Non-dominated Sorting Differential Evolution Research Articles

Simultaneous port number and location optimization of mold gates‐vents using in‐house coded stochastic multi‐objective optimization algorithm

AbstractThis research introduces multi‐objective stochastic optimization (MOSO) and non‐dominated sorting differential evolution (NSDE) in‐house coded algorithms for enhancing the optimization of the resin transfer molding (RTM) process during the mold‐fill phase for composite parts. Glass fiber‐vinyl ester‐based automotive bonnet and carbon fiber‐RTM6‐based aircraft wing flap parts were used as the case studies. Initially, the NSDE algorithm was developed for the simultaneous optimization of dry‐spot content and mold‐fill time by changing the locations of gates and vents with pre‐fixed port numbers. Then, the MOSO algorithm was designed for the concurrent optimization of the dry‐spot content, mold‐fill time and total number of ports by changing both the numbers and locations of gates and vents. The effect of race‐tracking was also investigated using higher permeability values at the composite part‐cut edges. When compared, the MOSO algorithm predicted less dry‐spot content, number of ports, mold‐fill time and uniform resin flow‐front progressions with lesser functional evaluations and computational time than the NSDE algorithm.Highlights The non‐dominated sorting differential evolution (NSDE) and multi‐objective stochastic optimization (MOSO) algorithms were developed for the optimization of the mold fill phase of resin transfer molded composite parts. The development of the NSDE algorithm aimed to concurrently optimize the dry‐spot content and mold‐fill time by changing the locations of gates and vents with pre‐fixed port numbers. The MOSO algorithm was designed to concurrently optimize dry‐spot content, mold‐fill time, and the total number of ports by varying both the numbers and locations of gates and vents. The MOSO algorithm predicted better‐automated mold‐fill phase optimalities for the resin transfer molded composite parts compared to the NSDE algorithm.

Capacity optimization allocation method for off-grid RES-H2 microgrid system considering the load-follow-source mechanism

Based on the uncertainty of the output of renewable energy such as wind and photovoltaic (PV), a capacity optimization allocation method considering the load-follow-source mechanism is proposed to solve the shortages of traditional methods, which is low in utilization rate of energy and low in economy. Firstly, considering the fluctuations of renewable energy sources (RES) and the operating characteristics of the electrolyzer, this paper proposes a capacity optimization method for off-grid hydrogen(H2) production system of renewable energy sources considering the load-follow-source operation mechanism. Then, combined with this mechanism, a capacity optimization allocation model is established, the objectives contain minimizing the levelized cost of hydrogen (LCOH), minimizing the capacity of energy storage, and optimizing the efficiency of power supply. Finally, an adaptive non-dominated sorting differential evolution algorithm (ANSDE) with external archiving is proposed to solve the established model. The calculation results show that the load-follow-source mechanism is effective for improving the economy of the system, and the proposed algorithm is high in efficiency and good in convergence comparing with traditional algorithms.

Development of non-dominated sorting differential evolution algorithm for cure cycle optimization of industrial composite parts

The main objective of this work is to develop a robust cost-effective in-house coded non-dominated sorting differential evolution (NSDE) algorithm to optimize the thermal-cure process through the minimization of part thickness thermal gradients and cure time of glass fibre-vinyl ester-based automotive bonnet and a carbon fibre-RTM6-based aircraft wing flap composite parts. The efficacy of the proposed algorithm was examined with the in-house coded NSGA-II and trial and error process simulations in terms of thermal gradient, cure time, and cure progression at the applied temperature cycles. At first, the NSDE and NSGA-II algorithms were developed for the simultaneous minimization of thermal gradients and cure time. Subsequently, the optimized thermal-cure profiles were obtained from trial-error simulations by manually changing the mould heating parameters. From the results, NSDE algorithm was found to be effective in achieving faster convergence with less cure process and computational time when compared to the NSGA-II algorithm. The NSDE algorithm performed effectively in terms of thermal gradient and cure time with the automated predictions of the mould heating parameters when compared with the trial and error process. From the results, the NSDE algorithm predicted a lower thermal gradient of 0.0008 K and 0.0022 K as well as cure times of 40 min and 35 min for the bonnet and wing flap parts, respectively.

Enhancing Urban Surface Runoff Conveying System Dimensions through Optimization Using the Non-Dominated Sorting Differential Evolution (NSDE) Metaheuristic Algorithm

Rapid urban development and increase in construction have significantly altered the surface coverage of cities, resulting in a rise in impervious surfaces such as roofs, streets, and pavements. These changes act as barriers against rainwater infiltration into the soil, leading to a substantial increase in surface runoff. Managing surface runoff has become a critical task in civil engineering and urban planning, as it can mitigate damage and provide opportunities for utilizing excess water. However, traditional flood control and guidance systems tend to be extensive and expensive, prompting researchers to explore cost-effective alternatives that consider all design parameters and variables. In this research, we propose an innovative approach that combines the NSDE (non-dominated sorting differential evolution) metaheuristic algorithm as an optimizer with the SWMM (storm water management model) as a simulator. The objective is to design efficient surface runoff collection networks by thoroughly investigating their hydraulic behaviors. This study focuses on the Chitgar watershed in Tehran, Iran, utilizing the SWMM model and NSDE multi-objective metaheuristic algorithm to determine the optimal dimensions of the channel and its intersecting structures. The aim is to minimize costs and reduce water leakage from the network. A comparison is made between the optimized design results and the existing network plan (without any design modifications). The analysis reveals substantial reductions in water leakage for all three design scenarios: a 7.66% reduction when considering only bridges, a 7.35% reduction with only the canal, and an impressive 95.26% reduction when both the canal and bridges are incorporated. These findings demonstrate the superiority of the optimized designs in terms of cost-effectiveness and the efficient management of surface runoff.

Optimal Exploitation of Urban Water Supply Networks Based on Pressure Management with the Nondominated Sorting Differential Evolution (NSDE) Algorithm

Urban water distribution networks are crucial infrastructures for providing essential services to society, but their exorbitant costs and limited water resources make their optimization a critical research area. Optimal management and design of these networks can help to reduce costs and enhance their efficiency while meeting technical, economic, and quality standards. In particular, the management of network pressure is critical for reducing leakage in water distribution systems. Thus, this study aimed to investigate two objective functions for optimizing the water distribution network: (i) minimizing costs while considering the number of pressure-relief valves, and (ii) minimizing network pressure by observing the optimal pressure range. To achieve this, the Nondominated Sorting Differential Evolution (NSDE) multi-objective metaheuristic algorithm was employed as the optimization tool, and a computer program was written in MATLAB software for solving the optimization models. EPANET software was also used for hydraulic simulation of the water distribution network. The efficiency and capabilities of these models were tested on the case study of the third district of Mashhad in Iran. The results indicated that the installation and adjustment of pressure-relief valves in accordance with the positions and optimal settings of the output of the proposed models significantly improved the desired goals, particularly the average pressure of the network. As an example of optimization, the study achieved a 56.12% reduction in pressure compared to the case without a plan, considering five pressure-relief valves.

Multivariate wind speed forecasting based on multi-objective feature selection approach and hybrid deep learning model

This study proposes an effective model for enhancing the short-term wind speed forecasting performance by considering the effect of multiple meteorological factors. (a) The filter-wrapper non-dominated sorting differential evolution algorithm incorporating K-medoid clustering (FWNSDEC) is designed to select key meteorological factors and generate multiple feature subsets. For each feature subset, the hybrid deep learning model is designed: (b) singular spectrum analysis (SSA) is used to decompose the meteorological factors and construct the three-dimensional input structure; (c) convolutional long short-term memory (ConvLSTM) network is then adopted to process the sample set of three-dimensional sequence, and the final forecasting result is the average prediction of all the built ConvLSTMs. To evaluate the effectiveness of FWNSDEC-SSA-ConvLSTM, three comparative experiments on four datasets collected from the National Renewable Energy Laboratory are implemented. Experiment results show that the average mean absolute percentage error over four datasets for 1-step-ahead, 2-step-ahead, and 3-step-ahead forecasting is 1.42%, 1.99%, and 3.28%, respectively, which are much better than the predictions using feature selection benchmarks, hybrid forecasting benchmarks with different deep learning networks and data decomposition methods, and other advanced forecasting systems. Extended model discussion in terms of Friedman test and parameters sensitivity analysis further verifies the potential of proposed model.

Nondominated Sorting Differential Evolution Algorithm to Solve the Biobjective Multi-AGV Routing Problem in Hazardous Chemicals Warehouse

For the multiple automated guided vehicle (multi-AGV) routing problems in the warehousing link of logistics, where the optimization objective is to minimize both the number of AGVs used and the maximum pickup time simultaneously, a nondominant sorting differential evolution (NSDE) algorithm is proposed. In the encoding and decoding stages, the pickup point area is divided. AGVs are allocated to each region according to the proposed rule based on avoiding duplicate paths. Meanwhile, the pickup points within the region can be adjusted to optimize the pickup paths and improve the pickup efficiency. The fast nondominated sorting method and elitist selection strategy in the nondominated sorting genetic algorithm II (NSGA-II) are introduced into the differential evolution algorithm, which sorts all the regions to obtain the best Pareto solution set. Lastly, the domination of the proposed NSDE algorithm in Pareto frontier evaluation indicators is verified by some numerical experiments.

A Multiobjective Evolutionary Nonlinear Ensemble Learning With Evolutionary Feature Selection for Silicon Prediction in Blast Furnace.

In the blast furnace ironmaking process, accurate prediction of silicon content in molten iron is of great significance for maintaining stable furnace conditions, improving hot metal quality, and reducing energy consumption. However, most of the current research works employ linear correlation coefficient methods to select input features in modeling, which may not fully take the nonlinear and coupling relationships between features into account. Therefore, this article considers the input feature selection issue of silicon content prediction model from a new perspective and proposes a multiobjective evolutionary nonlinear ensemble learning model with evolutionary feature selection mechanism (MOENE-EFS), in which extreme learning machine is adopted as the base learner. MOENE-EFS takes the input feature scheme of each base learner as well as their network structure and parameters as decision variables and proposes a modified nondominated sorting differential evolution algorithm to optimize two conflicting objectives, i.e., accuracy and diversity of base learners, simultaneously. Through the optimization, a set of Pareto optimal base learners with high accuracy and strong diversity can be obtained. Moreover, different from the linear ensemble methods commonly used in classical evolutionary ensemble learning, this article proposes a nonlinear ensemble method to combine the obtained base learners based on differential evolution. Experimental results indicate that the two proposed strategies, i.e., evolutionary feature selection and nonlinear ensemble, are very effective in improving the accuracy and stability of the prediction model. MOENE-EFS also outperforms the other prediction models in both benchmark data and practical industrial data. Furthermore, analysis on the input features of all Pareto optimal base learners shows that the evolutionary feature selection is capable of selecting essential features and is consistent with human experience, which indicates it is a promising method to deal with the input feature selection issue in silicon content prediction.

Multi-objective distribution network planning method with distributed generation based on non dominated sorting differential evolution algorithm

Combined with the specific problems of distribution network planning with distributed generation, this paper constructs a multi-objective optimization model of distribution network planning with distributed generation. According to the distributed generation distribution network layout planning with distributed generation, under the condition of uncertain load prediction value of distributed generation distribution network, taking the minimum voltage stability index, minimum network loss and minimum investment cost of distributed generation as sub objectives, a multi-objective programming model is established, and the model is solved by non dominated sorting differential evolution (NSDE) algorithm.

Mass design of nonlinear energy sinks

Nonlinear energy sink (NES) is attracting more attention because of its promising in the control of engineering structure vibration. This paper thoroughly reviews the selection of NES mass ratios in existing studies and finds the big difference. So there is still a lack of a unified and profound understanding of the mass design of the NES. To achieve decent vibration mitigation and reduce costs, the mass design of the NES is essential. This paper pioneers the optimal mass design of NESs. The non-dominated sorting differential evolution algorithm and parameter sensitivity analysis are adopted. The relations between the optimal mass and the vibration mitigation effects are discussed in different damping and harmonic excitation strength. The results show that different from the monostable NES, the bistable NES with a small mass ratio (no more than 0.7%) and small damping can reduce 30% of the resonance peak of the primary system in most situations. However, both these two NESs with large damping need larger mass ratios to achieve decent vibration mitigation. Smaller damping of the primary system will contribute to wider effective ranges of the mass ratio of the two NESs, while larger excitation will contribute to those of the monostable NES. To ensure the vibration mitigation effects in small mass ratio, the monostable and bistable NESs with small damping need large and small cubic stiffness, respectively. Besides, the bistable NES with small damping prefers small negative linear stiffness. In summary, this paper presents a detailed study of the mass design of the nonlinear energy sink. It will help researchers and engineers find the suitable mass and design NES in the vibration reduction of engineering structures.

Many-objective optimization for construction project scheduling using non-dominated sorting differential evolution algorithm based on reference points

Project scheduling has become one of the critical concerns of the construction project's success. In recent years, global construction markets have become increasingly competitive, and the number of project stakeholders has grown significantly. These issues have required concurrently reaching competing objectives, such as optimizing the time, resources, cost, environmental impact, safety risks, and quality of a project. Several types of research efforts have focused on multiple-objective construction scheduling models to deal with these objectives. However, there is still a need to integrate all these objectives in the scheduling process to consider most aspects of a project. In order to fill this gap, a many-objective optimization model considering time, cost, resource, environmental impact, safety, and quality based on a newly developed many-objective optimization algorithm, Non-dominated Sorting Differential Evolution algorithm based on Reference points (NSDE-R) is presented in this study. To determine the most proper schedule based on project priorities, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) merged with the optimization algorithm. The proposed model's applicability demonstrated employing a case study of a building construction project.

Splitting and placement of data-intensive applications with machine learning for power system in cloud computing

Aiming to meet the growing demand for observation and analysis in power systems that based on Internet of Things (IoT), machine learning technology has been adopted to deal with the data-intensive power electronics applications in IoT. By feeding previous power electronic data into the learning model, accurate information is drawn, and the quality of IoT-based power services is improved. Generally, the data-intensive electronic applications with machine learning are split into numerous data/control constrained tasks by workflow technology. The efficient execution of this data-intensive Power Workflow (PW) needs massive computing resources, which are available in the cloud infrastructure. Nevertheless, the execution efficiency of PW decreases due to inappropriate sub-task and data placement. In addition, the power consumption explodes due to massive data acquisition. To address these challenges, a PW placement method named PWP is devised. Specifically, the Non-dominated Sorting Differential Evolution (NSDE) is used to generate placement strategies. The simulation experiments show that PWP achieves the best trade-off among data acquisition time, power consumption, load distribution and privacy preservation, confirming that PWP is effective for the placement problem.

Dynamic multi-objective evolutionary algorithm with objective space prediction strategy

To solve dynamic multi-objective optimization problems, a dynamic multi-objective evolutionary algorithm (DMOEA) must be able to deal with the dynamics of the environment, and such modifications can lead to new optimal solutions over time. Various algorithms have been proposed that modify the way a change is handled. Among them, prediction-based methods are promising for solving this kind of problem. They provide guided direction for population evolution through a prediction mechanism that assists the DMOEA to respond quickly to new changes. Based on these strategies, we propose a dynamic non-dominated sorting differential evolution improvement with prediction in the objective space (DOSP-NSDE). The proposal uses the objective space prediction (OSP) strategy for both the static evolutionary process (between changes) and the change reaction mechanism to predict the new optimal front location. Experiments were performed on a real-world problem and four sets of test problems: FDA, dMOP, UDF, and DF. Comparison of DOSP-NSDE with several algorithms in the literature, considering three metrics, is presented, showing that the proposal is competitive with most problems.

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.

Optimization of integrated fuzzy decision tree and regression models for selection of oil spill response method in the Arctic

The challenging oil spill response in the Arctic calls for effective response decision support tools. In this study, a framework comprising the development of various integrated fuzzy decision tree and regression (FDTR) models as well as model optimization was developed to facilitate the selection of suitable response methods for oil spill accidents in Arctic waters. The FDTR models took into account the influential attributes affecting the effectiveness of oil spill response in harsh Arctic environments. Different FDTR models were developed based on the combinations of three regression analyses, including linear, non-linear, and Gaussian process regression (GPR) and four information evaluation measures for splitting a decision tree, including information gain, deviance, GINI impurities (GINI), and misclassification error. Non-dominated sorting differential evolution (NSDE) optimization was employed to enhance the predictive performance of the FDTR models. The prediction performance of the FDTR models was compared using an oil spill dataset. Using this framework, the average prediction accuracy and the number of rules (representing the robustness) of FDTRs were increased by 14% and decreased by 57%, respectively. A set of optimal prediction models to promptly select an appropriate response method can be obtained using this framework. Among all models, GPR-GINI performed the best concerning optimal values of objective functions.

Energy demand and quality management of standalone diesel/ PV /battery microgrid using reconfiguration

Power quality has become one of the most vital challenges in the planning of smart distribution grids due to popularity and myriad applications of power electronic devices. This paper introduces a new method based on network reconfiguration to efficiently deliver the energy demand of standalone Diesel/PV/Battery microgrids with improved power quality. The feeder reconfiguration is performed to decrease power loss, decrease the total harmonic distortion (THD), and improve voltage sag indices. Mathematical models of Diesel/PV/Battery standalone system are integrated with harmonic power flow algorithms to implement the concept. Reconfiguration algebraic equations along with nonlinearities introduced by Diesel/PV/Battery standalone system are solved by means of Non-Dominated Sorting Differential Evolution Algorithm (NDSDEA). The proposed methodology is carried out on a 33-bus standalone microgrid. The results have shown that reconfiguration not only improves the overall performance of the standalone energy system but also is effective to improve the quality of power delivered to the significant consumers. The method has both economic and technical benefits for such islanded networks.

Multiobjective Pareto optimisation of pharmaceutical product formulation using radial basis function network and non-dominated sorting differential evolution

Pharmaceutical product formulation often involves several composition factors and response characteristics and its optimal formulation requires the best combination of formulation variables to satisfy the multiple and conflicting characteristics of the product. In this work, a novel multiobjective Pareto optimisation strategy is developed by combining a radial basis function network (RBFN) with a non-dominated sorting differential evolution (NSDE) and applied for optimal formulation of a trapidil product involving conflicting response characteristics. The RBFN models of this strategy are developed by using spherical central composite design data of trapidil formulation variables representing the amounts of microcrystalline cellulose, hydroxypropyl methylcellulose and compression pressure and the corresponding response data of release order and rate constant. These models in combination with NSDE are augmented with naive and slow and constraint techniques to generate Pareto optimal solutions for product formulation. The optimal formulation results of RBFN-NSDE are compared with those of multiple regression model-based evolutionary Pareto optimisation strategy. The RBFN-NSDE is found to exhibit better Pareto optimal performance for pharmaceutical product formulation.

Multiobjective Pareto optimisation of pharmaceutical product formulation using radial basis function network and non-dominated sorting differential evolution

Multiobjective Pareto optimisation of pharmaceutical product formulation using radial basis function network and non-dominated sorting differential evolution

Multi-Objective Optimization for Interactive Reservoir-Irrigation Planning Considering Environmental Issues by Using Parallel Processes Technique

Water allocation to different users implies a trade-off between the benefits perceived by different sectors and environmental demands. This conflict, considering the income of different sectors and environmental issues, is very crucial, especially in reservoir operation. In this study, a multi-objective optimization algorithm combined with two simulation models (SWAT and WEAP) has been developed to consider the tradeoff between agricultural benefit and environmental water demand through parallel processing. In this model, decision variables are monthly agricultural demands which are considered as inputs into the Water Evaluation And Planning (WEAP) model. Then, the production of different crops and discharge to the downstream lake are obtained based on the allocated water to each sector using the Soil and Water Assessment Tool (SWAT) model. After the simulation, the objective functions are calculated based on agricultural income and environmental flow discharged to the downstream lake. To evaluate the performance of the proposed model, Mahabad reservoir, in Mahabad river basin (located at the south of the Lake Urmia and northwestern of Iran), was studied during a drought period. This reservoir supplies domestic, industry, agriculture water of the Mahabad plain and downstream environmental demand which is discharged to the Lake Urmia. Non-dominated Sorting Differential Evolution (NSDE) algorithm is used to find the optimal water allocation to different sectors and efficient agricultural irrigation patterns of different crops (winter wheat, alfalfa, apple, and sugar beet). Results showed the optimal operation of the reservoir through irrigation management could improve water stress indicator from 80% in traditional irrigation pattern to 60%, only by 6.5% decrease in agricultural income. Also, the inflow to Urmia Lake was increased from 173 MCM to 354 MCM during the drought period (5 years).

Computation offloading for multimedia workflows with deadline constraints in cloudlet-based mobile cloud

In recent years, mobile applications have become more demanding on device performance, especially for the multimedia applications. As the battery capacity and computing performance of mobile devices gradually fails to meet the needs of mobile applications for device performance, mobile cloud computing (MCC) is adopted to handle multimedia workflows. Offloading mobile applications to remote cloud with rich computing resources for execution helps improve the execution efficiency of multimedia applications on mobile devices. However, because the remote cloud is too far away from users, MCC has a high network latency, which influences the experience of multimedia applications that require high real-time performance. As a small mobile cloud computing center on the edge of Internet, cloudlet provides the powerful computing resources for surrounding mobile devices and supports resource-intensive and interactive mobile applications. However, in the cloudlet-based cloud computing environment, it is still a challenge to offload mobile applications to optimize the energy consumption of mobile devices while meeting the deadline of each mobile application. Given the challenge, an offloading method based on non-dominated sorting differential evolution (NSDE) is designed. Firstly, the multimedia application is modeled as a constrained multi-objective optimization problem by the workflow technology. Then, we use the NSDE algorithm to optimize this multi-objective optimization problem, and minimize the energy consumption with the constraints of meeting the deadline of each multimedia workflow. Finally, through a large number of experimental comparisons and analysis, the validity and superiority of our proposed method are verified.