Academic literature on the topic 'Nondominated sorting genetics algorithm (C-NSGA-II)'

Aluminium bronzes possess a unique combination of high strength and wear and corrosion resistance in aggressive environments; thus, these alloys find wide application in marine, shipbuilding, aviation, railway, offshore platform applications and other fields. Iron-aluminium bronzes (IABs) are the cheapest and most widely used. When the aluminium content is above 9.4 wt%, IAB is biphasic (i.e., it undergoes β-transformation) and can be subjected to all heat-treatment types, depending on the desired operating behaviour of the bronze component. This article presents correlations (mathematical models) between the primary mechanical characteristics (yield limit, tensile strength, elongation, hardness and impact toughness) and the ageing temperature and time of quench at 920 °C in water of Cu-11Al-6Fe bronze, obtained using the centrifugal casting method. The microstructure evolution was evaluated depending on the ageing temperature and time changes. Overall, the research was conducted in three successive inter-related stages: a one-factor-at-a-time study, planned experiment, and optimisations. Four optimisation tasks, which have the greatest importance for practice, were formulated and solved. The defined multiobjective optimisation tasks were solved by searching for the Pareto-optimal solution approach. The decisions were made through a nondominated sorting genetic algorithm (NSGA-II) using QstatLab. The optimisation results were verified experimentally. Additional samples were made for this purpose, quenched at 920 °C in water and subjected to subsequent ageing with the optimal values of the governing factors (ageing temperature and time) for the corresponding optimisation task. The comparison of the results for the mechanical characteristics with the theoretical optimisation results presents a good agreement.

This study aimed to optimize the postharvest blanching and drying process of blueberries using high-humidity air impingement (HHAIB) and hot-air-assisted infrared (HAIR) heating. A novel pilot-scale hot-air-assisted carbon-fiber infrared (IR) blanching/drying system was developed. Fresh blueberries with an average diameter of 10~15 mm were first blanched with high-humidity air at 110 °C and 12 m/s velocity for different durations (30, 60, 90, and 120 s); subsequently, the preblanched blueberries were dried at different IR heating temperatures (50, 60, 70, 80, and 90 °C) and air velocities (0.01, 0.5, 1.5, and 2.5 m/s), following a factorial design. The drying time (DT), specific energy consumption (SEC), ascorbic acid content (VC), and rehydration capacity (RC) were determined as response variables. A three-layer feed-forward artificial neural network (ANN) model with a backpropagation algorithm was constructed to simulate the influence of blanching time, IR heating temperature, and air velocity on the four response variables by training on the experimental data. Objective functions for DT, SEC, VC, and RC that were developed by the ANN model were used for the simultaneous minimization of DT and SEC and maximization of VC and RC using a nondominated sorting genetic algorithm (NSGA II) to find the Pareto-optimal solutions. The optimal conditions were found to be 93 s of blanching, 89 °C IR heating, and a 1.2 m/s air velocity, which resulted in a drying time of 366.7 min, an SEC of 1.43 MJ/kg, a VC of 4.19 mg/100g, and an RC of 3.35. The predicted values from the ANN model agreed well with the experimental data under optimized conditions, with a low relative deviation value of 1.43–3.08%. The findings from this study provide guidance to improve the processing efficiency, product quality, and sustainability of blueberry postharvest processes. The ANN-assisted optimization approach developed in this study also sets a foundation for the smart control of processing systems of blueberries and similar commodities.

A new closed-loop supply chain logistics network of vehicle routing problem with simultaneous pickups and deliveries (VRPSPD) dominated by remanufacturer is constructed, in which the customers are originally divided into three types: distributors, recyclers, and suppliers. Furthermore, the fuel consumption is originally added to the optimization objectives of the proposed VRPSPD. In addition, a bee evolutionary algorithm guiding nondominated sorting genetic algorithm II (BEG-NSGA-II) with a two-stage optimization mechanism is originally designed to solve the proposed VRPSPD model with three optimization objectives: minimum fuel consumption, minimum waiting time, and the shortest delivery distance. The proposed BEG-NSGA-II could conquer the disadvantages of traditional nondominated sorting genetic algorithm II (NSGA-II) and algorithms with a two-stage optimization mechanism. Finally, the validity and feasibility of the proposed model and algorithm are verified by simulating an engineering machinery remanufacturing company’s reverse logistics and another three test examples.

Most of the modern multiobjective optimization algorithms are based on the search technique of genetic algorithms; however the search techniques of other recently developed metaheuristics are emerging topics among researchers. This paper proposes a novel multiobjective optimization algorithm named multiobjective heat transfer search (MOHTS) algorithm, which is based on the search technique of heat transfer search (HTS) algorithm. MOHTS employs the elitist nondominated sorting and crowding distance approach of an elitist based nondominated sorting genetic algorithm-II (NSGA-II) for obtaining different nondomination levels and to preserve the diversity among the optimal set of solutions, respectively. The capability in yielding a Pareto front as close as possible to the true Pareto front of MOHTS has been tested on the multiobjective optimization problem of the vehicle suspension design, which has a set of five second-order linear ordinary differential equations. Half car passive ride model with two different sets of five objectives is employed for optimizing the suspension parameters using MOHTS and NSGA-II. The optimization studies demonstrate that MOHTS achieves the better nondominated Pareto front with the widespread (diveresed) set of optimal solutions as compared to NSGA-II, and further the comparison of the extreme points of the obtained Pareto front reveals the dominance of MOHTS over NSGA-II, multiobjective uniform diversity genetic algorithm (MUGA), and combined PSO-GA based MOEA.

In general, the proximities to a certain diversity along the front and the Pareto front have the equal importance for solving multiobjective optimization problems (MOPs). However, most of the existing evolutionary algorithms give priority to the proximity over the diversity. To improve the diversity and decrease execution time of the nondominated sorting genetic algorithm II (NSGA-II), an improved algorithm is presented in this paper, which adopts a new vector ranking scheme to decrease the whole runtime and utilize Part and Select Algorithm (PSA) to maintain the diversity. In this algorithm, a more efficient implementation of nondominated sorting, namely, dominance degree approach for nondominated sorting (DDA-NS), is presented. Moreover, an improved diversity preservation mechanism is proposed to select a well-diversified set out of an arbitrary given set. By embedding PSA and DDA-NS into NSGA-II, denoted as DNSGA2-PSA, the whole runtime of the algorithm is decreased significantly and the exploitation of diversity is enhanced. The computational experiments show that the combination of both (DDA-NS, PSA) to NSGA-II is better than the isolated use cases, and DNSGA2-PSA still performs well in the high-dimensional cases.

Fast nondominated sorting genetic algorithm II (NSGA-II) is a classical method for multiobjective optimization problems and has exhibited outstanding performance in many practical engineering problems. However, the tournament selection strategy used for the reproduction in NSGA-II may generate a large amount of repetitive individuals, resulting in the decrease of population diversity. To alleviate this issue, Lévy distribution, which is famous for excellent search ability in the cuckoo search algorithm, is incorporated into NSGA-II. To verify the proposed algorithm, this paper employs three different test sets, including ZDT, DTLZ, and MaF test suits. Experimental results demonstrate that the proposed algorithm is more promising compared with the state-of-the-art algorithms. Parameter sensitivity analysis further confirms the robustness of the proposed algorithm. In addition, a two-objective network topology optimization model is then used to further verify the proposed algorithm. The practical comparison results demonstrate that the proposed algorithm is more effective in dealing with practical engineering optimization problems.

Single-objection function cannot describe the characteristics of the complicated hydrologic system. Consequently, it stands to reason that multiobjective functions are needed for calibration of hydrologic model. The multiobjective algorithms based on the theory of nondominate are employed to solve this multiobjective optimal problem. In this paper, a novel multiobjective optimization method based on differential evolution with adaptive Cauchy mutation and Chaos searching (MODE-CMCS) is proposed to optimize the daily streamflow forecasting model. Besides, to enhance the diversity performance of Pareto solutions, a more precise crowd distance assigner is presented in this paper. Furthermore, the traditional generalized spread metric (SP) is sensitive with the size of Pareto set. A novel diversity performance metric, which is independent of Pareto set size, is put forward in this research. The efficacy of the new algorithm MODE-CMCS is compared with the nondominated sorting genetic algorithm II (NSGA-II) on a daily streamflow forecasting model based on support vector machine (SVM). The results verify that the performance of MODE-CMCS is superior to the NSGA-II for automatic calibration of hydrologic model.

A kind of unrelated parallel machines scheduling problem is discussed. The memberships of fuzzy due dates denote the grades of satisfaction with respect to completion times with jobs. Objectives of scheduling are to maximize the minimum grade of satisfaction while makespan is minimized in the meantime. Two kind of genetic algorithms are employed to search optimal solution set of the problem. Both Niched Pareto Genetic Algorithm (NPGA) and Nondominated Sorting Genetic Algorithm (NSGA-II) can find the Pareto optimal solutions. Numerical simulation illustrates that NSGA-II has better results than NPGA.

Flexible job-shop scheduling problem (FJSP) is an NP-hard puzzle which inherits the job-shop scheduling problem (JSP) characteristics. This paper presents a bee evolutionary guiding nondominated sorting genetic algorithm II (BEG-NSGA-II) for multiobjective FJSP (MO-FJSP) with the objectives to minimize the maximal completion time, the workload of the most loaded machine, and the total workload of all machines. It adopts a two-stage optimization mechanism during the optimizing process. In the first stage, the NSGA-II algorithm withTiteration times is first used to obtain the initial populationN, in which a bee evolutionary guiding scheme is presented to exploit the solution space extensively. In the second stage, the NSGA-II algorithm withGENiteration times is used again to obtain the Pareto-optimal solutions. In order to enhance the searching ability and avoid the premature convergence, an updating mechanism is employed in this stage. More specifically, its population consists of three parts, and each of them changes with the iteration times. What is more, numerical simulations are carried out which are based on some published benchmark instances. Finally, the effectiveness of the proposed BEG-NSGA-II algorithm is shown by comparing the experimental results and the results of some well-known algorithms already existed.

With the deteriorating environment and excessive consumption of primary energy, solar energy has become used in buildings worldwide for renewable energy. Due to the fluctuations of solar radiation, a solar photovoltaic (PV) power system is often combined with a storage battery to improve the stability of a building’s energy supply. In addition, the real-time energy consumption pattern of the residual houses fluctuates; a larger size for a PV and battery integrated system can offer more solar energy but also bring a higher equipment cost, and a smaller size for the integrated system may achieve an energy-saving effect. The traditional methods to size a PV and battery integrated system for a detached house are based on the experience method or the traversal algorithm. However, the experience method cannot consider the real-time fluctuating energy demand of a detached house, and the traversal algorithm costs too much computation time. Therefore, this study applies Nondominated Sorting Genetic Algorithm-II (NSGA-II) to size a PV and battery integrated system by optimizing total electricity cost and usage of the grid electricity simultaneously. By setting these two indicators as objectives separately, single-objective genetic algorithms (GAs) are also deployed to find the optimal size specifications of the PV and battery integrated system. The optimal solutions from NSGA-II and single-objective GAs are mutually verified, showing the high accuracy of NSGA-II, and the rapid convergence process demonstrates the time-saving effect of all these deployed genetic algorithms. The robustness of the deployed NSGA-II to various grid electricity prices is also tested, and similar optimal solutions are obtained. Compared with the experience method, the final optimal solution from NSGA-II saves 68.3% of total electricity cost with slightly more grid electricity used. Compared with the traversal algorithm, NSGA-II saves 94% of the computation time and provides more accurate size specifications for the PV and battery integrated system. This study suggests that NSGA-II is suitable for sizing a PV and battery integrated system for a detached house.

Les matériaux à changement de phase MCP révèlent des performances importantes dans le domaine de la gestion thermique. Ces matériaux ont une capacité importante de stockage thermique. L’excès de la chaleur dissipée par les composantes électroniques peut causer des graves défaillances. Un système de refroidissement développé basé sur les matériaux à changement de phase est l’une des solutions les plus recommandées afin d’assurer un fonctionnement sécurisé de ces composants microélectroniques. Bien que la faible conductivité de ces matériaux soit considérée comme la limitation majeure de leurs utilisations dans les applications de gestion thermique. L’objectif principal de cette thèse est l’amélioration de la conductivité thermique de ces matériaux et l’optimisation des dissipateurs thermiques. Dans les premiers chapitres, des modélisations numériques sont effectuées afin de déterminer la configuration optimale d’un dissipateur à partir de l’étude de plusieurs paramètres comme l’insertion des ailettes, la dispersion des nanoparticules et l’utilisation de multiples matériaux à changement de phase. L’innovation de cette étude est la modélisation du transfert thermique des matériaux à changement de phase avec une concentration des nanoparticules relativement importante par rapport à la littérature et plus précisément les travaux scientifiques expérimentaux. Des conclusions intéressantes sont extraites de cette étude paramétrique qui va nous permettre parla suite de proposer un nouveau modèle basé sur des multiples des matériaux à changement de phase améliorés avec les nanoparticules. Des études d’optimisation fiabiliste sont après réalisées.En premier lieu, une étude d’optimisation fiabiliste mono-objective a été réalisé dans le but est de proposer un modèle du dissipateur fiable à multiple NANOMCPS avec des dimensions optimales. Donc l’objectif est d'optimiser (minimiser) le volume total du dissipateur tout en considérant les différents contraintes géométriques et fonctionnels. La méthode hybride robuste (RHM) montre une efficacité à proposer un modèle fiable et optimal comparant à la méthode d’optimisation déterministe (DDO) et les différentes méthodes d’optimisation de la conception basée sur la fiabilité (RBDO) considérées. En plus de la nouveauté de modèle proposée basé sur des multiples NANOMCPs, l’intégration d’une méthode de RBDO développée (RHM) pour l’application de gestion thermique est considérée comme une innovation dans la littérature récente.En deuxième lieu, une étude d’optimisation fiabiliste multi objective est proposée. Deux objectives sont considérées : le volume total du dissipateur et le temps de décharge pour atteindre la température ambiante. De plus, l’utilisation d’une méthode d’optimisation RHM, et l’algorithme génétique de tri non dominée, sont adoptées afin de chercher le modèle optimal et fiable qui offre le meilleur compromis entre les deux objectifs. En outre, un modèle de substitution avancée est établi dans le but de réduire le temps de simulation vu que le nombre important des itérations jusqu'à aboutir à un modèle optimal
Phase-change materials exhibit considerable potential in the field of thermal management.These materials offer a significant thermal storage capacity. Excessive heat dissipated by miniature electronic components could lead to serious failures. A cooling system based on phase-change materials is among the most recommended solutions to guarantee the reliable performance of these microelectronic components. However, the low conductivity of these materials is considered a major limitation to their use in thermal management applications. The primary objective of this thesis is to address the challenge of improving the thermal conductivity of these materials. Numerical modeling is conducted, in the first chapters, to determine the optimal configuration of a heat sink, based on the study of several parameters such as fin insertion, nanoparticle dispersion, and the use of multiple phase-change materials. The innovation in this parametric study lies in the modeling of heat transfer from phase-change materials with relatively high nanoparticle concentration compared to the low concentration found in recent literature (experimental researchs). Significant conclusions are deducted from this parametric study, enabling us to propose a new model based on multiple phase-change materials improved with nanoparticles (NANOMCP). Reliable optimization studies are then conducted. Initially, a mono-objective reliability optimization study is carried out to propose a reliable and optimal model based on multiple NANOMCPs. The Robust Hybrid Method (RHM)proposes a reliable and optimal model, compared with the Deterministic Design Optimization method (DDO) and various Reliability Design Optimization methods (RBDO). Furthermore,the integration of a developed RBDO method (RHM) for the thermal management applicationis considered an innovation in recent literature. Additionally, a reliable multi-objective optimization study is proposed, considering two objectives: the total volume of the heat sink and the discharge time to reach ambient temperature. The RHM optimization method and the non-dominated sorting genetics algorithm (C-NSGA-II) were adopted to search for the optimal and reliable model that offers the best trade-off between the two objectives. Besides, An advanced metamodel is developed to reduce simulation time, considering the large number of iterations involved in finding the optimal model

Antenna and microwave design problems are, in general, multi-objective. Multi-objective Evolutionary Algorithms (MOEAs) are suitable optimization techniques for solving such problems. Particle Swarm Optimization (PSO) and Differential Evolution (DE) have received increased interest from the electromagnetics community. The fact that both algorithms can efficiently handle arbitrary optimization problems has made them popular for solving antenna and microwave design problems. This chapter presents three different state-of-the-art MOEAs based on PSO and DE, namely: the Multi-objective Particle Swarm Optimization (MOPSO), the Multi-objective Particle Swarm Optimization with fitness sharing (MOPSO-fs), and the Generalized Differential Evolution (GDE3). Their applications to different design cases from antenna and microwave problems are reported. These include microwave absorber, microwave filters and Yagi-uda antenna design. The algorithms are compared and evaluated against other evolutionary multi-objective algorithms like Nondominated Sorting Genetic Algorithm-II (NSGA-II). The results show the advantages of using each algorithm.

Dual-fuel reactivity controlled compression ignition (RCCI) combustion has shown high thermal efficiency and superior controllability with low NOx and soot emissions. However, as in other low temperature (LTC) combustion strategies, the combustion control using low exhaust gas recirculation (EGR) or high compression ratio at high load conditions has been a challenge. The objective of this work was to examine the efficacy of using dual direct injectors for combustion phasing control of high load RCCI combustion. The present computational work demonstrates that 21bar gross indicated mean effective pressure (IMEP) RCCI is achievable using dual direct injection. The simulations were done using the KIVA3V-Release 2 code with a discrete multi-component fuel evaporation model, coupled with sparse analytical Jacobian solver for describing the chemistry of the two fuels (iso-octane and n-heptane). In order to identify an optimum injection strategy a Nondominated Sorting Genetic Algorithm II (NSGA II), which is a multi-objective genetic algorithm, was used. The goal of the optimization was to find injection timings and mass splits among the multiple injections that simultaneously minimize the six objectives: soot, nitrogen oxide (NOx), carbon monoxide (CO), unburned hydrocarbon (UHC), indicated specific fuel consumption (ISFC), and ringing intensity. The simulations were performed for a 2.44 liter, heavy-duty engine with 15:1 compression ratio. The speed was 1800 rev/min and the intake valve closure (IVC) conditions were maintained at 3.42bar, 90°C, and 46% EGR. The resulting optimum condition has 12.6bar/deg peak pressure rise rate, 158bar maximum pressure, and 48.7% gross indicated thermal efficiency. NOx, CO, and soot emissions are very low.

This paper investigates the mission based propeller design optimization methodology for coaxial rotors for vertical take-off and landing (VTOL) vehicles. The modified blade element momentum theory is used for blade performance prediction, which is first validated using the experimental data available in literature. Each segment has to be bound by some lower and upper bounds and it has to satisfy the design constraints specified according to the requirements for the mission. The mission considered in this paper involves a standard package delivery mission involving, vertical take-off, cruise in forward flight, drop-off in hover mode and return to origin in cruise mode. The optimization is carried out by a genetic algorithm, which is a variant of Nondominated Sorting Genetic Algorithm (NSGA -II). The performance of the optimal blade designed for a specific mission is evaluated for a range of different missions to evaluate whether the design generalizes well over a range of missions. The optimized blade profile is curve fitted and smoothened out using splines and polynomial curves. Then the performance of optimal blade is evaluated against available off-the-shelf propellers. The results indicate that the power required by using off the shelf propellers for a mission involving 100 s of hover and 500 s of cruise to be as high as 41% greater than the power required by the optimized coaxial rotor system. The optimal coaxial propeller system is observed to have different geometries for both upper and lower rotors.