Non-dominated Sorting Particle Swarm Optimization Research Articles

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

The 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

An Elitist Multi-Objective Particle Swarm Optimization Algorithm for Sustainable Dynamic Economic Emission Dispatch Integrating Wind Farms

In recent years, wind energy has been widely used as an alternative energy source as it is a clean energy with a low running cost. However, the high penetration of wind power (WP) in power networks has created major challenges due to their intermittency. In this study, an elitist multi-objective evolutionary algorithm called non-dominated sorting particle swarm optimization (NSPSO) is proposed to solve the dynamic economic emission dispatch (DEED) problem with WP. The proposed optimization technique referred to as NSPSO uses the non-dominated sorting principle to rank the non-dominated solutions. A crowding distance calculation is added at the end of all iterations of the algorithm. In this study, WP is represented by a chance-constraint which describes the probability that the power balance cannot be met. The uncertainty of WP is described by the Weibull distribution function. In this study, the chance constraint DEED problem is converted into a deterministic problem. Then, the NSPSO is applied to simultaneously minimize the total generation cost and emission of harmful gases. To proof the performance of the proposed method, the ten-unit and forty-unit systems—including wind farms—are used. Simulation results obtained by the NSPSO method are compared with other optimization techniques that were presented recently in the literature. Moreover, the impact of the penetration ratio of WP is investigated.

Reliability Enhanced Multi-objective Economic Dispatch Strategy for Hybrid Renewable Energy System with Storage

An optimal dispatch strategy for the economic operation of hybrid renewable energy system with storage is presented in this paper. Solar photovoltaic (PV), Wind and Battery Storage are the prime components of interest constituting the hybrid system. In addition to the economic aspect of the systems, the formulation focuses on renewable prioritized operation, system reliability and environmental sustainability enhancement. Being a multi-objective (MO) problem, a modified non-dominated sorting particle swarm optimization is used for solving the problem, considering operational cost, pollutant emission, and expected energy not served as operational objectives. The non-dominated sorting particle swarm optimization (NSPSO) is augmented with crowding distance technique, stochastic weight trade-off and chaotic mutation approaches, to control the exploration of global and particle bests, alleviating premature convergence, and enhancing solution search capability. A two-stage approach is used to derive the best solution. A modified IEEE 30-bus test system is used to demonstrate the results. By using the proposed approach, a lower and wider Pareto front is obtained, in comparison with prominent optimization approaches.

Realistic energy commitments in peer-to-peer transactive market with risk adjusted prosumer welfare maximization

As the local energy sources are mostly uncertain and fluctuating in nature, the ‘energy risk’ due to discrepancies between committed energy transactions and metered measurements is prominent in peer to peer (P2P) markets. We propose a P2P market settlement mechanism which lowers this risk and maximizes the welfare of buyers and sellers. The risk in energy production is modelled using Markowitz portfolio theory and the best point where energy return per unit risk is maximum is obtained from the efficient frontier by using the modified Sharpe ratio. The energy portfolio thus obtained is used as a constraint while optimizing the conflicting prosumer benefits using multi-objective stochastic weight trade-off chaotic non-dominated sorting particle swarm optimization (SWTC-NSPSO). In effect, only a reliable proportion of total energy demand submitted in the bid is cleared in the market, foreseeing the real-time fluctuations. The proposed market settlement mechanism also gives room to the existing distribution system operators by assigning them the duty of 1) optimally allocating energy among buyers and sellers in accordance with their competitive bids 2) providing the infrastructure, managing the market and charging for the service and 3) checking the technical feasibility by performing load flow and monitoring power transfer sensitivities to encourage short distance transactions. The energy allocation is done in CIGRE LV benchmark microgrid with ten peers having solar and wind generation. The allocated energy is found to be closer to the metered measurements and hence the reserve cost is observed to be less.

A two-phase non-dominated sorting particle swarm optimization for chip feature design to improve wafer exposure effectiveness

Enhancing the competitive advantages of wafer fabs is crucial to increase the number of gross dies per wafer and to reduce average die cost. Most of existing studies about IC (integrated circuit) feature design focus on yield enhancement, yet little research has been done on cost reduction through increasing gross die number and decreasing shot number simultaneously. To generate the alternative feature design to improve wafer exposure effectiveness, a prediction model between chip size and gross die number and shot number was built through amount of data collection and model training. However, it’s difficult to consider different exposure conditions under various parameter setting of amount of IC features. In order to fill the gap of considering real setting, this study aims to propose a two-phase non-dominated sorting particle swarm optimization (TNSPSO) method to maximize number of gross die and minimize the shot number and then suggests alternative chip features for IC designers. First, non-dominated sorting algorithm is used to find the solutions on the frontier. Second, these particles on the frontier are diffused toward the sparse region on the frontier. To evaluate the validity of proposed approach, two conventional heuristic algorithms, non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective particle swarm optimization (MOPSO) were selected . The experiment results showed that the proposed method not only capture the solutions closer to the Pareto frontier but also has better convergence and diversity of the solutions than the other methods. The proposed approach can assist IC designer in effectively deriving chip layout design with enhancement of wafer exposure effectiveness.

Non-Dominated Sorting Particle Swarm Optimization (NSPSO) for Multi-Channel Cooperative Spectrum Sensing in Heterogeneous Green CRNs

In the radio spectrum the exact help of white spaces is needed speed, robust and the correct approaches for the recognition of scarcity. To overcome these troubles new approaches is introduced in the radio spectrum to identify the white spaces. In this paper mainly focused the online algorithms to reduce, a non-dominated sorting particle swarm optimisation (NSPSO) is engaged to iteratively estimate its result. The first task is to choose the cluster heads (CHs) with the exact reporting paths with the reduced faults. Another task is to resolve the optimal sensing attributes like sensing periods and identification of thresholds of the entire secondary user (SU) to reduce the power consumption of the entire cluster. Employing Poisson-beta-binomial distribution, a new and common K-out-of-N voting regulation is implemented for the heterogeneous CRNs to permit the secondary users to have the various identification performances.

Tradeoff analysis between economic and ecological benefits of the inter basin water transfer project under changing environment and its operation rules

Inter basin water transfer projects inevitably affect the ecosystem of the river in the water source area, especially under the changing environment. The tradeoff analysis between economic and ecological benefits has become a focus problem in actual multi-objective reservoirs operation. Therefore, this paper aims to research the tradeoff relationship by the multi-objective reservoirs optimal operation model considering economic and ecological objectives. Specifically, mutated runoff series are corrected by seasonal and trend decomposition using Loess (STL), and three ecological water demand processes are gained from the corrected runoff. Then, an improved evolutionary algorithm is proposed to solve this model, named Non-dominated Shorting Particle Swarm Optimization with Feasible Search Space (FSS–NSPSO). Furthermore, contour maps and functional formulae are fitted to describe the tradeoff analysis among three objectives. Finally, operation rules are generated considering ecological water demand. The paper applies the model to the Hanjiang to Wei River Water Diversion Project. The results indicate that: (1) the ecological water quantity in the river has decreased further under the influence of changing environment. (2) The FSS-NSPSO has advantages in terms of convergence, uniformity, and diversity compared to the Fast Elitist Non-dominated Sorting Genetic Algorithm and Non-dominated Sorting Particle Swarm Optimization. Real number coding, less evolutionary operator parameters and a feasible search space in the FSS-NSPSO are benefit for solving complex multi-objective reservoir models. (3) The contradiction between ecological and economic benefits is obvious. Ecological water demand process affects reservoir operation benefit. (4) The reservoir operation rules can be generated based on the modified ecological water demand process to balance the interests of multiple parties. This study is expected to be of merit to quantitative tradeoff analysis between ecological and economic benefits in inter basin water transfer projects, and as a reference method for multi-objective reservoir operation models.