LLVM back-end instruction scheduling optimization based on improved genetic algorithm

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.

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 presents a new multi-objective optimization method, which is inspired from the idea of non-dominated sorting genetic algorithm (NSGA) and genetic quantum algorithm (GQA). The GQA has been tested on well known test beds in single objective optimization and compared with the genetic algorithm (GA) in the lead author’s previous work [22]. This paper aims to apply the idea of GQA to multi-objective optimization (MOO). The developed method is called non-dominated sorting genetic quantum algorithm (NSGQA). The developed method is tested with benchmark problems collected from literature, which have characteristics representing various aspects of a MOO problem. Test results show that NSGQA has better performance on most benchmark problems than currently popular MOO methods such as the NSGA. The integration of GQA with MOO, and the systematic comparison with other MOO methods on benchmark problems, should be of general interest to researchers on MOO and to practitioners using MOO methods in design.

In this paper, we propose a hybrid algorithm combining two different metaheuristic methods, “Genetic Algorithms (GA)” and “Sperm Swarm Optimization (SSO)”, for the global optimization of multimodal benchmarks functions. The proposed Hybrid Genetic Algorithm and Sperm Swarm Optimization (HGASSO) operates based on incorporates concepts from GA and SSO in which generates individuals in a new iteration not only by crossover and mutation operations as proposed in GA, but also by techniques of local search of SSO. The main idea behind this hybridization is to reduce the probability of trapping in local optimum of multi modal problem. Our algorithm is compared against GA, and SSO metaheuristic optimization algorithms. The experimental results using a suite of multimodal benchmarks functions taken from the literature have evinced the superiority of the proposed HGASSO approach over the other approaches in terms of quality of results and convergence rates in which obtained good results in solving the multimodal benchmarks functions that include cosine, sine, and exponent in their formulation.

This paper presents a comparative performance assessment for loss minimization of vector controlled induction motor (IM) drive based on two different efficient optimization algorithms, namely Genetic Algorithm (GA) and Golden Search (GS). Here the features of GA and GS both for estimation and recalculation of optimized flux component of current ( ), have been utilized for a better optimal efficiency operation of the IM drive. In this context, a comparative performance of IM drive based on GA and GS optimization algorithms is carried out. Both the GA and GS based algorithms greatly improve the efficiency by reducing the core loss of the drive system. Moreover, both the approaches have no effect on parameter variation and also need no additional hardware for hardware implementation. However, GA based loss minimization scheme proves its edge over GS based scheme for the IM drive. The simulation results for various speed patterns and operating conditions are presented here. Stability study of the whole drive system is also carried out utilizing both the optimizing schemes. The performance of the proposed drive is validated experimentally on dSPACE-1104 based laboratory prototype.

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.

Hybrid design of PID controller for four DoF lower limb exoskeleton

The main concern of all economic companies is the resources equipping and allocating them in different economic sectors with the aim of maximizing profit and minimizing risk. Decentralization is one of the important factors that reduce investment risk. The investors plan to create investment by carefully planning and collecting sufficient information on the economic situation and analyzing the situation of various industries. As an economic enterprise, banks are looking for short- and long-term investments in a types of loans ,such as bailment of a capital , civil participation, reward, etc, which guarantees the return of their capital. In this paper, considering the condition of a bank as an economic enterprise, a model is presented which not only increases profit but also reduces risk. Two objective functions have been defined that the first objective is to minimize the risk and the second objective function is to maximize the of the bank profit, which is used by robust programming and Malvi Sim model. In this paper, we have investigated the Risky and non-Risky Partfolio and the optimal portfolio of bank assets from scenario based solution of the model and by using PSO and Genetic Optimization Algorithm. At all levels of confidence and optimal values of risk based on the estimation of SPP-CVAR method by Particle Swarm Algorithm (PSA) is less than genetic algorithm, which indicates better performance of Particle Swarm Algorithm (PSA) than Genetic Algorithm (GA). Also, the optimum wealth obtained from PSA solution is higher at all levels of confidence than the corresponding value of Genetic Algorithm (GA), and this is another reason to confirm the performance of PSO algorithm compared to the Genetic Algorithm (GA). The values of the first goal function, obtained from the PSO algorithm, for all confidence levels are lower than those of the genetic algorithm. The optimum wealth obtained from PSA is higher than genetic algorithm. At 0.9 level, the value of LR of kupiec statistics for the SPP-CVAR method was less than the Chi-square statistics (Critical value) which was assumed to be acceptable.

In search based test data generation, the problem of test data generation is reduced to that of function minimization or maximization.Traditionally, for branch testing, the problem of test data generation has been formulated as a minimization problem.In this paper we define an alternate maximization formulation and experimentally compare it with the minimization formulation.We use genetic algorithm and binary particle swarm optimization as the search technique and in addition to the usual operators we also employ a branch ordering strategy, memory and elitism.Results indicate that there is no significant difference in the performance or the coverage obtained through the two approaches and either could be used in test data generation when coupled with the branch ordering strategy, memory and elitism.

Chapter 5 - Scalar Optimization and Usability

This paper concerns the use of Genetic Algorithms designed to optimize agricultural land use based on economic criteria. The agricultural areas considered are heather moorland areas in the UK where sheep farming competes with grouse farming and the land is managed differently for each activity. Additionally, there are tenant farmers who rent land for fixed periods and are more interested in short term economic gain and landlords who are more concerned with land value and capability and economic returns in the longer term. This paper explores the application of Genetic Algorithms (GAs) to what we call an inter-temporal optimization. Inter-temporal optimization aims to maximize outcomes for a period of time, not for a time point. GAs are shown to be able to cope with two important features of inter-temporal optimization: (1) dynamics; (2) optimizing areas of landscape. These two features make it difficult for traditional approaches such as econometrics and mathematical dynamic programming to tackle such an optimization problem. This paper exemplifies GA’s capabilities by tackling an inter-temporal optimization problem in land-use decision making. We use GA to represent land-use decisions, to simulate economic and biologic dynamics, and to optimize decision-makers’ objectives in inter-temporal optimization. Experimental results indicate that a long-term inter-temporal optimization smoothes the impacts of dynamics and reduces the number of decision changes. We also compare the experimental results versus the predictions made by agricultural experts. We have found that a GA system forecasts land-use changes in line with experts’ predictions. This work demonstrates how GA successfully deals with dynamics for inter-temporal optimization.

Most scheduling problems require either exponential time or space to generate an optimal answer [7]. Instruction scheduling is an instance of a general scheduling problem and Dewitt [8] uses this fact to show instruction scheduling is a NP-complete problem. This paper applies Genetic Algorithms, Tabu Search, and list scheduling to the instruction scheduling problem and compares the results obtained by each.

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.