Leak Detection and Calibration of Water Distribution Systems Using Transients and Genetic Algorithms

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.

During the last decade, evolutionary methods such as genetic algorithms have been used extensively for the optimal design and operation of water distribution systems. More recently, ant colony optimization algorithms (ACOAs), which are evolutionary methods based on the foraging behavior of ants, have been successfully applied to a number of benchmark combinatorial optimization problems. In this paper, a formulation is developed which enables ACOAs to be used for the optimal design of water distribution systems. This formulation is applied to two benchmark water distribution system optimization problems and the results are compared with those obtained using genetic algorithms. The findings of this study indicate that ACOAs are an attractive alternative to GAs for the optimal design of water distribution systems, as they outperformed GAs for the two case studies considered both in terms of computational efficiency and their ability to find near global optimal solutions.

In recent years, genetic algorithm (GA), particle swarm optimization (PSO) and hybrid genetic algorithm particle swarm optimization (HGAPSO) have attracted considerable attention among various modern heuristic optimization techniques. In this study the HGAPSO, PSO and GA optimization techniques are used for to search the optimal placement and sizing of static VAR compensator (SVC) in power system. The objective function is defined for reducing power loss, voltage deviation and investment costs of SVC. The effectiveness of the proposed hybrid based approach is applied and demonstrated on IEEE 30 Bus network. The results show that the proposed hybrid HGAPSO compared with PSO and GA optimization for performs and giving better solution.

The three-dimensional spatial structure of a methylene-acetal-linked thymine dimer present in a 10 basepair (bp) sense-antisense DNA duplex was studied with a genetic algorithm designed to interpret NOE distance restraints. Trial solutions were represented by torsion angles. This means that bond angles for the dimer trial structures are kept fixed during the genetic algorithm optimization. Bond angle values were extracted from a 10 bp sense-antisense duplex model that was subjected to energy minimization by means of a modified AMBER force field. A set of 63 proton-proton distance restraints defining the methylene-acetal-linked thymine dimer was available. The genetic algorithm minimizes the difference between distances in the trial structures and distance restraints. A large conformational search space could be covered in the genetic algorithm optimization by allowing a wide range of torsion angles. The genetic algorithm optimization in all cases led to one family of structures. This family of the methylene-acetal-linked thymine dimer in the duplex differs from the family that was suggested from distance geometry calculations. It is demonstrated that the bond angle geometry around the methylene-acetal linkage plays an important role in the optimization.

A hybrid algorithm (HA) integrated genetic algorithm (GA) with ant colony optimization (ACO) for solving Traveling Salesman Problems(TSP) was studied to get better optimization performance than each single algorithm, and complement merits each other and avoid each own demerits. The hybrid algorithm runs GA first and then ACO. A new strategy called GSA was proposed aiming at the key link in the HA that converts genetic solution from GA into information pheromone to distribute in ACO. GSA takes new matrix which is formed by the combination of the former 90% of individual from genetic solution and 10% of individual by random generation as the basis of transformation of pheromone value. The best combination of genetic operators in GA was also discussed. Several TSP were used as simulation tests to test genetic operators matching and optimization performance of HA. The results show that PMX crossover matched with IVM mutation in the GA is the best combination of genetic operators which is able to make GA improve the precision of optimal solution, and HA using the best combination operators and GSA strategy is successful and available to search for optimal solution in high efficiency and has good convergence.

The control of structural vibration in aeroplanes and ships is of great importance in achieving low noise targets. Currently, such control is effected using viscoelastic coating materials although much current research is concerned with active, anti-noise based control measures. Recent studies using Genetic Algorithm (GA) optimization methods in the field of Statistical Energy Analysis (SEA) suggest that it may be possible to design passive noise filtration characteristics into such structures. This paper reports initial work in this field: it compares GA’s with more classical optimization methods and shows how improvements in noise performance can be obtained for the simple structures considered.

Improved random selection strategy, self-adaptive crossover and mutation operators, population co-evolution algorithm are adopted in this paper to set up a new genetic algorithm(GA), guaranteeing the universality of population, the thoroughness of solution and the execution efficiency of algorithm in solving multi-process, process crossover, work-piece flow and other condition based scheduling problem of flexible manufacturing system(FMS). The problem of GA in solving FMS scheduling is summarized in this paper, reasons and countermeasures for GA's pre-maturing are studied, longitudinal and transverse comprehensive state-based correlative self-adaptive crossover and mutation operators are raised according to the analysis on GA to avoid local optimization and pre-maturity, the co-evolutionary GA is designed to solve the scheduling model for FMS, and the algorithm is validated for the scheduling of box-type parts for precise machine tools.

As the population increases, the number of vehicle on the road also increases exponentially. This results in vehicular traffic with a delay in finding parking space, which then results in traffic congestion and environmental pollution. This paper provides the use of various sensors in smart parking system and the optimization done by using genetic algorithms. The magnetic sensors, ultrasonic sensors, radar sensors, infrared sensors are used to sense the status of the parking slot. The genetic algorithm and ant colony optimization techniques are used to find the nearest appropriate and unoccupied parking slot. Further in this paper we have proposed a smart parking system solution with a speedy genetic algorithm for an effective path optimization

This paper presents efficiency optimization of switched reluctance motor based on genetic algorithm optimization technique. Switched reluctance motor (SRM) is considered for various applications due to its simple and robust construction. It is very essential to improve efficiency of switched reluctance motor. In this paper, optimization of 8/6 switched reluctance motor is achieved by using genetic algorithm with efficiency as its objective function. The objective of the paper is to identify the best switched reluctance motor design that provides better efficiency to satisfy the unique requirements of various applications. Using finite element analysis, a design validation of motor and characterization was made. It is analyzed that analytical results and simulation results are very close which establishes correctness of designs. The optimization result shows that the newly developed SRM design achieved better efficiency. The efficiency is increased from 82.75 % to 86.19 % with minor increase in weight. Improvement in efficiency can lead to lower energy usage, longer motor life span, and better performance.

BIM-based approach for optimizing life cycle costs of sustainable buildings

Rotation speed and propulsive force are the two critical parameters in the work of the air-borne bolting rig. To address the problem that unreasonable rotation speed and propulsive force will induce the breakage of the drill pipe and the inability of the drill bit to cut coal adequately this paper proposes an adaptive control strategy for the air-borne bolting rig based on genetic algorithm optimization. Firstly, we obtain the corresponding coal hardness by the real-time acquisition of the working torque of the drill pipe. Then we calculate the reasonable rotation speed of the hydraulic motor and the propulsive force of the hydraulic cylinder on the coal of different hardness. Secondly, the genetic algorithm is applied to optimize the parameters of the PID (proportion integration differentiation) controller so that the system may attain the target value fast and reliably and achieve adaptive control. Finally, a simulation model of the slewing system and the propulsion system of the air-borne bolting rig are established in the AMESim hydraulic software, and the simulation tests were carried out under two distinct working conditions: single coal hardness and coal hardness of sudden change. The results indicate that the PID control strategy based on genetic algorithm optimization has a shorter response time, a smaller overshoot, and a lower steady-state error than the traditional PID control strategy.

Development of a Mutation Operator in a Real-Coded Genetic Algorithm for Bridge Model Optimization

With the rapidly growing number of wireless users and systems more frequency bands are needed, which led to the spectrum scarcity problems. Cognitive radio (CR) is an emerging technology that enables the dynamic spectrum access by optimizing the usage of the available spectrum through giving the unlicensed Secondary Users (SU) the opportunity to exploit the spectrum without causing any interference with the Primary Users’ (PU) usage. Security threats is one of the substantial problems issues that must be addressed in CR networks. Primary User Emulation (PUE) attacks are one of the main threats in deploying CRN, where the Malicious Users (MU) mimic the PU signal to confuse the other SUs and corrupt the spectrum sensing process. In this paper, a hybrid Genetic Whale Optimization Algorithm (GWOA) is proposed to optimize the spectrum utilization and to combat the PUE attacks. GWOA integrates the crossover and mutation operations with WOA algorithm to reach the balance between exploitation and exploration phases, to find the optimal solution based on a fitness function. The simulations results show promising performance of GWOA, when compared with recent detection algorithms.

A stochastic conjugate gradient multi-modulus algorithm based on global artificial fish swarm and genetic optimization DNA encoding sequences(GAFS-GDNA-SMMA) is proposed to solve the high computational loads, low convergence rate, and large mean square error(MSE) of the multi-modulus blind equalization algorithm(MMA). In this proposed algorithm, the optimal DNA coding sequence can be found and used as the initial optimal weight vector of the stochastic conjugate gradient MMA(SMMA) after decoding the optimization DNA sequences. The simulation results show that the proposed algorithm has the faster convergence speed and smaller mean square error comparison with MMA, SMMA, GA-DNA-SMMA(Genetic algorithm and DNA optimization based SMMA)

In this work, we performed a quantum reactive study using a new potential energy surface (PES) fitted to the Na + HF→NaF + H reaction. The new PES was obtained using the genetic algorithm optimization technique (GAOT). We calculated the reactive probabilities (RP) of the Na + HF system using the new PES. The GAOT RP are compared with the obtained RP using two other PES found to the Na + HF system: Bond Order 5 (BO5) and the generalized simulated annealing (GSA) PES. We found that the GAOT RP are in excellent agreement with the GSA and BO5 RP. It should be point out that the BO5 RP are considered the benchmark of literature. Therefore, the GAOT is shown to be an appropriate method to fit PES of reactive systems. © 2008 Wiley Periodicals, Inc. Int J Quantum Chem, 2008