greatly affect PV systems, especially in large Photovoltaic systems. Under the Partial shading condition, P-V curves become more complex as it is characterized by multiple peaks. The conventional PSO is associated with less accuracy in tracking the Global Maximum Power Point (Global MPP) and slow convergence time in obtaining the Global MPPT and oscillations. In this thesis, a modified Particle Swarm Optimization based Maximum Power Point Tracking technique is designed in MATLAB/Simulink to track the global Maximum Power Point of a Photovoltaic system under partial shading. The proposed modified PSO combines conventional PSO and P&O methods. The particle position in the PSO method is given as the duty cycle value d of the DC-DC converter. Conventional PSO equations are used to update the velocities and duty cycle. Thereafter, the maximum velocity and duty cycle are perturbed to reduce convergence time. The designed PV system was simulated in a MATLAB environment for 10 different irradiation levels and results were compared with results from related works. The average convergence time was 0.99 seconds and efficiency was up to 99.8% with the proposed model which performed better than conventional methods.

Cloud computing load prediction by decomposition reinforced attention long short-term memory network optimized by modified particle swarm optimization algorithm

Cloud computing load prediction by decomposition reinforced attention long short-term memory network optimized by modified particle swarm optimization algorithm