An Adaptive Genetic Algorithm with Optimal Recombination for Scheduling Problems with Energy Resource

The primary objective of this study is to solve the Vehicle Routing Problem with Stochastic Demands (VRPSD) under restocking policy by using adaptive Genetic Algorithm (GA). The problem of VRPSD is one of the most important and studied combinatorial optimization problems, which finds its application on wide ranges of logistics and transportation area. It is a variant of a Vehicle Routing Problem (VRP). The algorithms for stochastic VRP are considerably more intricate than deterministic VRP and very time consuming. This has led us to explore the used of metaheuristics focusing on the permutation-based GA. The GA is enhanced by automatically adapting the mutation probability to capture dynamic changing in population. The GA becomes a more effective optimizer where the adaptive schemes are depend on population diversity measure. The proposed algorithm is compared with standard GA on a set of randomly generated problems following some discrete probability distributions inspired by real case of VRPSD in solid waste collection in Malaysia. The performances of several types of adaptive mutation probability were also investigated. Experimental results show performance enhancements when adaptive GA is used.

Background/Objective: The objective is to minimize ambiguous test cases by using Adaptive Genetic Algorithm (AGA). Problem description/Proposed Method: Here we are concerned with the problem of randomly generated test cases. It can contain some ambiguous test cases, which lead to problems at the organizational level. A random algorithm will generate random test cases each time it is run, and it will have resemblance each time. Another problem related to random algorithms is that running them can take a lot of time. To minimize these issues we propose a new technique, which will reduce the given drawbacks. We proposed an Adaptive Genetic Algorithm (AGA), which will provide legal input in each case where it applied. Thus the problem of ambiguity will decrease. Results/Findings: In this research, the near optimal inputs will be generated based on the Adaptive Genetic Algorithm (AGA), which will reduce the illegal inputs. The fault detection rate is used as the fitness function in AGA. To remove the fault proneness, our AGA uses the coverage metrics of the test cases. Conclusion: Random algorithms will generate low cost test cases in large number but problem is that it will consists ambiguous test cases ,to reduce these here we are using AGA which will further reduce test cases by moderating the illegal inputs.

It is now common knowledge that blind search algorithms cannot perform with equal efficiency on all possible optimization problems defined on a domain. This knowledge applies also to Genetic Algorithms when viewed as global and blind optimizers. From this point of view it is necessary to design algorithms capable of adapting their search behavior by making use in a direct fashion of the knowledge pertaining to the search landscape. The paper introduces a novel adaptive Genetic Algorithm where the exploration/exploitation is directly controlled during evolution using a Bayesian decision process. Test cases are analyzed as to how parameters affect the search behavior of the algorithm.

The linear motor feeding system is a typical electromechanical coupling system. Conventional characteristic analyses of electromechanical coupling often overlook the influence of flexible deformation in critical components of the linear motor feeding system. Moreover, when employing genetic algorithms to optimize servo system PID control parameters, slow convergence, nonconvergence, or premature convergence problems may arise. To address these issues, this paper proposes a new performance optimization method for a linear motor feeding system. The method uses a combination of “multi-body theory + finite element” to accurately account for the flexible deformation of critical components of the feeding system, establishes a rigid–flexible electromechanical coupling model of the linear motor feeding system, and optimizes the PID parameters of the established model with an improved adaptive genetic algorithm. Simulation results demonstrate that, when utilizing an adaptive genetic algorithm to optimize the rigid–flexible electromechanical coupling model and a control system model that disregards flexible body deformation, the system achieves stability in 0.02 s and 0.027 s with overshoots of 13% and 27%, respectively. These outcomes confirm the accuracy and importance of considering flexible body deformation in the optimization performance of a linear motor feeding system. At the same time, the time required to reach the steady state of the rigid–flexible electromechanical coupling model optimized by the adaptive genetic algorithm is shortened from 0.035 s to 0.02 s. The sinusoidal signal response curve of the optimized system does not exhibit any peak overshoot compared with that of the nonoptimized system, and the response speed is also faster. These results demonstrate the effectiveness of the rigid–flexible electromechanical coupling model optimized by the nonlinear adaptive genetic algorithm. The displacement response curves of the linear motor feeding system under different workbench loads are obtained through experiments and compared with those obtained from simulations to verify the established model and the correctness of the proposed method.

Distribution is the challenging and interesting problem to be solved. Distribution problems have many facets to be resolved because it is too complex problems such as limited multi-level with one product, one-level and multi-product even desirable in terms of cost also has several different versions. In this study is proposed using an adaptive genetic algorithm that proved able to acquire efficient and promising result than the classical genetic algorithm. As the study and the extension of the previous study, this study applies adaptive genetic algorithm considering the problems of multi-level distribution and combination of various products. This study considers also the fixed cost and variable cost for each product for each level distributor. By using the adaptive genetic algorithm, the complexity of multi-level and multi-product distribution problems can be solved. Based on the cost, the adaptive genetic algorithm produces the lowest and surprising result compared to the existing algorithm

In this paper we propose an adaptive genetic algorithm (AGA) to optimize wind farm layout in order to achieve a highest aggregate wind power conversion efficiency with the presence of wake effect impacts. Instead of using random “crossover” in each iteration as in conventional GA, our proposed adaptive algorithm introduces novel relocation of “bad” turbines so that turbines experiencing worst wake effect impacts in a layout will be relocated to some new and more efficient positions. Therefore each iteration of the AGA can more effectively improve the aggregate wind power conversion efficiency and greatly accelerate the algorithm convergence. We experiment the proposed AGA and compare its performance with conventional GA based on a number of scenarios such as multi-directional wind distribution, offshore and inland wind distribution, with sparse and crowded farm settings. Numerical results verify the effectiveness of the proposed AGA algorithm which is able to locate an optimal layout at a much faster convergence speed and achieve a higher aggregate wind power output from a farm.

A hybrid diagnostic method utilizing Extended Kalman Filter (EKF) and Adaptive Genetic Algorithm (AGA) is presented for performance degradation estimation and sensor anomaly detection of turbofan engine. The EKF is used to estimate engine component performance degradation for gas path fault diagnosis. The AGA is introduced in the integrated architecture and applied for sensor bias detection. The contributions of this work are the comparisons of Kalman Filters (KF)-AGA algorithms and Neural Networks (NN)-AGA algorithms with a unified framework for gas path fault diagnosis. The NN needs to be trained off-line with a large number of prior fault mode data. When new fault mode occurs, estimation accuracy by the NN evidently decreases. However, the application of the Linearized Kalman Filter (LKF) and EKF will not be restricted in such case. The crossover factor and the mutation factor are adapted to the fitness function at each generation in the AGA, and it consumes less time to search for the optimal sensor bias value compared to the Genetic Algorithm (GA). In a word, we conclude that the hybrid EKF-AGA algorithm is the best choice for gas path fault diagnosis of turbofan engine among the algorithms discussed.

Various factors in the stock market are intricate and complex, and changes in the primary and secondary relationship are uncertain. The investigation found that the stock price is affected by factors such as buyers and sellers, stock policies, investors and other factors. Considering these factors, it is easy to draw a conclusion: stock price changes are random. After understanding the most obvious characteristics of stock prices, I found that there are very few high-precision stock price prediction models in the market. Under this background, it is necessary to study high-precision stock price prediction models. After looking up information from multiple parties, the method chosen by the author is a new method formed by the combination of BP neural network and genetic algorithm. The investigation found that the random initial weight of BP neural network and genetic algorithm will enter an infinite loop due to the local optimal solution. The author made some improvements, because adaptive genetic algorithm can solve the problem of initial weight of BP neural network, so BP The neural network is improved by an adaptive genetic algorithm, and a high-precision stock prediction model can be obtained. Heterogeneous processors are increasingly used in the field of high-performance computing, especially in the application of processor cores, which provides huge potential for parallel computing, but it also brings huge challenges. Higher accuracy requires a large knowledge system and a more advanced programming model. People need to master a more advanced programming model. Because for most programmers, this requirement is almost impossible to complete, so I thought of another way, using parallel compilation to complete the work. At present, the parallel compilation technology for heterogeneous architecture has achieved fruitful research results, but there are still many problems that need to be analyzed and solved. For these problems, the author proposes to create a new data area that transfers data to the nested external calculation, so the data transfer only occurs before and after the loop calculation, thereby avoiding the internal parallel loop in the iterative calculation process. Multiple copies of external serial loop data. Experiments show that these storage optimization methods can optimize the transmission, storage and access of different levels and different granularities of data, thereby improving the performance of the generated parallel programs to varying degrees.

The identification of stiffness parameters of armoured vehicles is the prerequisite and key to the accurate calculation of armoured vehicle dynamics. A linear multi-rigid-flexible body dynamics model of the armoured vehicle system is developed by using transfer matrix method of multibody system. The overall transfer equation, overall transfer matrix, and characteristics equation of the system are deduced based on the automatic deduction theorem of overall transfer equation of multibody system. Combined with an adaptive genetic algorithm, the objective function is built using the eigenfrequencies obtained from the modal test and simulation to optimize the minimum value. In comparison with the ordinary genetic algorithm, the adaptive genetic algorithm has a faster convergence speed. Numerical simulation and experimental verification show the effectiveness and accuracy of the method. The paper provides an effective method for studying the identification of stiffness parameters of armoured vehicles.

An adaptive genetic algorithm using mutation matrix is introduced for the solution of a series of zero/one knapsack problems of increasing complexity and structure. The evolution of the population in our adaptive genetic algorithm is based on a time dependent mutation matrix that is co-evolving, guided by the locus statistics and the fitness distribution of the population. This co-evolution of the mutation matrix provides a parameter free framework for the adaptive genetic algorithm, but different implementations of the mutation matrix have different performance for the knapsack problem. Here three structures of the knapsack problems are used as the benchmark tests: simple knapsack, parallel knapsack, and hierarchical or layer knapsack. For each type of knapsack structure, an index of difficulty is constructed based on the number of items and constraints. Numerical experiments are performed to test three models of implementation of the mutation matrix corresponding to three different way of controlling the number of mutation operations used. The numerical results for the three different knapsack problems using these three different implementations are discussed, along with a heuristic explanation for their different efficiencies. The conclusion is that an adaptive mutation matrix is best, where adaptation is based on the fitness distribution of the population, so that the mutation probability is implicitly time dependent. Furthermore, a directed mutation greatly improves the performance of all three models of implementation as this involves the imitation of the best chromosome by the less fit ones.

An adaptive genetic algorithm is presented as an intelligent algorithm for the assembly line balancing in this paper. The probability of crossover and mutation is dynamically adjusted according to the individual’s fitness value. The individuals with higher fitness values are assigned to lower probabilities of genetic operator, and vice versa. Compared with the traditional heuristic algorithms, the adaptive genetic algorithm has effective convergence and efficient computation speed. The computational results demonstrate that the proposed adaptive genetic algorithm is an effective algorithm to deal with the assembly line balancing to obtain a smoother line.

The effects of adaptive genetic algorithms (AGAs) and defected ground structures (DGSs) on performance optimization of tapered microstrip filter are investigated. The proposed structure achieves an ultra wide stopband with high attenuation within a small surface area, as well as 45% smaller size, in comparison with conventional filters. The parameters of the filter are optimized using in-home AGA code. In the proposed AGA algorithm, the crossover and mutation probabilities are adaptively changed according to the value of individual fitness. Then by utilizing the proposed DGS, a compact S-band lowpass filter with ultra-wide spurious free window is obtained. The proposed filter achieves an insertion loss of 0.8 dB from DC up to 4 GHz and 21 dB rejection in the stopband from 4.3 up to 60 GHz. The fabricated and measured results exhibit good agreement with the simulated results. They demonstrate that combining AGA and DGS yields best possible response for this group of filters.

Uncertainties in the production process would inevitably result in deviations from the existing schedule in flexible manufacturing systems (FMSs). This paper tries to study the problem in an environment with realistic interruptions and a requirement of time-restricted response in rescheduling. In our paper, the rescheduling system is based on the records of a dynamic database (DDB). It is able to reform the up-to-date status of a disturbed system via summarizing the remaining resources and works in process (WIPs) precisely. By using it as the new initial state, the new schedule is configured smoothly in conjunction with the existing schedule to improve the efficiency of FMS at this critical instant. Considering both speed and economic benefit, an adaptive genetic algorithm (AGA) is proposed for finding the new sub-optimal schedule of a large and complicated job shop FMS shortly after the interruption occurred. The AGA is designed to prevent the premature convergence and refine the performance of genetic algorithms in re-scheduling. During the AGA evolution process, the probabilities of crossover and mutation are varied, depending on both the fitness value and the normalized fitness distances between solutions. With help from DDB, the FMS scheduling model and AGA, the results obtained in the test examples of rescheduling are quite satisfactory.

The paper illustrates the procedure of reliability optimization modeling for contact springs of AC contactors under nonlinear multi-constraint conditions. The adaptive genetic algorithm (AGA) is utilized to perform reliability optimization on the contact spring parameters of a type of AC contactor. A method that changes crossover and mutation rates at different times in the AGA can effectively avoid premature convergence, and experimental tests are performed after optimization. The experimental result shows that the mass of each optimized spring is reduced by 16.2%, while the reliability increases to 99.9% from 94.5%. The experimental result verifies the correctness and feasibility of this reliability optimization designing method.

It is important for an enterprise to select one or more trusty suppliers to be the partners and build an efficient supply chain in the fierce competition. A selection and evaluation model based on the adaptive genetic algorithm and improved BP neural network is created in the paper. In the former process, the adaptive genetic algorithm is applied to adjust weights and thresholds of the BP neural network, and the additional momentum BP algorithm with learning rate adaptive adjustable method is used to search in neighborhoods of the approximate optimal solution in the later. The program written in visual basic 6.0, and the results show that the learning precision of the combination algorithm is more correctly than that of the generally BP algorithm. The training speed and convergence rate of the former is significantly improved in the experiment. It is helpful to realize automated evaluation for supplier selection and evaluation.