Liquid propellant engine conceptual design by using a fuzzy-multi-objective genetic algorithm (MOGA) optimization method

In response to the problems of traditional genetic algorithms in evaluating English compositions, the stability of automatic grading of English compositions has been further enhanced. This article evaluates the teaching effectiveness of automatic grading of English compositions using an optimization fusion algorithm combined with genetic optimization algorithm and CNN model. By analyzing genetic content and optimization algorithms, a corresponding fusion optimization model was obtained, and the automatic evaluation of English compositions was analyzed and predicted through experimental verification. The results indicate that the curves corresponding to different parameters exhibit typical segmentation features through the variation curves of individual numbers under different scale factors. And through quantitative description and analysis of the curve, it can be seen that the change in proportion factor has an absolute advantage in the impact of genetic algorithm on the number of children. As the number of samples increases, the performance of genetic optimization algorithms under the f function shows an upward trend. Research has shown that the writing content index has the greatest impact on English writing, while the corresponding grammar errors have the smallest impact on English writing. Finally, the accuracy of the optimized model was verified by comparing the model curve with experimental data. This study provides theoretical support for the use of genetic optimization algorithms and CNN models in English, and provides ideas for the use of optimization algorithms in other fields.

Y-stiffened panel multi-objective optimization using genetic algorithm

Multicast routing is an effective way to communicate among multiple hosts in a network. For multimedia applications, the routing algorithms should consider many Quality of Service (QoS) parameters such as delay, cost and so on to find a new route. However, to find routes with two or more QoS parameters is an NP-hard problem. This paper describes a new evolutionary scheme for optimization of multicast QoS routing based on the hybrid of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), called HGAPSO. In HGAPSO, individuals in a new generation are created, not only by crossover and mutation operation as in GA, but also by PSO. The upper-half of the best-performing individuals in a population are regarded as elites. Instead of being reproduced directly to the next generation, these elites are first enhanced. The group constituted by the elites is regarded as a swarm, and each elite corresponds to a particle within it. In this regard, the elites are enhanced by PSO, an operation which mimics the maturing phenomenon in nature. This scheme can simultaneously optimize the cost of the tree, the maximum end-to-end delay, the average delay and the maximum link utilization. In this way, a set of optimal solutions, known as Pareto set, is calculated in only one run, without a priori restrictions. It has revealed an efficient method of the reconstruction of multicast tree topology and the experimental results demonstrated better performance than the conventional GA optimization method.

This paper firstly proposes an improved genetic algorithm (GA) for optimization in adaptive bus signal priority control at signalized intersections. Unlike conventional genetic algorithms with slow convergence speed, this algorithm can increase the convergence speed by utilizing the compensation rule between consecutive signal cycles to narrow new possible generated population spaces. Secondly, the paper would like to present a way to apply the algorithm to a simple adaptive bus signal priority control as well as compare how much the computation time is saved when applying the improved algorithm. Then the research thirdly investigates the efficiency of the proposed algorithm under various flow rate situations. The results show that the improved genetic algorithm can reduce the computation time considerably, by up to 48.39% for the studied case. With high saturation degrees on the cross street, the convergence rate performance of the improved genetic algorithm is significantly good. The figure can be up to 36.2% when compared with the convergence rate of the conventional GA.

This paper discusses the approach of cooling design optimization of a HPT endwall with 3D Conjugate Heat Transfer (CHT) CFD applied. This study involved the optimization of the spacing of impingement jet array and the exit width of shaped holes, which were different for each cooling cavity. The optimization objectives were to reduce the wall temperature level and also to increase the aerodynamic performance of the gas turbine. The optimization methodology consisted of an in-house parametric design & CFD mesh generation tool, a CHT CFD solver, a database of wall temperature distributions, a metamodel, and a genetic algorithm (GA) for evolutionary multi-objective optimization. The CFD tool was validated against experimental data of an endwall at CHT conditions. The metamodel, which could efficiently predict the aerodynamic loss and the wall temperature distribution of a new individual based on the database, was developed and coupled with Non-dominated Sorting Genetic Algorithm II (NSGA-II) to accelerate the optimization process. Through optimization search, the Pareto front of the problem was found costing only tens of CFD runs. By comparing with additional CFD results, it was demonstrated that the design variables in the Pareto front successfully reached the optimal values. The optimal spacing of each impingement array was decided accommodating the local thermal load while avoiding jet lift-off of film coolant. It was also suggested that using cylindrical film holes near throat could benefit both aerodynamics and cooling.

This paper presents the population-based sampling genetic algorithm, which allows for discrete design optimization under uncertainty. The population-based sampling approach uses the genetic algorithm population to provide samples for the probabilistic evaluation of aggregate uncertain constraint or objective functions. In population-based sampling, large numbers of samples are accumulated to evaluate the fitness values of good designs during the genetic algorithm run, whereas the computational cost spent on designs with poor fitness is minimal. Using Monte Carlo sampling with a genetic algorithm for optimization under uncertainty is a currently accepted approach; however, this approach incurs a large computational cost. In this paper, the genetic algorithm with population-based sampling generates solutions to a discrete optimization problem under uncertainty associated with a commercial satellite design that was solved in previous work via a genetic algorithm with Monte Carlo sampling. The genetic algorithm with population-based sampling and genetic algorithm with Monte Carlo sampling approaches are compared in terms of efficiency (computational cost) and effectiveness (solution quality). The comparison also examines the scalability of the algorithms' performance when solving three additional problem sizes. Furthermore, two population-based sampling variants are introduced, namely, the variable population-based sampling approach, which combines the concepts of population-based sampling and Monte Carlo sampling, and the generalized population-based sampling approach, which removes the restriction in population-based sampling that the uncertain parameters associated with the design variables all have Gaussian probability distributions.

For grid-scale intermittent electricity storage, liquid air energy storage (LAES) is considered to be one of the most promising technologies for storing renewable energy. In this study, a steady-state process model was developed for an LAES, by combining a Linde liquefaction process and an open Rankine power cycle. To investigate the system performance and achieve global optimization, a single-factor analysis approach and multifactor genetic algorithm (GA) optimization model were built using MATLAB software. The effects of the charging pressure, storage pressure, discharging pressure, and isentropic efficiency of the compressor/turbine on the LAES performance parameters, such as the inlet temperature of the Joule–Thomson valve, round-trip efficiency (RTE), liquefaction ratio (LR), and power consumptions in the compressor, cryo-pump, and turbine, were investigated. Subsequently, the optimal conditions were obtained using the GA optimization method to achieve the optimal performance of the LAES. The results showed that the charging pressure, discharging pressure, and isentropic efficiencies of the compressor and turbine had significant effects on the RTE; an increase in the discharging pressure resulted in an improved expansion power output; the GA optimization could achieve RTE, LR, the system energy storage and recovery exergy efficiencies of 53.33%, 86.96%, 81.00%, and 78.16%; the power consumption in the compressor of GA optimization was 10.02% maximum saving. The proposed optimization method can be used to further explore the global optimization of cryogenic energy storage systems, such as different-layout LAES systems and different cryogenic liquefaction media energy storage systems.

Hybrid design of PID controller for four DoF lower limb exoskeleton

Urban flooding damages properties, causes economic losses and can seriously threaten public health. An innovative, fuzzy logic (FL)-based, local autonomous real-time control (RTC) approach for mitigating this hazard utilising the existing spare capacity in urban drainage networks has been developed. The default parameters for the control algorithm, which uses water level-based data, were derived based on domain expert knowledge and optimised by linking the control algorithm programmatically to a hydrodynamic sewer network model. This paper describes a novel genetic algorithm (GA) optimisation of the FL membership functions (MFs) for the developed control algorithm. In order to provide the GA with strong training and test scenarios, the compiled rainfall time series based on recorded rainfall and incorporating multiple events were used in the optimisation. Both decimal and integer GA optimisations were carried out. The integer optimisation was shown to perform better on unseen events than the decimal version with considerably reduced computational run time. The optimised FL MFs result in an average 25% decrease in the flood volume compared to those selected by experts for unseen rainfall events. This distributed, autonomous control using GA optimisation offers significant benefits over traditional RTC approaches for flood risk management.

Offset, gain and time mismatch errors exist in the channels of the time-interleaved analog-to-digital converter (TIADC), which severely affect the signal-to-noise and distortion ratio (SNDR), spurious-free dynamic range (SFDR) and effective number of bits (ENOB). Here, an error detection and correction method of TIADC based on genetic algorithm (GA) optimization is presented. The algorithm based on sine fitting is used for the first estimation and rough correction of the input signal, and GA is used for the second detection and fine correction of channel mismatch errors on the signal after frequency domain filtering. Experimental results show that the SNDR of the TIADC system is enhanced to 92.24 dB, the SFDR is enhanced to 102.05 dB, the ENOB is enhanced to 15.03 bits and the spectrum spurs are reduced. This method provides a new idea for error detection and correction of high-resolution TIADC systems with more than 16 bits.

This paper uses the Genetic Algorithm(GA) optimization method to find the best S-band quadrifilar antenna for the satellite's telemetry function. Since the backradiation as the cross-polarization of the RHCP antenna on one side of the satellite will possibly degrade the desired performance of the LHCP antenna on the other side, in order to minimize the backradiation and maximize the flatness of the forward radiation from the RHCP antenna, we will get the optimal values of the physical design elements such as the number of turns, the radius of the helix and the height. Finally, we will present the excellent performance of the RHCP antenna realized with the optimized physical dimensions.

This paper investigates UAV conceptual design using optimization methods. The conceptual design of a Medium Altitude Long Endurance (MALE) UAV is formulated as two different optimization problems. The optimization problems use a single objective and dual objective functions. For the single objective case, endurance maximization of the UAV is taken as the objective function with aspect ratio and wing loading as design variables. Wing weight, stall speed, maximum speed and rate of climb are taken as constraints. In the dual objective case, minimizing weight is removed from the constraint set and added as the second objective and four more design variables namely taper ratio, thickness to chord ratio, loiter velocity and loiter altitude are added. In the single objective case, both conventional optimization algorithm and genetic algorithm are used to solve the problem and identical results are obtained. A newly developed multi-objective evolutionary algorithm named NSGA-II (non-dominated sorting genetic algorithm) is used to capture the full Pareto front for the dual objective problem. For the dual objective case, numerical results show that several useful Pareto optimal designs exist for the conceptual design of UAV. These Pareto points are thoroughly analyzed for use in detailed design.

Vortex beams (VBs) generated by plasmonic metasurfaces hold great potential in the field of information transmission due to their unique helical phase wavefronts and infinite eigenstates. However, achieving perfect multiplexing and superposition of VBs with different orders remains a challenging issue in nanophotonics research. In this paper, based on a single-layer metallic porous metasurface structure applicable to the infrared spectrum, VBs with orders 2, 4, 6, and 8 are realized through the arrangement of annular elliptical apertures. Moreover, perfect VBs are achieved by optimizing key structural parameters using a genetic algorithm. The optimization of key structural parameters via genetic-based optimization algorithms to attain the desired effects can significantly reduce the workload of manual parameter adjustment. In addition, leveraging the orthogonality between VBs of different orders, concentric circular multi-channel VBs array (l = 2, 6) and (l = 4, 8) are realized. High-purity multiplexing architectures (>90%) are achieved via rational optimization of critical structural parameters using a genetic optimization algorithm, which further mitigates information crosstalk in optical communication transmission. The introduction of the genetic algorithm not only reduces the workload of manual arrangement of unit arrays but also enables the generation of more perfect VBs, providing a new research direction for optical communication transmission and optical communication encryption.

An efficient method combining classical (gradient-based) methods for global scalar optimization and genetic algorithms for multiobjective optimization (MOO) is proposed for approximating the Pareto frontier and the Edgeworth–Pareto hull (EPH) of the feasible objective set in complicated nonlinear MOO problems involving piecewise constant functions of criteria with numerous local extrema. An optima injection method is proposed in which the global optima of individual criteria are added to the population of a genetic algorithm. It is experimentally shown that the method is significantly superior to the original genetic algorithm in the order of convergence and the approximation accuracy. Experiments concerning EPH approximation are also performed for the problem of constructing control rules for a cascade of reservoirs with criteria reflecting the reliability with which the requirements imposed on the cascade are met.

Simultaneous optimization of solvent composition and operation parameters for sulfolane aromatic extractive distillation processes