Sorting Particle Swarm Optimization Research Articles

Design and stability performance optimization of a novel hybrid inspection robot walking device for smart grid applications

Inspecting in high-altitude windy environments requires maintaining a stable attitude to avoid significant oscillations, which is a crucial factor in advancing the use of hybrid power transmission line inspection robots (PTLIRs) in real-world scenarios for the electrical power and energy industry. We developed and optimized a novel walking device to improve the safety of hybrid PTLIRs operating in strong wind conditions. First, we investigate the operational conditions of transmission line inspection and design a walking device capable of transporting a flight device to be suspended under the line for inspection purposes. Second, a force equilibrium analysis is performed to explore the relationship between the key structural parameters of the walking device and the wind-induced deflection of the robot. A multiobjective optimization model is formulated to minimize the maximum deflection angle of the robot along three axes under wind loads. Finally, an optimization algorithm, using an improved non-dominated sorting particle swarm optimization (NSPSO) approach, is proposed to solve the model with good global search ability and fast convergence speed. A test platform is constructed to collect the wind deflection angle of a self-developed hybrid PTLIR under the transmission line. The test results demonstrate that the walking device can carry 30 kg to move along the transmission line. The average deflection angle of the robot around the Z axis is the highest under the influence of wind force level 7 (wind speed of 15.5 m/s) perpendicular to the windward surface of the robot. After optimization, the maximum deflection angle is limited to 10.38°, aligning within safety thresholds. The optimization algorithm effectively reduces the maximum deflection angles around the X, Z, and Y axes by 33.19 %, 39.21 %, and 13.23 %, with minimal average errors of 2.40 %, 3.87 %, and 2.13 %, respectively. The research in this paper can solve the bottleneck problem of robot practicality, which has important theoretical significance and practical application value for the safety, stability, and intelligent operation and maintenance management of power transmission lines.

EEG-Based Detection of Mild Cognitive Impairment Using DWT-Based Features and Optimization Methods.

In recent years, electroencephalography (EEG) has been investigated for identifying brain disorders. This technique involves placing multiple electrodes (channels) on the scalp to measure the brain's activities. This study focuses on accurately detecting mild cognitive impairment (MCI) from the recorded EEG signals. To achieve this, this study first introduced discrete wavelet transform (DWT)-based approaches to generate reliable biomarkers for MCI. These approaches decompose each channel's signal using DWT into a set of distinct frequency band signals, then extract features using a non-linear measure such as band power, energy, or entropy. Various machine learning approaches then classify the generated features. We investigated these methods on EEGs recorded using 19 channels from 29 MCI patients and 32 healthy subjects. In the second step, the study explored the possibility of decreasing the number of EEG channels while preserving, or even enhancing, classification accuracy. We employed multi-objective optimization techniques, such as the non-dominated sorting genetic algorithm (NSGA) and particle swarm optimization (PSO), to achieve this. The results show that the generated DWT-based features resulted in high full-channel classification accuracy scores. Furthermore, selecting fewer channels carefully leads to better accuracy scores. For instance, with a DWT-based approach, the full-channel accuracy achieved was 99.84%. With only four channels selected by NSGA-II, NSGA-III, or PSO, the accuracy increased to 99.97%. Furthermore, NSGA-II selects five channels, achieving an accuracy of 100%. The results show that the suggested DWT-based approaches are promising to detect MCI, and picking the most useful EEG channels makes the accuracy even higher. The use of a small number of electrodes paves the way for EEG-based diagnosis in clinical practice.

Non-dominated sorting simplified swarm optimization for multi-objective omni-channel of pollution-routing problem

Abstract The activities of the traffic department mainly contribute to the generation of greenhouse gas (GHG) emissions. The swift expansion of the traffic department results in a significant increase in global pollution levels, adversely affecting human health. To address GHG emissions and propose impactful solutions for reducing fuel consumption in vehicles, environmental considerations are integrated with the core principles of the vehicle routing problem. This integration gives rise to the pollution-routing problem (PRP), which aims to optimize routing decisions with a focus on minimizing environmental impact. At the same time, the retail distribution system explores the use of an omni-channel approach as a transportation mode adopted in this study. The objectives of this research include minimizing total travel costs and fuel consumption while aiming to reduce GHG emissions, promoting environmental sustainability, and enhancing the convenience of shopping and pickup for customers through the integration of online and offline modes. This problem is NP-hard; therefore, the Non-dominated Sorting Simplified Swarm Optimization (NSSO) algorithm is employed. NSSO combines the non-dominated technique of Non-dominated Sorting Genetic Algorithm II (NSGA-II) with the update mechanism of Simplified Swarm Optimization to obtain a set of Pareto-optimal solutions. Moreover, the NSSO, a multi-objective evolutionary algorithm, is adopted to address multi-objective problems. The PRP benchmark dataset is utilized, and the results are compared with two other multi-objective evolutionary algorithms: NSGA-II and Non-dominated Sorting Particle Swarm Optimization (NSPSO). The findings of the study confirm that NSSO exhibits feasibility, provides good solutions, and achieves faster convergence compared with the other two algorithms, NSGA-II and NSPSO.

Optimal Location and Size of SVC and TCSC for Multi-objective Static Voltage Stability Enhancement

A Non-dominated Sorting Particle Swarm Optimization (NSPSO) is used to solve a mixed continuousdiscreet Multi-objective optimization problem witch consist of optimal location and size of Static Var Compensators (SVC) and Thyristor Controlled Series Capacitors (TCSC) in order to maximize Static Voltage Stability Margin (SVSM), reduce power losses (PL) and minimize load Voltage Deviations (VD). While finding the optimal location, thermal limits for the lines and voltage limits for the buses are taken as security constraints. The optimization is performed considering two and three objectives for various combinations of FACTS. Simulations are performed on IEEE 14 test system for optimal location and size of FACTS devices. The obtained results are very encouraging and reveal the capability of the method to generate well-distributed non-dominated Pareto front.

Passive Intelligent Kinetic External Dynamic Shade Design for Improving Indoor Comfort and Minimizing Energy Consumption

In humid subtropical climates with a green environment, windows are the most dominant envelope elements affecting indoor visual and thermal comfort and visual connection to the outdoors. This research aims to optimize a dynamic external shading system for north-facing windows in Sydney, Australia, which acts automatically in eight predefined scenarios in response to indoor comfort conditions. The method of investigation was simulating a multi-objective optimization approach using Non-dominated Sorting Particle Swarm Optimization (NSPSO) to assess visual and thermal comfort along with energy usage and view of the outside. A combination of human and sensor assessments were applied to validate the simulations. A set of sensors and High Quality (HQ) cameras fed the system input to operate the shade. Simulations and field measurements demonstrated that optimized shading scenarios brought average yearly reductions of 71.43%, 72.52%, and 1.78% in Annual Solar Exposure, Spatial Daylight Glare, and LEED Quality View, respectively, without sacrificing Daylight Autonomy. Moreover, yearly improvements of 71.77% in cooling demand were achieved. The downside of the shading system was an increase of 0.80% in heating load and 23.76% in lighting electricity, which could be a trade-off for improved comfort and energy savings. This study investigated the effect of dynamic external shade on visual and thermal comfort together with energy usage and view, which has not been investigated for southern-hemisphere dwellings. A camera-sensor-fed mechanism operated the external shade automatically, providing indoor comfort without manual operation.

Multi-furnace optimization in silicon single crystal production plants by power load scheduling

The power consumption in the process of silicon single crystal growth is huge, so the power load must be limited to a specific range to ensure the safe and stable operation on the silicon single crystal furnace. This article establishes a two-objective mathematical model for the multi-furnace optimal scheduling problem in silicon single crystal production process. The first objective is to minimize the maximum completion time of silicon single crystal growth process on multi-furnaces, and the second objective is to minimize the maximum power consumption obtained by the multi-furnace operation together. A disturbed non-dominated sorting particle swarm optimization combined with genetic algorithm (DNSGPSO) is proposed to solve the multi-objective optimization problem. Through simulation analysis of actual growth data, the effectiveness of the proposed model and the superiority of the algorithm are verified.

Tuning of renewable energy bids based on energy risk management: Enhanced microgrids with pareto‐optimal profits for the utility and prosumers

AbstractThe increasing penetration of renewable energy sources (RES) and electric vehicles (EVs) demands the building of a microgrid energy portfolio that is cost‐effective and robust against generation uncertainties (energy risk). Energy risk may trigger financial risk in the local energy market, depending on bid values, cost of generation and price of upstream grid power. In this study, a microgrid energy portfolio is built based on adjustments to both the financial and energy risks. These risks are managed in two ways: (1) by pre‐tuning and prioritizing the bid prices for wind and solar energy sources based on their relative levels of energy risk as quantified through a conditional value‐at‐risk (CVaR) approach; and (2) by co‐optimizing the conflicting profits of the utility and prosumers using non‐dominated sorting particle swarm optimization (NSPSO) to obtain a risk‐adjusted Pareto‐optimal energy mix. Thus, the utility predicts the net power balancing cost from the scheduling time horizon, thereby moderating the adverse effect that the uncertainties in renewable energy could have on the collective welfare. The proposed method is tested on a grid‐connected CIGRE low‐voltage (LV) benchmark microgrid with solar and wind sources, microturbines, and EVs. The results demonstrate that the obtained portfolio is realistic, welfare‐optimized and cost‐efficient.

Optimal prediction intervals for macroeconomic time series using chaos and evolutionary multi-objective optimization algorithms

In a first-of-its-kind study, this paper formulates the problem of estimating the prediction intervals (PIs) in a macroeconomic time series as a bi-objective optimization problem and solves it with three evolutionary algorithms namely, Non-dominated Sorting Genetic Algorithm (NSGA-II), Non-dominated Sorting Particle Swarm Optimization (NSPSO) and Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA-D). We also proposed modeling the chaos present in the time series as a preprocessor, which we called stage-1. Accordingly, we proposed 2-stage models, where stage-1 is followed by obtaining the optimal point prediction using NSGA-II/NSPSO/MOEA-D and using these point predictions to obtain PIs (stage-2). We then proposed a 3-stage hybrid, which is built on the 2-stage model, wherein the 3 rd stage also invokes NSGA-II/NSPSO/MOEA-D in order to estimate the PIs from the point predictions obtained in 2 nd stage by simultaneously and explicitly optimizing (i) prediction interval coverage probability (PICP) and (ii) prediction interval average width (PIAW). The proposed models yielded better results in terms of both PICP and PIAW compared to the state-of-the-art Lower Upper Bound Estimation Method (LUBE) with Gradient Descent (GD) and LUBE with long short-term memory (LSTM) network. The 3-stage models outperformed the 2-stage models with respect to PICP but showed similar performance in PIAW at the cost of running NSGA-II/NSPSO/MOEA-D second time. Overall, MOEA-D yielded best PIs in two datasets and NSGA-II outperformed the other two in the third dataset. But, in terms of hypervolume, in 2-stage MOEA-D produced most diverse solutions in two datasets, while NSGA-II was the winner in the third dataset.

Two-stage surrogate-based optimization of a vehicle composite subframe with modified fast flexible space-filling designs

In this article, an optimization procedure of composite structures is proposed and applied to a vehicle subframe redesign. The process is divided into two stages and surrogate models are built by Gaussian Process Regression. The modified Fast Flexible Filling designs is used to deal with the non-rectangular design region problem. And combined with the adaptive sampling method, uniform training and test points which increase the accuracy of surrogate models are obtained. The two established surrogate models are solved by non-dominated sorting genetic algorithm-II and particle swarm optimization algorithm, respectively. The redesigned subframe exhibits significantly weight reduction and performance improvement.

Improved NSPSO Algorithm and Its Application in Static Insulation Optimization of Vacuum Interrupter

For optimizing the insulation performance of the 400 V/20 kA vacuum interrupter, the non-dominated sorting particle swarm optimization algorithm is improved and combined with the finite element electric field numerical simulation method. A three-dimensional electric field optimization model is proposed to minimize the peak of the electric field intensity with the contour parameters of the electrode and shielding cover as the optimized variables. The uniformity on the electric field and the macro field enhancement factor under different stroke are analyzed to realize static insulation optimization of the vacuum interrupter.

Optimization of the Internal Roller Burnishing Process for Energy Reduction and Surface Properties

Improving the surface quality after burnishing operation has been the subject of various published investigations. Unfortunately, the trade-off analysis between the energy consumption and surface characteristics of the internal burnishing has been not addressed due to the expensive cost and huge efforts required. The objective of the present work is to optimize burnishing factors, including the spindle speed, burnishing feed, depth of penetration, and the number of rollers for minimizing the energy consumed in the burnishing time, as well as surface roughness and maximizing Rockwell hardness. An adaptive neuro-based-fuzzy inference system (ANFIS) was used to develop burnishing objectives in terms of machining parameters. The optimization outcomes were selected using an evolution algorithm, specifically the non-dominated sorting particle swarm optimization (NSPSO). The results of the proposed ANFIS models are significant and can be employed to predict response values in industrial applications. The optimization technique comprising the ANFIS and NOPSO is a powerful approach to model burnishing performances and select optimal parameters as compared to the trial and error method as well as operator experience. Finally, the optimal solution can help to achieve the improvements in the energy consumed by 16.3 %, surface roughness by 24.3 %, and Rockwell hardness by 4.0 %, as compared to the common values.

Optimization of internal burnishing operation for energy efficiency, machined quality, and noise emission

Boosting energy efficiency and machining quality are prominent solutions to achieve sustainable production for burnishing operations. In this work, an effective optimization has been performed to enhance the energy efficiency (EFb) and decrease the machining noise (MN) as well as surface roughness (SR) of the internal burnishing operation. The burnishing factors are the spindle speed (S), burnishing feed (f), burnishing depth (D), and the number of rollers (N). The burnishing trails of the hardened material labeled SCr440 have been conducted on a CNC milling machine. The adaptive neuro-based-fuzzy inference system (ANFIS) was used to construct the correlations between the process inputs and burnishing responses. The entropy approach is employed to calculate the weight of each technical objective. The non-dominated sorting particle swarm optimization (NSPSO) is utilized to determine the optimal parameters. A comprehensive model of the production cost is developed to check the effectiveness of the proposed approach. The scientific outcomes revealed that the optimal values of the S, f, D, and N are 1645 RPM, 260 mm/min, 0.08 mm, and 4, respectively. The improvements in the EFb, SR, and MN are 6.98%, 25.00%, and 2.23%, as compared to the initial values. The machining cost is saved by 6.2% at the optimal solution. Moreover, the scientific finding is a potent technical solution to enhance machining performances for the burnishing process of various components having internal holes.

Analytical approach-based optimization of the actively driven rotary turning for environmental and economic metrics considering energy footprint of materials

The current work has been performed an effective optimization to decrease the total energy consumption (Etotal) and total machining time (Ttotal) with the constraint of the average roughness for the actively driven rotary turning (ADRT) of the material labeled SKD11. The optimizing factors are the tool rotational speed (vt), depth of cut (a), feed rate (f), and workpiece speed (vw). The analytical approach was used to construct the models of the Etotal and Ttotal. The weightage principal component analysis (WPCA) was applied in conjunction with the non-dominated sorting particle swarm optimization (NSPSO) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to determine the weight values of machining responses and select the best optimal solution. The scientific findings revealed that the optimal values of the vt, a, f, and vw were 78 m/min, 0.21 mm, 0.44 mm/rev., and 98 m/min, respectively. The reductions in the Etotal and Ttotal were 16.99% and 17.78%, respectively. Moreover, the proposed models of the Etotal and Ttotal were significant and could be used to predict technical performances with acceptable accuracy. The optimization technique comprising the analytical method, NSPSO, WPCA, and TOPSIS was named as a powerful approach to obtain optimal outcomes.

Multi-response optimization of the actively driven rotary turning for energy efficiency, carbon emissions, and machining quality

Boosting energy efficiency and machining quality are prominent solutions to achieve sustainable production for turning operations. In this work, a machining condition-based optimization has been performed to decrease the total specific energy (SEC), carbon emission (CE), and average roughness (AR) of the actively driven rotary turning (ADRT) process. The processing factors are the tool rotational speed (Tv), depth of cut (a), feed rate (fr), and workpiece speed (Wv). The turning experiments of the mold material labeled SKD11 have been conducted on a CNC lathe. The regression method is employed to develop comprehensive models of the total specific energy, carbon emissions, and average roughness. The entropy approach is then applied to drive out the weight value of each ADRT response. Finally, the non-dominated sorting particle swarm optimization (NSPSO) is utilized to determine the optimal parameters. The findings indicated that the optimal values of the Tv, a, fr, and Wv are 77 m/min, 0.32 mm, 0.25 mm/rev., and 128 m/min, respectively. The SEC, AR, and CE are decreased by 18.07%, 10.46%, and 5.02%, respectively, as compared to the initial approach. Moreover, the developed active rotary turning operation can be considered as an effective technical solution to boost the machining efficiency of hardened steels.

Using following heroes operation in multi-objective differential evolution for fast convergence

In this study, a new hybrid variant of multi-objective differential evolution is developed to improve the extent and the speed of convergence by combining the ability of DE/rand/1 strategy (NSDE1) to retain only better solutions in the selection operation with the ability of Following Heroes operation from adaptive social evolution algorithm to rapidly propagate the effect of the best solutions to the entire population. Such an algorithm is quite useful for fast convergence, particularly in solving computationally intensive multi-objective optimization problems for which it is highly desirable to obtain the better quality optimal solutions in a limited number of function calculations. The performance of the algorithm is first analyzed on thirty multi-objective test problems from ZDT, DTLZ, and WFG test suites for a limited number of function calculations, and the results are compared with four well-established multi-objective optimization algorithms: real-coded elitist non-dominated sorting genetic algorithm (RNSGA-II), non-dominated sorting particle swarm optimization (NSPSO), adaptive social evolution (ASE) and non-dominated sorting differential evolution with DE/rand/1 strategy (NSDE1) and found to be superior. The analysis is further extended to model-based algorithms, Pareto efficient global optimization (ParEGO), kriging for expensive evaluation Pareto (KEEP), and surrogate optimization of computationally expensive multi-objective problems (SOCEMO) which are commonly used for computationally intensive problems. These involve an additional computational cost of evolving the surrogate models. The performance of the proposed algorithm is found to be better than these, though, it has a relatively simple algorithmic structure and does not involve any additional computational cost. The algorithm is then evaluated on computationally intensive multi-objective optimization of bulk polymerization of methyl methacrylate. The numerical results obtained for this problem show better convergence for a limited number of function calculations.

Fault diagnosis of rolling element bearing using autonomous harmonic product spectrum method

Maintenance planning plays a critical role in the process industry, where any unplanned maintenance may lead to a significant loss. Condition monitoring happens to aid maintenance planning and has become an inherent part of the maintenance activity. Physical parameters such as vibration, acoustic emission, current, etc., are used for condition monitoring, out of which vibration is the most preferred parameter and is widely used in the industry. Vibration data is measured near to bearings, which themselves are monitored for their condition, and hence rolling element bearing (REB) is the focus of this study. REBs are monitored for the presence of a fault in them as well as for their severity. Fault diagnosis of REB using harmonic product spectrum (HPS) is proposed in this study. The proposed methodology's novelty lies in the signal pre-processing step, whose output is fed to the HPS method, which is used for defective raceway identification. The efficacy of HPS is assessed with parameter optimized Variational mode decomposition (VMD) and classical bandpass filtering method as pre-processors. It is observed that the HPS delivers better diagnostic results with the VMD method than the bandpass filtering method. Non-dominated sorting particle swarm optimization algorithm is deployed for parameter optimization of VMD. HPS combined with VMD as pre-processor forms an autonomous HPS(AHPS) algorithm, whose input is measured signal and output is defect frequency. The process is so designed that a raw signal, when fed to the algorithm, delivers the result as identification of a defective raceway. Unlike previously developed methods, the proposed method needs no manual intervention. Results obtained from simulated signals and signals recorded through experiments validate that the proposed methodology can be used effectively for fault diagnosis of REB.

Using Non-dominated Sorting Particle Swarm Optimization Algorithm II for Bi-objective Flow Shop Scheduling Problems

A hybrid particulate swarm optimization (hybrid) combination of an optimization algorithm of the particle swarm and a variable neighborhood search algorithm is proposed for the multi-objective permutation flow shop scheduling problem (PFSP) with the smallest cumulative completion time and the smallest total flow time. Algorithm for hybrid particulate swarm optimization (HPSO) is applied to maintain a fair combination of centralized search with decentralized search. The Nawaz-Enscore-Ham )NEH) heuristic algorithm in this hybrid algorithm is used to initialize populations in order to improve the efficiency of the initial solution. The method design is based on ascending order (ranked-order-value, ROV), applying the continuous PSO algorithm to the PFSP, introducing the external archive set storage Pareto solution, and using a hybrid strategy that combines strong dominance and aggregation distance to ensure the distribution of the solution set. We adopted the Sigma method and the roulette method, based on the aggregation distance, to select the global optimal solution. A variable neighborhood search algorithm was proposed to further search the Pareto solution in the external set. The suggested hybrid algorithm was used to solve the Taillard test set and equate the test results with the SPEA2 algorithm to check the scheduling algorithm’s efficacy.

Non-dominated sorting particle swarm optimisation for multi-channel cooperative spectrum sensing in heterogeneous green CRNs

Non-dominated sorting particle swarm optimisation for multi-channel cooperative spectrum sensing in heterogeneous green CRNs

A multi-objective genetic algorithm for assembly planning and supplier selection with capacity constraints

This study evaluates the supplier selection problem encountered when using multiple assembly plants with production capacity constraints to produce multiple products. It also applies assembly sequence planning (ASP) and assembly task assignment to production planning for the overall supply chain to determine optimal supplier combinations and production resource allocation. A multi-objective optimization mathematical model was constructed and a modified multi-objective algorithm was used to solve the optimization model. Developed on the basis of the nondominated sorting genetic algorithm II (NSGA-II), this modified algorithm incorporates an algorithmic and determination mechanism involving the greatest number of following tasks and the longest task time when initial solutions are generated. This increases the efficiency of solutions derived from the NSGA-II, here referred to as the W-NSGA2. The W-NSGA2 is then applied to the actual case of a faucet assembly task, and solutions derived by the W-NSGA2 are compared with those derived by the NSGA-II and the nondominated sorting particle swarm optimizer (NSPSO). Results show that the W-NSGA2 yields better solutions.

A mechanics and signal processing based approach for estimating the size of spall in rolling element bearing

Rolling element bearing (REB) is a critical component in any rotating machinery. It is subjected to surface damage during operation due to localised fatigue. Any prediction about damage severity helps in effective maintenance planning. Defect size estimation is one of the methods of assessing damage severity in REB and is the subject of this study. Several researchers have formulated analytical as well as signal processing based methods to estimate the defect size in REB. The study proposes a method that is a blend of a mechanics-based approach and signal processing approach for estimation of the size of the defect. The mechanics-based approach is based on the Hertzian contact theory and considers bearing operational parameters along with the effect of the load zone. The analogy of correlating peak acceleration at impact with force in the load zone is proposed and validated experimentally. The signal processing approach for locating entry and exit events encompasses concepts of cross-correlation, Variational mode decomposition (VMD), curve fitting, and root location. Parameter optimization of VMD is done using non dominated sorting particle swarm optimization. The proposed method is used to estimate the size of the defect, which is in the form of an artificially generated spall. Experiments are performed at various speeds and sizes of spall on outer as well as inner raceways. The results advocate the improved efficacy of the proposed method in estimating the size of artificially generated spalls.