Non-dominated Sorting Genetic Algorithm III Research Articles

Ship pipe route design based on NSGA-III and multi-population parallel evolution

Ship pipe route design (SPRD) aims to generate pipe layouts while considering various objectives and constraints in a confined 3D space, leading to extremely high complexity. To solve the problem of SPRD, traditional methods often adopt weighted summation of sub-objectives, introduce penalty functions to convert it to a single-objective optimization problem. However, these methods tend to yield a single optimal pipe route design pattern, and some existing multi-objective pipe routing algorithms do not have high search performance or sufficient treatment of constraints. Based on the grid-space decomposition model, this paper establishes a multi-objective pipe routing optimization model with several sub-objectives including path length, bends number, path energy, number of air-pockets, and number of violated bending distance, which also considers pipe routing for different diameters and interface direction requirements. A ship pipe route design framework based on the NSGA-III (Non-dominated Sorting Genetic algorithm III) is proposed, and its embedded pathfinding algorithm can explore pipe route using heuristic information and failure retry strategy, which greatly reduces the algorithm complexity compared to the classical algorithms such as A* and LEE and also facilitates parallel implementation. By employing OpenMP technology to achieve parallel evolution of multiple populations, more Pareto optimal solutions can be obtained in approximately the same timeframe as single population evolution. Finally, through comparative experiments using simulated cases, the feasibility and advancement of the proposed method are verified.

Many-objective coevolutionary learning algorithm with extreme learning machine auto-encoder for ensemble classifier of feedforward neural networks

In artificial neural network (ANN) learning, empirical risk can be expressed by training error, while structural risk can be expressed by network complexity. Learning from data is often seen as a tradeoff between these two risks. Additionally, balancing training error and validation error can overcome the overfitting problem to some extent. It is known that the network performance is also related to regularization term. In order to consider four factors, i.e. training error, validation error, network complexity and regularization term simultaneously in the training process of a single-hidden layer feedforword neural network (SLFN), a many-objective coevolutionary learning algorithm (MOCELA) integrated with extreme learning machine auto-encoder (ELMAE), called MOCELA-ELMAE is presented. In MOCELA, the non-dominated sorting genetic algorithm III (NSGA-III) is improved for handling the many-objective optimization model with hybrid variables, where binary coding is used for structure learning and real coding is utilized for representing input parameters, referring to all input weights and hidden biases of the AE network. Output parameters of AE, i.e. output weights are analytically calculated by the non-iterative learning rule. The network structure and connection parameters of SLFN are determined based on those of AE. A set of Pareto optimal solutions are eventually collected by the MOCELA-ELMAE, which represents multiple optimal SLFNs. To make the final decision, three best SLFNs with minimum validation errors are selected as the base classifiers for selective ensemble learning. Extensive experiments are implemented on the benchmark classification data sets of UCI machine learning repository, and obvious improvements have been observed when the proposed MOCELA-ELMAE is compared with the NSGA-III based on hybrid coding and completely non-iterative learning of SLFN respectively. The experimental results also illustrate that the MOCELA-ELMAE performs much better than other state-of-the-art learning algorithms on many data sets.

GPU-accelerated non-dominated sorting genetic algorithm III for maximizing protein production

<abstract><p>Maximizing protein expression levels poses a major challenge in bioengineering. To increase protein expression levels, numerous factors, including codon bias, codon context bias, hidden stop codons, homologous recombination, suitable guanine-cytosine ratio, and hairpin loop structure, are crucial and quantified by six objective functions: CAI, CPB, HSC, HD, GC3, and SL. Optimizing these six objectives simultaneously constitutes a multi-objective optimization problem, aiming to identify the favorable Pareto solutions rather than a singular optimal solution. However, achieving satisfactory solutions requires numerous cycles and solutions, thus leading to a large number of functional evaluations. While there are frameworks for multi-objective optimization problems, they often lack efficient support for objective function computation in protein encoding. In this paper, we proposed a method to design a set of coding sequences (CDSs) based on non-dominated sorting genetic algorithm III (NSGA-III), accelerated using NVIDIA graphical processing units (GPUs). Experimental results indicated that our method is 15,454 times faster than the Pymoo framework and is evaluated using 100 solutions and 100 cycles. Since our GPU implementation facilitated the use of larger solutions and more cycles, we were able to design a superior set of CDSs by increasing solutions to 400 and cycles to 12,800. In addition, our NSGA-III-based method consistently surpassed the NSGA-II approach when the number of cycles exceeded 3200 by utilizing 100 solutions. Finally, we observed that a gradual reduction of the mutation probability as the number of cycles increased yielded better quality results than maintaining a fixed mutation probability.</p></abstract>

Optimum design of planetary gear set using multi-objective optimization considering mass, power loss and transmission error

In this study, a multi-objective optimal design of a planetary gear set was performed considering mass, power loss, and transmission error. In particular, the results were analyzed from a novel perspective. The implemented multi-objective optimal design methodology minimizes the mass, power loss, and vibration with respect to seven macro-geometry variables. The peak-to-peak static transmission error (PPSTE) of each gear pair corresponds to the excitation source of the planetary gear set, and reducing the PPSTE helps minimize vibration. Thus, the PPSTE was calculated using the time-varying mesh stiffness obtained through an analytical model. The non-dominated sorting genetic algorithm-III (NSGA-III) was implemented as a multi-objective optimization algorithm, and the criteria importance through the inter-criteria correlation method was implemented for quantitative comparison purposes regarding the combination of objective functions. The results showed that obtaining an optimal macro-geometry design for a planetary gear set exhibiting good performance required simultaneous consideration of the mass, power loss, and vibration. Furthermore, the results confirmed that a versatile optimization algorithm, such as NSGA-III, should be used to capture the discontinuous solution distribution owing to various constraints of the planetary gear set.

Hybrid Flow Shop Scheduling Problem with Energy Utilization using Non-Dominated Sorting Genetic Algorithm-III (NSGA-III) Optimization

Hybrid flow shop scheduling (HFS) is an on sought problem modelling for production manufacturing. Due to its impact on productivity, researchers from different backgrounds have been attracted to solve its optimum solution. The HFS is a complex dilemma and provides ample solutions, thus inviting researchers to propose niche optimization methods for the problem. Recently, researchers have moved on to multi-objective solutions. In real-world situations, HFS is known for multi-objective problems, and consequently, the need for optimum solutions in multi-objective HFS is a necessity. Regarding sustainability topic, energy utilization is mainly considered as one of the objectives, including the common makespan criteria. This paper presents the existing multi-objective approach for solving energy utilization and makespan problems in HFS scheduling using Non-Dominated Sorting Genetic Algorithm-III (NSGA-III), and a comparison to other optimization models was subjected for analysis. The model was compared with the most sought algorithm and latest multi-objective algorithms, Strength Pareto Evolutionary Algorithm 2 (SPEA -II), Multi-Objective Algorithm Particle Swarm Optimization (MOPSO), Pareto Envelope-based Selection Algorithm II (PESA-II) and Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D). The research interest starts with problem modelling, followed by a computational experiment with an existing multi-objective approach conducted using twelve HFS benchmark problems. Then, a case study problem is presented to assess all models. The numerical results showed that the NSGA-III obtained 50% best overall for distribution performance metrics and 42% best in convergence performance metrics for HFS benchmark problems. In addition, the case study results show that NSGA-III obtained the best overall convergence and distribution performance metrics. The results show that NSGA-III can search for the best fitness solution without compromising makespan and total energy utilization. In the future, these multi-objective algorithms’ potential can be further investigated for hybrid flow shop scheduling problems.

Optimization of agricultural planting structure in irrigation areas of Heilongjiang province considering the constraints of water resource system resilience

From the perspective of sustainable development in irrigation areas, imperfect constraints, which have negatively impacted previous agricultural planting structure optimization models, tend to produce unreasonable optimization results. Thus, in this paper, attempts are made to incorporate the resilience of water resource systems into the constraint set and explore the optimal layout of agricultural planting structures in irrigation areas. We selected 12 sample irrigation areas in Heilongjiang Province, China, including Chahayang, Fuyu, and Tailai, as the research areas and considered their actual situations. Then, we established a multiobjective optimization model for an agricultural planting structure with rice, soybean, and corn planting areas as decision variables; socioeconomic benefits, agricultural production benefits, ecological benefits, and water resource benefits as objective functions; and water resource system resilience and net crop output value and other factors as constraints. Fifteen scenarios were assessed through the combination of objective functions, and the nondominated sorting genetic algorithm III (NSGA-III) was used to optimize and analyze the agricultural planting structure of typical irrigation areas in Heilongjiang Province and to determine the optimal planting plan. The research results found that the function values adjusted by planting area have significantly improved compared to those before optimization. The adjustment trend of agricultural planting area is to increase the planting area of rice and soybean and reduce the planting area of corn. The introduction of constraints on water resource system resilience ensures that other benefits remain basically unchanged; thus, the water resource objective function is significantly better with resilience constraints than without them. Therefore, water resource system resilience can act as an effective constraint; this finding enriches and expands the theoretical and methodological system for optimizing the management of agricultural planting structures in irrigation areas and thus provides important technological support for ensuring regional food security, ecological security, and water security, which has substantial practical significance.

Multi-objective optimization of gradient gas diffusion layer structures for enhancing proton exchange membrane fuel cell performance based on response surface methodology and non-dominated sorting genetic algorithm-III

The gas diffusion layer structure has a significant impact on water and gas transport in proton exchange membrane fuel cell (PEMFC). In this study, a multi-objective optimization (MOO) method is applied to optimize the PEMFC gas diffusion layer (GDL) structures for performance enhancement. Nine design parameters are studied to analyses the output performance of the fuel cell and the average water content of the membrane, oxygen concentration non-uniformity, power density and system efficiency are used as performance indexes. The MOO forms the response surface regression model based on the three-dimensional numerical model of PEMFC, and the response surface methodology after processing through the non-dominated sorting genetic algorithm-III (NSGA-III) to obtain the Pareto frontier, and prioritizes the best operating conditions and structures based on technique for order preference by similarity to an ideal solution (TOPSIS). Compared with the reference point, the performance indexes of the decision point obtained by the NSGA-III algorithm and TOPSIS algorithm is enhanced by 0.45 %, 42.06 %, 2.99 % and 0.25 %, respectively. This study presents a new multi-objective optimization method for optimizing operating conditions combine with GDL structures for building efficient PEMFC, which provides a solution for achieving higher performance. The optimal results in this paper can provide some guidance for fuel cell performance improvement and control optimization.

Many-objective optimization of BEV design parameters based on gradient boosting decision tree models and the NSGA-III algorithm considering the ambient temperature

In this study, extensive efforts have been devoted to optimizing the energy distribution and performance of a battery electric vehicle (BEV). An integrated simulation model based on energy flow test data is built and validated, and a parallel framework to implement automatic batch simulations is developed. On this basis, eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM) and Categorical Boosting (CatBoost) models of the BEV are established and compared, and many-objective optimization is carried out based on the Non-dominated Sorting Genetic Algorithm-III (NSGA-III) algorithm. The results indicate that the developed parallel framework can efficiently perform automatic batch computations of the integrated simulation model for the BEV, and the CatBoost model demonstrates superior prediction performance on both the train and test sets. A uniformly distributed non-dominated set approximating the Pareto Front (PF) is obtained by the many-objective optimization, with the fourth non-dominated solution exhibiting a favorable optimization effect. The electricity consumption per 100 km, half-axis effective work, electricity recovered by battery, energy utilization and recovery efficiency are improved by 9.4 %, 12.2 %, 6.4 %, 96.3 % and 16.0 %, respectively. These findings can provide the theoretical basis, directional guidance and data support for the accurate modeling, batch simulation and many-objective optimization of BEVs.

A framework for real-time operation of urban detention reservoirs: Application of the cellular automata and rainfall nowcasting

Detention reservoirs are employed in urban drainage systems to reduce peak flows downstream of reservoirs. In addition to the volume of detention reservoirs, their operational policies could significantly affect their performance. This paper presents a framework for the real-time coordinated operation of detention reservoirs using deep-learning-based rainfall nowcasting data. Considering the short concentration time of urban basins, the real-time operating policies of urban detention reservoirs should be developed quickly. In the proposed framework, a cellular automata (CA)-based optimization algorithm is linked with the storm water management model (SWMM) to optimize real-time operating policies of gates at the inlets and outlets of detention reservoirs. As CA-based optimization models are not population-based, their computational costs are much less than population-based metaheuristic optimization techniques such as genetic algorithms. To evaluate the applicability and efficiency of the framework, it is applied to the east drainage catchment (EDC) of Tehran metropolitan area in Iran. The results illustrate that the proposed framework could reduce the overflow volume by up to 60%. For complete flood control in the study area, in addition to the real-time operation of detention reservoirs, constructing five tunnels with a total length of 13200 m is recommended. To evaluate the performance of the CA-based optimization model, its results are compared with those obtained from the non-dominated sorting genetic algorithm III (NSGA-III). It is shown that the CA-based model provides similar results with only 5% of the run-time of NSGA-III. A sensitivity analysis is also performed to evaluate the effects of optimization models’ parameters on their performance.

Development and application of a new water-carbon-economy coupling model (WCECM) for optimal allocation of agricultural water and land resources

The optimal allocation of agricultural water and land resources is of great significance in ensuring sustainable food production and economic benefits of farmers. However, agriculture, as an important carbon cycle ecosystem, has paid limited attention to carbon sequestration in the optimal allocation of water and land resources. Therefore, this study developed a new water-carbon-economy coupling model (WCECM) for optimal allocation of agricultural water and land resources. In this model, the minimum water scarcity, maximum carbon sequestration and maximum economic benefits are taken as the optimization objectives. In addition, surface water volume and groundwater volume and planting area etc. were defined as constraints, respectively. Then, the model was solved using the Non-dominated Sorting Genetic Algorithm III (NSGA-III) and the Entropy-weighted-TOPSIS evaluation method. The developed model was demonstrated in the largest Farm, Youyi Farm, which is one of commercial grain production base in China to analyze the optimization of water and land resources from 2010 to 2019. We found that the new WCECM, based on the simulation of a complex coupled water-carbon-economy system, can realize the optimal allocation of agricultural water and land resources to protect regional water resources, increase carbon sequestration and adjust the agricultural planting structure. In detail, through the multi-objective optimization model, the planting structure and the allocation ratio of surface water and groundwater irrigation water consumption are more suitable for this study area. After the optimization, the area planted with Rice was significantly reduced, the area planted with Maize was increased, and the area planted with Soybean did not change significantly compared with the first two crops. The planting structure has changed from focusing on paddy cultivation to dryland cultivation, with the ratio of Rice area, Maize area and Soybean area being 3:6:1. The water consumption is constrained within manageable limits, with an average annual irrigation water consumption of 2.01 × 108 m3. The amount of carbon sequestered has increased significantly, with an average annual increase of 7.8 × 108 kg. Meanwhile, the optimized economic benefits increased slightly, with a value of ¥2.35 billion. In short, optimization of water and land resources is beneficial for improving farmers' incomes, increasing carbon sequestration in agriculture, and conserving water resources.

Risk-informed design of advanced nuclear reactor: A target unavailability approach

Incorporating specified safety and production targets during the design phase of an engineered facility can effectively support reductions in construction and operation costs of nuclear power plants. In addition, this supports efforts toward making nuclear power technology more competitive and profitable than other electric power production forms by incorporating operational and design features into the design phase that directly support the increased likelihood of meeting such originating targets. Unavailability is the key parameter to establish plant-level capabilities that cascade down to function, system, and component levels to ensure that all sources of unavailability are addressed in the facility's design phases. In this article, two methods are presented to propose unavailability targets for nuclear reactor systems to risk-inform and optimize the design features, from an availability perspective: the first method is a simple method, and the second is a non-dominated sorting genetic algorithm III (NSGA-III) multi-objective optimization. These iterative methods are demonstrated in this paper for a hypothetical facility with a design maturity such that there is knowing the main components to comply with the engineering and security functions of the systems. With an assumed mature design, it is implied that generic data to execute quantitative reliability, and availability analyses are available at the systems levels that are sufficient to support a design-specific probabilistic risk assessment (PRA). The proposed methods modify the probabilities of basic events within the fault tree structures that model system and component unavailability sources that will be required for the facility’s operating license. The methods provided are applied to visualize the characteristics of the problem and the quantitative results. This work forms the basis for future work in which design alternatives affecting unavailabilities can be estimated and compared to their associated unavailability targets at different levels of granularity.

Land use spatial optimization for city clusters under changing climate and socioeconomic conditions: A perspective on the land-water-energy-carbon nexus

Unbalanced land use development has resulted in water scarcity, high energy consumption, and a significant increase in carbon emissions. Complex and changing environments make it more difficult to manage land use sustainably. This study constructed a variety of future change scenarios coupling climate and socioeconomic development and developed a multidimensional land use spatial optimization (LUSO) model linked with water-energy-carbon. The model is solved using a nondominated sorting genetic algorithm-III (NSGA-Ⅲ) coupled with the information feedback model (IFM). The advantages of this framework include: (1) LUSO based on the clarification of the interactions between different land use types and water-energy-carbon; (2) trade-offs between dimensions are considered to achieve coordinated multidimensional development of the economy, resources, environment, and spatial conversion; and (3) a spatial optimization pattern of land use in response to climate change and socioeconomic development changes can be obtained. The model framework is applied to the Mid-Yangtze River City Cluster for empirical analysis. The results show that socioeconomic development can lead to rapid changes in land use patterns, especially in cultivated and construction land. By 2030, the optimized land use pattern under the intermediate route model is the most suitable land use scenario for the region to achieve sustainable development. Under this scenario, the area of construction land increased by 22.09%, the area of cultivated land decreased by 2.2%, the economic output increased by 76,678.31 (1012 yuan), and the carbon emissions increased by only 5677.79 (107 kg), with an overall sustainability level of 0.85. This study can provide decision-makers with sustainable land resource management options that can respond to changing environments and achieve multidimensional synergistic development.

Structural Optimization Design of Dual Robot Gripper Unloading Device Based on Intelligent Optimization Algorithms and Generative Design.

The main aim of this paper is to explore new approaches to structural design and to solve the problem of lightweight design of structures involving multivariable and multi-objectives. An integrated optimization design methodology is proposed by combining intelligent optimization algorithms with generative design. Firstly, the meta-model is established to explore the relationship between design variables, quality, strain energy, and inherent energy. Then, employing the Non-dominated Sorting Genetic Algorithm III (NSGA-III), the optimal frameworks of the structure are sought within the entire design space. Immediately following, a structure is rebuilt based on the principle of cooperative equilibrium. Furthermore, the rebuilt structure is integrated into a generative design, enabling automatic iteration by controlling the initial parameter set. The quality and rigidity of the structure under different reconstructions are evaluated, resulting in solution generation for structural optimization. Finally, the optimal structure obtained is validated. Research outcomes indicate that the quality of structures generated through the comprehensive optimization method is reduced by 27%, and the inherent energy increases by 0.95 times. Moreover, the overall structural deformation is less than 0.003 mm, with a maximum stress of 3.2 MPa-significantly lower than the yield strength and meeting industrial usage standards. A qualitative study and analysis of the experimental results substantiate the superiority of the proposed methodology for optimized structural design.

Parameter estimation of the hydraulic turbine regulator system using multi-objective lion algorithm

Accurate modeling of electric power generating unit and its hydraulic turbine regulation systems provides support for the speed controller synthesis and stability analysis. It is however a difficult task due to the presence of many non-linear factors in this system. an approach to estimate the parameters of hydraulic turbine regulatory system models is to derive the physical representation of each component and, through simulation, to compare to compare their models, outputs with real data obtained from a hydroelectric plant located in Brazil. The objective of this paper is to find the best values that will represent the system under study as a whole. This problem can be seen as an optimization problem. To find its feasible and optimal solution, this work proposes a new metaheuristics multi-objective based on the Lion Algorithm (LA), called the Multi-Objective Lion Algorithm (MOLA), and its application in the estimation of parameters of the system under study. In addition, the new metaheuristic proposed is validated by using a set of benchmark cases. The results have demonstrated that MOLA outperforms or at least performs similarly to Multi-objective Grey Wolf Optimizer (MOGWO), Multiple Objective Particle Swarm Optimization (MOPSO), Multi-objective Salp Swarm Algorithm (MSSA), Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D), and Non-dominated Sorting Genetic Algorithm III (NSGA-III) in the optimization of multi-objective benchmark functions. These results, suggest that the proposed MOLA algorithm works efficiently.

Multi-Objective Optimization Design of the External Rotor Permanent Magnet-Assisted Synchronous Reluctance Motor Based on the Composite Algorithm

Based on the complex structural characteristics of permanent magnet-assisted synchronous reluctance motors (PMA-SynRMs), this paper proposes a multi-objective optimization design method for the motor using a composite algorithm. Firstly, the power density, electromagnetic torque, cogging torque, and torque fluctuation coefficient were used as optimization targets based on parametric analysis data of 14 motor structure variables, where parametric sensitivity analysis helped select eight optimization variables. Secondly, the motor prediction model was fitted using the genetic algorithm–back propagation (GA-BP) neural network. Finally, non-dominated sorting genetic algorithm-III (NSGA-III), based on the reference points, was used to find the optimization of the prediction model and complete the multi-objective optimization design of the external rotor PMA-SynRM with eight inputs and four outputs. A comparative analysis of the electromagnetic performance of the motor before and after optimization verifies the feasibility of optimizing the motor using the composite algorithm. This paper provides an analytical tool for the multi-parameter and multi-objective PMA-SynRM optimization design.

A DQL-NSGA-III algorithm for solving the flexible job shop dynamic scheduling problem

In recent years, the flexible job shop dynamic scheduling problem (FJDSP) has received considerable attention; however, FJDSP with transportation resource constraint is seldom investigated. In this study, FJDSP with transportation resource constraint is considered and an improved non-dominated sorting genetic algorithm-III (NSGA-III) algorithm (DQNSGA) integrated with reinforcement learning (RL) is proposed. In DQNSGA, an initialization method based on heuristic rules and an insertional greedy decoding approach are designed, and a double-Q Learning with an improved ε-greedy strategy is used to adaptively adjust the key parameters of NSGA-III. An improved elite selection strategy is also applied. Through extensive experiments and practical case studies, this algorithm has been compared with three other well-known algorithms. The results demonstrate that DQNSGA exhibits significant effectiveness and superiority in all tests. The research presented in this paper enables effective adjustments of production plans in response to dynamic events, which is of critical importance for production management in the manufacturing industry.

Optimized Design of Sponge-Type Comprehensive Pipe Corridor Rainwater Chamber Based on NSGA-III Algorithm

Currently, most of the studies using optimization algorithms to mitigate the urban flooding problem have no more than three optimization objectives, and few of them take the operation status of the traditional drainage system as one of the optimization objectives, which is not conducive to the overall design of the city. This study proposes to mitigate the urban flooding problem by using a sponge-type comprehensive pipe corridor rainwater chamber. A four-objective optimization model is established by coupling the Storm Water Management Model (SWMM) and the Non-dominated Sorting Genetic Algorithm-III (NSGA-III), and two traditional drainage system state indicators (pipe overload time, node overflow volume), surface runoff coefficient, and total investment cost are selected as the optimization objectives for solving the problem. The results show that (1) the reduction rates of surface runoff coefficient, pipe overload time, and node overflow volume rate by the optimization model are 37.015–56.917%, 81.538–91.435%, and 51.578–84.963%, respectively; and the total investment cost is RMB 4.311–4.501 billion. (2) The effectiveness of combining SWMM and NSGA-III for an optimization solution is verified, and the relationship between the four objectives is explored. The study may provide useful information for urban flood control.

Trajectory optimization of wall-building robots using response surface and non-dominated sorting genetic algorithm III

Traditional wall-building robots regard brick masonry as a simple assembly process, ignoring the viscoelastic effect of cement mortar, which leads to poor masonry quality. Therefore, this paper proposes a many-objective trajectory optimization method based on response surface methodology (RSM) and non-dominated sorting genetic algorithm III (NSGA-III). Firstly, a substitution model between the objective functions and the design variables is established using RSM, which solves the problem of difficult construction of cement mortar viscoelastic model. Then, a trajectory optimization model is constructed based on seven times non-uniform B-spline, and it is solved using NSGA-III. Finally, the optimal solution is obtained from the Pareto solution set, and it is compared and analyzed with the standard gate-type masonry solution. The results show that after the trajectory optimization, the masonry error is reduced from 2.62 mm to 0.11 mm, which significantly improves the masonry quality, and other performance indicators are also improved. These results contribute to the construction of walls and promote the development of the smart construction industry.

The Use of Shape Accel Array for Deformation Monitoring and Parameter Inversion of a 300 m Ultrahigh Rockfill Dam

The global construction of rockfill dams has now surpassed the 300 m height level. Despite great achievements in dam design and construction, monitoring techniques have lagged behind the development of high rockfill dams. Existing deformation monitoring techniques are ill-suited to the high earth and water pressures, and extended monitoring periods are required for ultrahigh rockfill dams. This study introduces, for the first time, the use of a shape accel array (SAA) to monitor internal displacement in a 300 m high earth core rockfill dam. The SAA employs a rope-like array of capacitive MEMS accelerometers for deformation measurement. Compared to conventional monitoring techniques, SAA is a data-intensive monitoring technique. Based on the intensive data obtained from SAA, we employed a parameter inversion method, utilizing multiobjective optimization algorithm, the nondominated sorting genetic algorithm-III (NSGA-III), to inverse the constitutive model parameters of the rockfill dam. The multiobjective parameter inversion method maximizes the use of multisource monitoring data for predicting rockfill dam deformation.

Multi-objective architecture for strategic integration of distributed energy resources and battery storage system in microgrids

Microgrids with integrated renewable energy-based distributed generation (RDG) and battery energy storage systems (BESS) should be effectively designed and controlled to reap the potential benefits. In this context, this study recommends a novel Multi-objective Artificial Hummingbird Algorithm (MOAHA) based framework for optimal RDG allocation and sizing, along with BESS operation strategy, to enhance the voltage stability margin (VSM) and reduce the overall yearly expenses. In this work, the developed formulation has been evaluated on the IEEE 33-bus system, IEEE 69-bus system, and the Masirah Island distribution grid of Oman. Furthermore, the proposed method’s Pareto fronts are concluded to be superior to four of the recent metaheuristics employed in this research domain: Multi-objective Multiverse Optimization method (MOMVO), Multi-objective Equilibrium Optimization Technique (MOEOT), Multi-objective Particle Swarm Optimization (MOPSO), and Non-dominated Sorting Genetic Algorithm-III (NSGA-III). According to the findings, the MOAHA optimal Pareto solution candidates (PSC) for the three test systems satisfied the VSM constraints and cost objectives while providing substantial energy transfer during off-peak and peak demand hours. Additionally, all PSCs effectively avoided voltage violations, and the active power losses during each optimization period and the total energy losses were significantly reduced.