Sorting Genetic Algorithm Research Articles

Optimization of laser powder bed fusion process parameter for the fabrication of AlSi12 using NSGA‐II and Pareto search algorithm

AbstractAdditive manufacturing, notably laser powder bed fusion (LPBF), excels in producing complex geometries and is widely used in the automotive, aerospace, and naval industries. Laser powder bed fusion enables the creation of components with the required stiffness and strength at a lighter weight than traditional manufacturing methods. Aluminium alloys are particularly promising for laser powder bed fusion in the automotive and aerospace sectors. To enhance the effectiveness of laser powder bed fusion‐produced components, optimized process parameters must be designed for specific materials. This study investigates the influence of processing parameters, scan speed, scan strategy, and hatch space, on the relative density, surface roughness, and microhardness of AlSi12 samples fabricated by laser powder bed fusion. A Taguchi L27 orthogonal array was used to systematically analyze the effects of these parameters. A regression model was developed and evaluated through analysis of variance using signal‐to‐noise (S/N) ratios to identify optimal parameter values. Results indicated that the scan pattern significantly affects relative density, while hatch space impacts surface roughness and microhardness. Optimal solutions were obtained through multi‐objective optimization using the non‐dominated sorting genetic algorithm (NSGA‐II) and Pareto search algorithms. Experimental validation showed average errors of 0.483 % and 0.461 % for NSGA‐II and Pareto search algorithms, respectively.

A multiobjective optimization framework for site investigation program based on Bayesian approach and NSGA‐II

AbstractSite investigation provides essential geotechnical parameter information for analysis and design. However, three conflicting objectives, namely exploration effort, robustness and parameter uncertainty, pose a challenge to the development of an optimal site investigation program. In this study, a three objective optimization framework for the site investigation program is proposed based on the Bayesian approach and the non‐dominated sorting genetic algorithm (NSGA‐II). The only inputs required by the proposed framework are prior distribution of geotechnical parameters and error information. The prior distribution of geotechnical parameters is derived from integrating engineering experience and measurements from basic exploration boreholes. The error information is obtained based on literature and expert judgment related to the specific project. Firstly, a design pool of candidate investigation programs is generated using Bayesian approach to determine the locations and number of exploration boreholes. The NSGA‐II is then applied to identify the optimal program that balances lower cost, higher robustness, and lower uncertainty. The proposed multiobjective optimization framework is illustrated and validated through a real site investigation case in Chongqing, China, aimed at determining the ultimate bearing capacity of the rock foundation. The spatial correlation of parameters within the study area is also considered. The optimal program is represented by the location and number of exploration boreholes. By comparing measurements with predictions from different site investigation programs, the efficiency of the proposed multiobjective framework is demonstrated. Additionally, the influence of engineering experience and random field modeling on the investigation program is discussed.

Multi‐objective optimization of hybrid polypropylene composites for enhanced mechanical, thermal, and flame‐retardant properties

AbstractOptimizing composite materials is crucial for engineering advancements because it allows for the creation of materials tailored to specific functional requirements, thereby enhancing efficiency, safety, and sustainability. This study examines both the combined and individual effects of long flax fiber (LFF) bundles, short basalt fibers (BF), and rice husk powder (RHP) on the properties of polypropylene (PP) hybrid composites. LFF primarily provides mechanical strength, BF enhances mechanical, thermal, and flame‐retardant properties by filling gaps, and RHP reinforces and improves the overall composite. Contrary to previous studies that relied on random combinations, this research employs a systematic Box–Behnken design (BBD) for three variables: LFF plies, BF, and RHP by weight percentage. This approach facilitated the development of a new second‐order regression equation via response surface methodology (RSM), clarifying the contribution of each component, with R2 values exceeding 0.85, indicating high predictability. Optimization, conducted using a non‐dominated sorting genetic algorithm (NSGA‐II), demonstrated that the optimal composites significantly outperformed the non‐optimized ones in mechanical strength, thermal stability, and flame retardancy. Compared to the average values before optimization, improvements post‐optimization included increases of 49.86% in tensile strength (TS), 38.82% in TS modulus, 73.93% in char yield (YC), and 29.81% in peak heat release rate (pHRR). Specifically, the fire performance index improved by 237.5% and the fire growth index by 101.33%, compared to pure PP, highlighting significant advancements in fire safety. This study shows that mathematical equations can predict composite properties, helping to select balanced compositions without sacrificing flammability.Highlights Multi‐filler approach technique used for manufacturing of PP composites. LFF, BF & RHP optimize mechanics & flammability. Box–Behnken design predicts optimal composition for superior properties. NSGA‐II optimization improves tensile, thermal & flame retardant properties.

Optimal hybrid layout of point and open‐path gas detectors in process facilities of natural gas stations

AbstractBecause open‐path gas detectors offer superior technical performance, leakage monitoring systems tend to use a hybrid layout of point and open‐path gas detectors. However, most research on the optimized layout of leakage monitoring has focused only on point gas detectors. In this paper, an optimization model for a hybrid layout of point and open‐path gas detectors was proposed considering the cost–benefit ratio and detection time. As the measured value of an open‐path gas detector is an integral concentration, a monitor line in the simulation was calculated via the approximate rectangle method. The results showed that the use of a multiobjective gas detector layout considering the cost–benefit ratio and an improved non‐dominated sorting genetic algorithm (NSGA‐II) could optimize the layout of hybrid detectors. The hybrid layout was analyzed in a case study of process facilities at a natural gas station. As the number of detectors increased (safety investment), the proportion of open‐path gas detectors increased, improving both leakage monitoring performance and the cost–benefit ratio. Additionally, the layout of the point gas detectors was analyzed. A comparison of objective function values for detection time showed the superiority of the hybrid layout in our research.

Optimization design of a hybrid thermal runaway propagation mitigation system for power battery module using high-dimensional surrogate models

The demand for high energy density in lithium-ion battery packs for electric vehicles poses a challenge to maintaining its optimum operating temperature while reducing the risk of thermal runaway (TR) propagation. This study proposes a novel hybrid TR propagation mitigation system that balances heat transfer and thermal insulation requirements using low and high thermal conductivity phase change materials (PCM), heat pipes (HP), and air-cooling. The design and optimization of such a mitigation system are complex due to the many design parameters involved. The Adaptive-Kriging-High dimensional model representation (Adaptive-Kriging-HDMR) is used to establish a surrogate model of the system, and the sensitivity of the system's design parameters is evaluated with the maximum battery temperature and the system weight as the targets, thereby improving the efficiency of model calculation and reducing the dimension of optimization parameters. Then, the design of the sensitive parameters is optimized using an extended elitist non‐dominated sorting genetic algorithm (E-NSGA-II) multi-objective optimization algorithm. The results show that the modeling difficulty and optimization calculation time are significantly reduced by using a surrogate model. The calculation time for a single surrogate model only takes a few seconds instead of several hours for the original three-dimensional heat transfer and flow calculation. The thermal conductivity of high thermal conductivity-PCM, the distance between battery and low thermal conductivity-PCM, the battery spacing, and the HP length significantly affect the system. The optimized system substantially reduces the overall weight of the battery system while ensuring its good heat dissipation capability. In the case of TR in a single battery, the system succeeds in limiting the TR propagation to the same row, with the maximum battery temperature in the second row being only 64.3 °C, well below the battery TR trigger point. Under more severe conditions, such as TR occurring in two batteries simultaneously, the maximum battery temperature in the second row is 155.5 °C, and no TR spreads to the adjacent row. This study provides a rapid and effective method for designing a TR propagation mitigation system. It can serve as a reference for the engineering design and optimization of battery thermal management systems.

Stability and thermodynamic property of TiO2/R141b nanorefrigerants by multi‐objective optimization

AbstractUtilizing nanorefrigerants as working fluids can significantly enhance the energy efficiency of low‐temperature waste heat recovery systems (≤ 50°C). Refrigerants with low viscosity and density require a substantial amount of surfactant to maintain a stable suspension of nanoparticles. However, the excessive use of surfactants, which have a notably low thermal conductivity, could lead to foam generation and reduce heat transfer coefficient. High viscosity lubricating oils with small amount of surfactant can prolong the stable suspension time and produce repulsive force. Therefore, a new combination of them improves the stability of TiO2/R141b nanorefrigerants. Additionally, viscosity and thermal conductivity of the nanorefrigerants were optimized using an implementation of a modified non‐dominated sorting genetic algorithm (NSGA‐II). The results show that adding lubricating oil inhibits aggregation of the nanoparticles leading to a stable suspension for more than 6 h at volumetric mixing ratios (lubricating oil: refrigerant) greater than 1:30. The best dispersion stability was achieved at surfactant polyvinyl pyrrolidone (PVP) mass ratio of 0.5, and the average absorbance value was increased by 65.45%. Compared with pure refrigerants, the thermal conductivity of TiO2/R141b (0.15 vol.%) nanorefrigerant was enhanced by up to 12.59% under the optimum mixing ratio. Moreover, the studied nanorefrigerants exhibited shear thickening behavior throughout the studied shear rate range, with increased non‐Newtonianization with decreasing temperature. Finally, the Pareto points were divided into three representative groups based on thermal conductivity and viscosity. These findings suggest enhanced high heat transfer efficiency with pumping power of nanorefrigerant in the waste heat recovery systems.

Cooperative optimal operation of hybrid energy integrated system considering multi‐objective dragonfly algorithm

AbstractTo meet the recent energy demand, hybrid energy systems (HESs) play an important role in providing more stable power as well as backup power to the grid. It is always preferable for the power system network to be operated in a secure, dependable, stable, and cost‐effective manner. The optimal power flow (OPF) problem is used to determine the ideal power system network control parameter settings. The majority of the OPF problem, fuel cost reduction is treated as an objective function, but the environmental impact of the generating is ignored. Both fuel expense and emission are treated as objective functions of the work in this case. This research describes a solution for multi‐objective optimal power flow (MOOPF) with a HES integrated conventional power system. HES in this work is made up of a wind park and a compressed air energy storage system (CAES). In this case, locational marginal pricing (LMP) is employed to determine the best location of HES in the power system. To achieve the goals of this work, the multi‐objective dragonfly algorithm (MODA) is used. The IEEE 30 bus system is used to evaluate the MODA. The MODA method findings are validated against other well‐known optimization algorithms such as the multi‐objective ant colony system (MOACS) algorithm, multi‐objective enhanced self‐adaptive differential evolution (MOESDE) algorithm, modified multi‐objective evolutionary algorithm‐based decomposition (MOEA/D), multi‐objective particle swarm algorithm (MOPOSO), and non‐dominated sorting genetic algorithm (NSGA‐II).

Research on the lightweight design and multilevel optimization method of B‐pillar of hybrid material based on side‐impact safety and multicriteria decision‐making

AbstractThis paper designs and optimizes a new structure hybrid material B‐pillar assembly. Establish a finite element analysis model for the side impact test of an electric vehicle to verify the model's accuracy. Construct a finite element model of the hybrid material B‐pillar assembly. Combined with the static performance analysis of the B‐pillar, the laminate design of the carbon fiber composite B‐pillar reinforcement plate and optimization. For the outer and inner panels of the B‐pillar, the surrogate model method and the multiobjective optimization method are combined to set 11 design variables and 13 responses. The optimal Latin hypercube design method was used to randomly sample each design variable and fit the radial basis function (RBF) approximate model. The modified second‐generation non‐dominated sorting genetic algorithm (MNSGA‐II) was used to carry out multiobjective optimization of the mixed material B‐pillar assembly, and the multicriteria decision‐making method was used to obtain the optimal solution for the Pareto solution set. A drop weight impact test was performed on the hybrid material B‐pillar assembly. The results showed that the peak collision force of the hybrid material B‐pillar was reduced by 43.7% compared with the original steel B‐pillar, the specific energy absorption was increased by 17.5%, and the side impact safety performance was better. After optimization, the weight of the mixed material B‐column was reduced by 26.44%. The improvement rates of the intrusion amount at P2 and the intrusion speed at P1 were 23.99% and 0.63%, respectively, and the lightweight effect and side impact safety were significantly improved.Highlights The finite element analysis parameters of carbon fiber composite materials were constructed through experiments, and the basis for simulation analysis under various working conditions of the B‐pillar based on performance‐driven optimization was established. A hybrid material B‐pillar design method is proposed, which consists of a variable‐thickness high‐strength steel B‐pillar outer plate, a variable‐thickness high‐strength steel B‐pillar inner plate, and a carbon fiber composite B‐pillar reinforcement plate. The super layer of the carbon fiber composite B‐pillar reinforcement plate was optimized and the laying plan of the carbon fiber composite B‐pillar reinforcement plate was determined. Performance‐driven optimization is adopted to realize the collaboration of multilevel design and optimization methods such as B‐pillar conceptual design, detailed design, and multiobjective optimization. A modified NSGA‐II algorithm was proposed and combined with the multicriteria decision‐making method and the entropy‐weighted gray relation analysis method to determine the Pareto optimal solution and the optimal design scheme for the mixed material B‐pillar.

Analytical modelling of ultra‐small group delay variation of ultra‐broadband RF power amplifier using NSGA‐II algorithm

AbstractThis paper proposes a ± 9.4 ps ultra‐small group delay (GD) variation of fully integrated 65 nm complementary metal oxide semiconductor (CMOS) power amplifier (PA) over 6.5–17 GHz broadband for wireless application. The proposed CMOS PA is realised by using broadband stage, RLC inter‐stage and power stage topologies. The non‐dominated sorting genetic algorithm (NSGA‐II) is employed for PA parameter optimisation to ensure a small GD variation of ±9.4 ps over broadband with an excellent small signal gain flatness of 23.65 ± 1.85 for 6.5–17 GHz. The small GD variation of ±9.4 ps and ± 11.05 ps are attained under two cases of DC supply voltages of 2.4/1.2 V and 1.2/1.2 V, respectively. To the best of author's knowledge, the achieved GD variations are lowest among all CMOS PAs as reported so far. In addition, an analytical modelling of GD is derived to validating the minimum GD variation using zero‐pole compensation. With supply voltages of 2.4/1.2 V at 6.5 GHz, the large signal power gain, Psat and OP1dB are 26 dB, 19.3 dBm and 17.94 dBm, respectively, while peak power added efficiency (PAE) is 38.196%. At reduced supply voltages of 1.2/1.2 V, the PA achieves maximum power gain of 17.7 dB and peak PAE of 35% at 6.5 GHz. The CMOS PA occupies an area of 0.206 mm2.

Modelling of compression ignition engine by soft computing techniques (ANFIS-NSGA-II and RSM) to enhance the performance characteristics for leachate blends with nano-additives

Integrating nanoparticles in waste oil-derived biodiesel can revolutionize its performance in internal combustion engines, making it a promising fuel for the future. Nanoparticles act as combustion catalysts, enhancing combustion efficiency, reducing emissions, and improving fuel economy. This study employed a comprehensive approach, incorporating both quantitative and qualitative analyses, to investigate the influence of selected input parameters on the performance and exhaust characteristics of biodiesel engines. The focus of this study is on the potential of using oils extracted from food waste that ended up in landfills. The study's results are analysed and compared with models created using intelligent hybrid prediction approaches including adaptive neuro-fuzzy inference system, Response surface methodology-Genetic algorithm, and Non sorting genetic algorithm. The analysis takes into account engine load, blend percentage, nano-additive concentration, and injection pressure, and the desired responses are the thermal efficiency and specific energy consumption of the brakes, as well as the concentrations of carbon monoxide, unburned hydrocarbon, and oxides of nitrogen. Root-mean-square error and the coefficient of determination were used to assess the predictive power of the model. Comparatively to Artificial Intelligence and the Response Surface Methodology-Genetic Algorithm model, the results provided by NSGA-II are superior. This is because it achieved a pareto optimum front of 24.45 kW, 2.76, 159.54 ppm, 4.68 ppm, and 0.020243% for Brake Thermal Efficiency, Brake Specific Energy Consumption, Oxides of nitrogen, Unburnt Hydro Carbon, and Carbon monoxide. Combining the precision of ANFIS's prediction with the efficiency of NSGA-optimization II's gives a reliable and thorough evaluation of the engine's settings. The qualitative assessment considered practical aspects and engineering constraints, ensuring the feasibility of applying the parameters in real-world engine applications.

Resilience evaluation and optimal design for weapon system of systems with dynamic reconfiguration

The weapon system of systems (WSoS) is a collection of various weapon systems used to achieve a common goal and mission. Stakeholders are committed to lowering design costs while achieving greater resilience. In this paper, we establish a resilience evaluation and optimal design method for WSoS under different attack and reconfiguration strategies that can balance its resilience and cost. Firstly, a WSoS-oriented three-phase resilience model and evaluation algorithm are developed, taking into account resistance, adaptability, and recovery factors. Then, a resilience-based WSoS cost model is presented to assess the cost of new equipment, improved equipment, and resilience-associated costs. Additionally, a resilience-based optimization model is proposed that considers reduced costs and increased resilience. Non-dominated sorting in genetic algorithms: II (NSGA-II) is selected to achieve the Pareto optimal solution. Finally, a typical 5-node formation air defense WSoS is utilized to illustrate the resilience evaluation and optimization design, which can serve as a reference for WSoS design.

Multi‐objectives optimization of parameter design for LLC converter based on data‐driven surrogate model

AbstractDue to high computation complexity, traditional design methods for LLC converter usually consider limited kinds of performance, and the process for searching the optimal parameter scheme is discrete, which might cause the missing of real optimal solution. In order to solve these problems, a multi‐objectives optimization design method for the LLC converter is proposed in this paper. According to the numerical calculation method, a closed parameter space for resonant parameters is established under the constraints of operation mode, switching frequency, zero voltage switching (ZVS), and voltage stress of resonant capacitor. Besides, the root‐mean‐square (RMS) value of resonant current and secondary current, as well as the core loss of the transformer, are selected as the optimization objectives. Non‐dominated sorting genetic algorithm (NSGA‐II) is utilized to deal with these multi‐objective formulations. In order to reduce the computation complexity, a data‐driven method, called adaptive polynomial approximation (APA), is selected to obtain the explicit expressions of the parameter space and the optimization objectives. Then, by substituting this simplified surrogate model into the NSGA‐II algorithm, the optimal parameter scheme is obtained. The comprehensive comparison analysis is performed, and a 400 W/48 V experimental prototype was built to verify the theoretical analysis, which shows that the proposed method features the highest efficiency of 95%.

An optimal joint planning of DGs and electric vehicle charging stations in grid‐connected and islanded microgrids

AbstractThis paper proposes a new framework for the planning of both distributed generators (DGs) and electric vehicle charging stations (EVCSs). The proposed method efficiently produces a unified solution for the joint planning of DGs and EVCSs for both grid‐connected and islanded scenarios. The problem is formulated as a novel two‐stage planning problem. The first stage determines the locations and sizes of the DGs with locations of EVCSs in grid‐connected scenario, whereas the second stage planning identifies the optimal islands under the islanded microgrid scenario. A non‐dominated sorting genetic algorithm (NSGA‐II) is applied to solve the first stage planning problem; in this stage, the algorithm minimizes two objective functions: the system‐losses and total cost. In the second stage, another single objective optimization problem is designed which minimizes supply voltage variations to find optimal islands for the DGs and EVCSs to ensure a secure supply of power for EVs. The proposed framework is implemented on the IEEE 33‐bus system and verified with four test cases. The results demonstrate the effectiveness of the proposed method and show that the sizes and locations of DGs, and locations of EVCSs are adequate for both grid‐connected and islanded microgrids.

Wireless body area networks task offloading method combined with multiple communication and computing resources supported by MEC

AbstractIn recent years, mobile edge computing (MEC) has become a promising solution to solve the shortage of technical resources in wireless body area networks (WBANs). However, the existing research work has not fully utilized the communication and computing resources in WBANs scenarios. To solve this problem, a task offloading framework that combined with cellular, WiFi networks and device‐to‐device communications is proposed, that makes full use of resources to improve system reliability. Considering that a single MEC server may be overloaded by a large number of patients, the total task offloading cost and load variance is formulated into a multi‐objective optimization problem (MOOP). A non‐dominated sorting genetic algorithm with smart mobile device ‐ patient connection matrix (NSGA ‐SPCM) to solve the MOOP. In view that an SDM may connect multiple patients at the same time during chromosome crossing, the SPCM can quickly detect the unfeasible gene location and mutate it into viable. Simulation results show that the proposed framework and algorithm have good performance.

Solving a multiobjective professional timetabling problem using evolutionary algorithms at Mandarine Academy

AbstractIn this paper, we propose a method for solving a real‐world timetabling problem at Mandarine Academy. The primary motivation for this work is to provide an automated professional course scheduling tool to replace the time‐consuming task of manually creating timetables that are constantly incorrect. Following a review of both scientific literature and company requirements, a mathematical model of the problem is provided, which includes 18 constraints (hard/soft) and five objectives, two of which are competing. We test a handful of multi‐objective evolutionary algorithms (MOEA's) starting with the non‐dominated sorting genetic algorithm (NSGA II and NSGA III), the multi‐objective evolutionary algorithm based on decomposition (MOEA/D), the indicator‐based evolutionary algorithm and finally the strength Pareto evolutionary algorithm . Two custom genetic operators (mutation and crossover) are proposed and compared to conventional operators (PMX and swap mutation). To obtain elite configurations, a tuning phase involving all of the aforementioned algorithms is carried out. Experiments were divided by problem size, with three to five objectives tested. Experiments include the use of real‐world data from the company's catalog. This dataset was made available to the scientific community to serve as a testing ground for professional course scheduling, an underexploited field of scheduling. We discuss findings, including a comparison of each algorithm's performance using various metrics, as well as convergence graphs and population evolution.

Towards a more flexible demand responsive transit service with compensation mechanism considering boundedly rational passengers

AbstractDemand responsive transit (DRT) with app‐based reservation platforms is experiencing a renaissance to bring the tremendous potential for mobility in the urban universe. Nevertheless, how to attract and retain passengers for long‐term use has become one of the most significant problems. The decision‐making psychology of passengers is often overlooked but incredibly critical in the practical applicability of DRT services. This paper proposes a more flexible DRT service with soft time windows considering boundedly rational passengers. A compensation mechanism is developed to alleviate the dissatisfaction of passengers while considerably promoting the system efficiency. A two‐stage model is designed to incorporate bounded rationality into the optimization process of mixed demand, including the static phase for reservation passengers and the dynamic phase for real‐time passengers. To enhance the computational efficiency, a hybrid heuristic algorithm combining spatiotemporal clustering and non‐dominated sorting genetic algorithm (NSGA)‐II is constructed to obtain the Pareto solutions set. An illustrative example of the Nguyen–Dupuis network is presented to demonstrate the validity of the algorithm. Subsequently, a large‐scale case study in Beijing evaluated the applicability of DRT in the real‐world network. The results reveal that dynamic DRT with compensation mechanism can substantially improve the system performance while ensuring the service quality. The response rate of passengers has been dramatically promoted to 80%. The operating profit has been enormously improved by up to 73%. Therefore, this study is radically conducive to understanding the passenger's decision‐making psychology while constructing a more cost‐efficient flexible strategy for the service provider.

Genetic optimization design for aerodynamic structure of oblique flow compressor radial inlet chamber in compressed air energy storage system

AbstractThe compressor plays a significant role in the compressed air energy storage (CAES) system, and its performance directly determines the overall efficiency of the system and the economy of energy storage. Internal flow losses and outlet distortion due to poor design of its radial inlet chamber (RIC) geometry can degrade the performance of downstream components and reduce compressor unit performance. In this article, a multiobjective optimization design is carried out for the RIC of the oblique flow compressor in CAES system, and a parametric optimization design method of the RIC annular convergent channel meridian profile is developed. The optimal Latin hypercube experimental design (opt LHD) method is used to generate the sample library, and the radial basis function neural network (RBFNN) is used to establish the surrogate model of each design parameter and RIC performance. The multiobjective optimization is executed by the second‐generation non‐dominated sorting genetic algorithm (NSGA‐II). After optimization, the area of the annular convergent channel of the RIC is expanded, the transition of the meridian profile is smoother, the distortion coefficient of the RIC is reduced by 0.58%, and the total pressure loss coefficient is reduced by 16.45%. By using the optimized RIC, the performance of oblique flow compressor has been improved. Compared with the original model, the total pressure ratio and isentropic efficiency of the whole machine at the design point have been increased by 0.6% and 0.32%, respectively.

Multi‐parameter optimization of performance and economic viability of Ferris wheel wind turbine for low wind speed regions in Africa

AbstractPrevious studies on wind turbine and wind farm optimization for Levellized cost of energy (LCOE) and annual energy production (AEP) have focused on horizontal axis wind turbines (HAWT) and vertical axis wind turbines (VAWT). Regions with lower wind speed resources tend to have a higher levellized cost of energy and lower annual energy production. In this paper, the authors investigate the optimization of a novel, Ferris wheel wind turbine (FWT) for low wind speed regions of Africa. The research used an Excel‐based Multi‐Objective Optimization (EMOO) model. The EMOO program has both binary‐coded and real‐coded Elitist Non‐Dominated Sorting Genetic Algorithm (NSGA‐II). The optimization is conducted by studying the effect of varying the rim diameter, number of blades, and the rated wind speeds for an 800‐kW generator on the performance and economics in 21 African cities. The results show that, on average, the return‐on‐investment increases over the base design by up to 182%, and both the simple payback period (SPP) and the levellized cost of electricity decreased by 39% as the rim diameter increases combined with a 50% reduction in blade numbers. In addition, a 75% reduction in blade numbers caused a further 32% decrease on average for both the simple payback period and the levellized cost of electricity.

Power distribution network expansion planning to improve resilience

AbstractHigh‐impact, low‐probability events that cause significant annual damages seriously threaten the health of distribution networks. The effects of these events have made the expansion planning for distribution systems something beyond the traditional reliability criteria, so there is an ever‐increasing need for modifications in current planning approaches and focusing on the resilience in the expansion planning of distribution networks. The new attitude dealing with resilience and distributed generation sources in distribution networks necessitates a fundamental reconsidering of traditional distribution network planning methods. Here, by modelling common natural disasters such as floods and storms, an appropriate index is introduced to evaluate the distribution network resilience in the presence of distributed generation (DG) sources, including conventional gas‐fired and photovoltaic sources. Then, by presenting an appropriate model for load and photovoltaic production, the problem of comprehensive distribution network planning, including substations, feeders, and DG sources, is mathematically formulated as a multi‐objective optimization problem to improve resilience and optimize costs. Furthermore, a non‐dominated sorting genetic algorithm is used to solve the problem of comprehensively planning a resilient distribution network. Implementation of the proposed model on the IEEE 54‐bus sample network shows that network resilience can be improved with minimum cost by optimal network planning.

SONG: A Multi-Objective Evolutionary Algorithm for Delay and Energy Aware Facility Location in Vehicular Fog Networks.

With the emergence of delay- and energy-critical vehicular applications, forwarding sense-actuate data from vehicles to the cloud became practically infeasible. Therefore, a new computational model called Vehicular Fog Computing (VFC) was proposed. It offloads the computation workload from passenger devices (PDs) to transportation infrastructures such as roadside units (RSUs) and base stations (BSs), called static fog nodes. It can also exploit the underutilized computation resources of nearby vehicles that can act as vehicular fog nodes (VFNs) and provide delay- and energy-aware computing services. However, the capacity planning and dimensioning of VFC, which come under a class of facility location problems (FLPs), is a challenging issue. The complexity arises from the spatio-temporal dynamics of vehicular traffic, varying resource demand from PD applications, and the mobility of VFNs. This paper proposes a multi-objective optimization model to investigate the facility location in VFC networks. The solutions to this model generate optimal VFC topologies pertaining to an optimized trade-off (Pareto front) between the service delay and energy consumption. Thus, to solve this model, we propose a hybrid Evolutionary Multi-Objective (EMO) algorithm called Swarm Optimized Non-dominated sorting Genetic algorithm (SONG). It combines the convergence and search efficiency of two popular EMO algorithms: the Non-dominated Sorting Genetic Algorithm (NSGA-II) and Speed-constrained Particle Swarm Optimization (SMPSO). First, we solve an example problem using the SONG algorithm to illustrate the delay-energy solution frontiers and plotted the corresponding layout topology. Subsequently, we evaluate the evolutionary performance of the SONG algorithm on real-world vehicular traces against three quality indicators: Hyper-Volume (HV), Inverted Generational Distance (IGD) and CPU delay gap. The empirical results show that SONG exhibits improved solution quality over the NSGA-II and SMPSO algorithms and hence can be utilized as a potential tool by the service providers for the planning and design of VFC networks.