Research on Resource Scheduling of Cloud Computing Based on Improved Genetic Algorithm

In order to solve the problem that the resource scheduling time of cloud data center is too long, this paper analyzes the two-stage resource scheduling mechanism of cloud data center. Aiming at the minimum task completion time, a mathematical model of resource scheduling in cloud data center is established. The two-stage resource scheduling optimization simulation is realized by using the conventional genetic algorithm. On the technology of the conventional genetic algorithm, an adaptive transformation operator is designed to improve the crossover and mutation of the genetic algorithm. The experimental results show that the improved genetic algorithm can significantly reduce the total completion time of the task, and has good convergence and global optimization ability.

Control of diversity in genetic algorithms using multimodal strategies

An optimization process is a kind of process that systematically comes up with solutions that are better than a previous solution used before. Optimization algorithms are used to find solutions which are optimal or near-optimal with respect to some goals, to evaluate design tradeoffs, to assess control systems, to find patterns in data, and to find the optimum values (local or global) of mathematical functions. A genetic algorithm is one of the optimization techniques. In this way, a heuristic search that is inspired by Charles Darwin’s theory of natural evolution. This algorithm reflects the process of natural selection where the fittest individuals are selected for reproduction in order to produce offspring of the next generation which are population algorithms that emulate behavior similar to Darwinian natural selection. Taking into account these issues, this article shows the performance of a genetic algorithm designed, which allows to find several minimums within a function from the control of population diversity. To perform the tests, the algorithm with four different functions was used, with the particularity of having several minima with the same value. Proposed strategy was compared with a conventional genetic algorithm, the result was the conventional one can only find some of the minimums of the function and sometimes only one, while the proposal finds most of the minimums

Analysis of Toothbrush Rig Parameter Estimation Using Different Model Orders in Real-Coded Genetic Algorithm (RCGA)

System identification is a method to build a model for a dynamic system from the experimental data. In this paper, optimization technique was applied to optimize the objective function that lead to satisfying solution which obtain the dynamic model of the system. Real-coded genetic algorithm (RCGA) as a stochastic global search method was applied for optimization. Hence, the model of the plant was represented by the transfer function from the identified parameters obtained from the optimization process. For performance analysis of toothbrush rig parameter estimation, there were six different model orders have been considered where each of model order has been analyzed for 10 times. The influence of conventional genetic algorithm parameter - generation gap has been investigated too. The statistical analysis was used to evaluate the performance of the model based on the objective function which is the Mean Square Error (MSE). The validation test-through correlation analysis was used to validate the model. The model of model order 2 is chosen as the best model as it has fulfilled the criteria involved in selecting the accurate model. Generation gap used was 0.5 has shorten the algorithm convergence time without affecting the model accuracy.

A robust approach to design a single facility layout plan in dynamic manufacturing environments using a permutation-based genetic algorithm

During the past few decades, developing efficient methods to solve dynamic facility layout problems has been focused on significantly by practitioners and researchers. More specifically meta-heuristic algorithms, especially genetic algorithm, have been proven to be increasingly helpful to generate sub-optimal solutions for large-scale dynamic facility layout problems. Nevertheless, the uncertainty of the manufacturing factors in addition to the scale of the layout problem calls for a mixed genetic algorithm–robust approach that could provide a single unlimited layout design. The present research aims to devise a customized permutation-based robust genetic algorithm in dynamic manufacturing environments that is expected to be generating a unique robust layout for all the manufacturing periods. The numerical outcomes of the proposed robust genetic algorithm indicate significant cost improvements compared to the conventional genetic algorithm methods and a selective number of other heuristic and meta-heuristic techniques.

Chaotic Map Model-Based Interference Employed in Quantum-Inspired Genetic Algorithm to Determine the Optimum Gray Level Image Thresholding

In this chapter, a Quantum-Inspired Genetic Algorithm (QIGA) is presented. The QIGA adopted the inherent principles of quantum computing and has been applied on three gray level test images to determine their optimal threshold values. Quantum random interference based on chaotic map models and later quantum crossover, quantum mutation, and quantum shift operation have been applied in the proposed QIGA. The basic features of quantum computing like qubit, superposition of states, coherence and decoherence, etc. help to espouse parallelism and time discreteness in QIGA. Finally, the optimum threshold value has been derived through the quantum measurement phase. In the proposed QIGA, the selected evaluation metrics are Wu's algorithm, Renyi's algorithm, Yen's algorithm, Johannsen's algorithm, Silva's algorithm, and finally, linear index of fuzziness, and the selected gray level images are Baboon, Peppers, and Corridor. The conventional Genetic Algorithm (GA) and Quantum Evolutionary Algorithm (QEA) proposed by Han et al. have been run on the same set of images and evaluation metrics with the same parameters as QIGA. Finally, the performance analysis has been made between the proposed QIGA with the conventional GA and later with QEA proposed by Han et al., which reveals its time efficacy compared to GA along with the drawbacks in QEA.

A New Genetic-Based Approach for Function Optimization

We present a novel genetic-based algorithm for optimizing n-D simple-bounded continuous functions. In this paper, we propose a new mutation operator, called rotational mutation. The proposed approach starts from the vertices of the polytope created by the simple bounds, as the initial population. Similar to the conventional genetic algorithm, we calculate the optimum point of each population based on its cost value using the elitism mechanism. Then, we create the new generations based on the proposed rotational mutation and the conventional crossover operators. We have evaluated the algorithm on the two well-known test problems. Experimental results showed that the proposed approach outperforms the conventional genetic algorithm, in terms of the number of generations.

Chaotic Map Model-Based Interference Employed in Quantum-Inspired Genetic Algorithm to Determine the Optimum Gray Level Image Thresholding

In this chapter, a Quantum-Inspired Genetic Algorithm (QIGA) is presented. The QIGA adopted the inherent principles of quantum computing and has been applied on three gray level test images to determine their optimal threshold values. Quantum random interference based on chaotic map models and later quantum crossover, quantum mutation, and quantum shift operation have been applied in the proposed QIGA. The basic features of quantum computing like qubit, superposition of states, coherence and decoherence, etc. help to espouse parallelism and time discreteness in QIGA. Finally, the optimum threshold value has been derived through the quantum measurement phase. In the proposed QIGA, the selected evaluation metrics are Wu’s algorithm, Renyi’s algorithm, Yen’s algorithm, Johannsen’s algorithm, Silva’s algorithm, and finally, linear index of fuzziness, and the selected gray level images are Baboon, Peppers, and Corridor. The conventional Genetic Algorithm (GA) and Quantum Evolutionary Algorithm (QEA) proposed by Han et al. have been run on the same set of images and evaluation metrics with the same parameters as QIGA. Finally, the performance analysis has been made between the proposed QIGA with the conventional GA and later with QEA proposed by Han et al., which reveals its time efficacy compared to GA along with the drawbacks in QEA.

Online Self-Test System Grouping Function of Genetic Algorithm-Based Optimization

The paper mainly discusses the genetic algorithm to optimize the test paper module and Research and Implementation of online self-test system. Online self-test characteristics of the system, coding, crossover and mutation improvement on traditional genetic algorithm. Experimental results show that, the improved genetic algorithm has better performance than the conventional genetic algorithm, improves the efficiency of solving the quality of test paper and issues, promotes a more widely used online self-testing system in the field of education.

A Hybrid Genetic Algorithm to Minimize Total Tardiness for Unrelated Parallel Machine Scheduling with Precedence Constraints

The paper presents a novel hybrid genetic algorithm (HGA) for a deterministic scheduling problem where multiple jobs with arbitrary precedence constraints are processed on multiple unrelated parallel machines. The objective is to minimize total tardiness, since delays of the jobs may lead to punishment cost or cancellation of orders by the clients in many situations. A priority rule-based heuristic algorithm, which schedules a prior job on a prior machine according to the priority rule at each iteration, is suggested and embedded to the HGA for initial feasible schedules that can be improved in further stages. Computational experiments are conducted to show that the proposed HGA performs well with respect to accuracy and efficiency of solution for small-sized problems and gets better results than the conventional genetic algorithm within the same runtime for large-sized problems.

Solving Collaborative Manufacturing Resources Optimization Deployment Problems Based on Improved DNA Genetic Algorithm

In this paper, According to the collaborative manufacturing resources optimization deployment problems, designed subsection crossover and subsection mutation based on process code, adopted fitness scaling method and ranking method to select operators, proposed an improved genetic algorithm based on DNA computation for solving the resources optimization deployment problems, so that the offspring are better able to inherit the good features of parent. Through simulation, tested the designed algorithm performance; by comparing with conventional genetic algorithm test results, it proved the validity of the designed algorithm.