Master-slave Parallel Genetic Algorithm Research Articles

Reliability evaluation and optimization design method of piezoelectric actuator vibration suppression system

As an effective means of structural vibration suppression, piezoelectric actuator vibration suppression system(PAVSS) has been gradually applied in structural vibration control. However, the performance degradation or failure of piezoelectric actuator under long service life will lead to the degradation of service reliability of PAVSS, so the scheme obtained by deterministic optimization method is difficult to ensure the service reliability. Aiming at the reliability evaluation and optimal design of PAVSS in service, the failure mechanism considering of the performance degradation or failure of piezoelectric actuator is analyzed, and the reliability evaluation method based on nested sampling and weighted statistics is constructed, which solves the reliability evaluation problem of PAVSS with the characteristics of load-sharing and redundancy. A reliability-based optimization method based on master-slave parallel genetic algorithm is proposed to optimize the position and angle of the actuators. At last, the feasibility and effectiveness of the proposed method are proved by an example, which not only provides a theoretical basis for the high reliability design of PAVSS in service, but also provides a new solution for the engineering problem of structural vibration control.

Optimum State-of-Charge Operating Range for Frequency Regulation of Energy Storage Systems Using a Master–Slave Parallel Genetic Algorithm

Lithium batteries are used for frequency regulation in power systems because of their fast response and high efficiency. Lithium batteries have different life characteristics depending on their type, and it is necessary to set the optimal state-of-charge (SOC) operating range considering these characteristics to obtain the maximum gain. In general, narrowing the operating range increases the service life but may lower the performance of charging and discharging operations in response to frequency fluctuations, and vice versa. We present performance assessment indicators that consider charging and discharging due to frequency variations and lifespan of the batteries. However, to evaluate the performance, while reflecting the non-linear life characteristics of lithium batteries, simulating the entire operation is necessary, which requires a long calculation time. Therefore, we propose a master–slave parallel genetic algorithm to derive the optimal SOC operating range with reduced calculation time. A simulation program was implemented to evaluate the computational performance that determines the optimal SOC range. The proposed method reduces the calculation time while considering the non-linear life characteristics of lithium batteries. It was confirmed that a more accurate SOC operating range could be calculated by simulating the entire life span.

A Master Slave Parallel Genetic Algorithm for Feature Selection in High Dimensional Datasets

Feature Selection in High Dimensional Datasets is a combinatorial problem as it selects the optimal subsets from N dimensional data having 2N possible subsets. Genetic Algorithms are generally a good choice for feature selection in large datasets, though for some high dimensional problems it may take varied amount of time - few seconds, few hours or even few days. Therefore, it is important to use Genetic Algorithms that can give quality results in reasonably acceptable time limit. For this purpose, it is becoming necessary to implement Genetic Algorithms in an efficient manner. In this paper, a Master Slave Parallel Genetic Algorithm is implemented as a Feature Selection procedure to diminish the time intricacies of sequential genetic algorithm. This paper describes the speed gains in parallel Master-Slave Genetic Algorithm and also discusses the theoretical analysis of optimal number of slaves required for an efficient master slave implementation. The experiments are performed on three high-dimensional gene expression data. As Genetic Algorithm is a wrapper technique and takes more time to find the importance of any feature, Information Gain technique is used first as pre-processing task to remove the irrelevant features.

High-speed detection of emergent market clustering via an unsupervised parallel genetic algorithm

We implement a master-slave parallel genetic algorithm with a bespoke log-likelihood fitness function to identify emergent clusters within price evolutions. We use graphics processing units (GPUs) to implement a parallel genetic algorithm and visualise the results using disjoint minimal spanning trees. We demonstrate that our GPU parallel genetic algorithm, implemented on a commercially available general purpose GPU, is able to recover stock clusters in sub-second speed, based on a subset of stocks in the South African market. This approach represents a pragmatic choice for low-cost, scalable parallel computing and is significantly faster than a prototype serial implementation in an optimised C-based fourth-generation programming language, although the results are not directly comparable because of compiler differences. Combined with fast online intraday correlation matrix estimation from high frequency data for cluster identification, the proposed implementation offers cost-effective, near-real-time risk assessment for financial practitioners.

Parallel genetic algorithm based automatic path planning for crane lifting in complex environments

Abstract Heavy lifting is a common and important task in industrial plants. It is conducted frequently during the time of plant construction, maintenance shutdown and new equipment installation. To find a safe and cost effective way of lifting, a team works for weeks or even months doing site investigation, planning and evaluations. This paper considers the lifting path planning problem for terrain cranes in complex environments. The lifting path planning problem takes inputs such as the plant environment, crane mechanical data, crane position, start and end lifting configurations to generate the optimal lifting path by evaluating costs and safety risks. We formulate the crane lifting path planning as a multi-objective nonlinear integer optimization problem with implicit constraints. It aims to optimize the energy cost, time cost and human operation conformity of the lifting path under constraints of collision avoidance and operational limitations. To solve the optimization problem, we design a Master–Slave Parallel Genetic Algorithm and implement the algorithm on Graphics Processing Units using CUDA programming. In order to handle complex plants, we propose a collision detection strategy using hybrid configuration spaces based on an image-based collision detection algorithm. The results show that the method can efficiently generate high quality lifting paths in complex environments.

Structural Optimization of Composite Wing Based on Different Blending Models Using Genetic Algorithm

In previous studies guide-based blending including inner and outer blending has been found to be an efficient way to deal with large composite optimization problem considering structural integrity. A new blending model named generalized blending based on genetic algorithm for composite optimization is presented. First, On the basis of region division, a length-control indicator is introduced to decide the covered regions of each ply for the generalized blending model. Also, the master-slave parallel genetic algorithm (GA) is adopted to decrease optimization time. Finally, the three blending models are used for a large composite wing optimization. The result shows that the three optimal designs are manufacturable and the generalized blending model has more design freedom in blending designs.

Structure Optimization of Large Composite Wing Box with Parallel Genetic Algorithm

An optimization method for the design of large composite structures is presented. This optimization method can make the stacking sequence and thickness optimize simultaneously. First, on the basis of region division, a lengthcontrol indicator is added to decide the covered regions of each ply. Second, the master–slave parallel genetic algorithm is adopted to make it a high-efficiency algorithm. Finally, the method proposed is used to optimize a composite wing box to demonstrate its high efficiency. The acceleration effect of the parallel genetic algorithm is also analyzed.

Master-Slave Parallel Genetic Algorithm Based on MapReduce Using Cloud Computing

The implementation platforms of parallel genetic algorithms (PGAs) include high performance computer, cluster and Grid. Contrast with the traditional platform, a Master-slave PGA based on MapReduce (MMRPGA) of cloud computing platform was proposed. Cloud computing is a new computer platform, suites for larger-scale computing and is low cost. At first, describes the design of MMRPGA, in which the whole evolution is controlled by Master and the fitness computing is assigned to Slaves; then deduces the theoretical speed-up of MMRPGA; at last, implements MMRPGA on Hadoop and compares the speed-up with traditional genetic algorithm, the experiment result shows MMRPGA can achieve slightly lower linear speed-up with Mapper’s number.

Protein structure prediction with the 3D-HP side-chain model using a master–slave parallel genetic algorithm

Abstract This work presents a master-slave parallel genetic algorithm for the protein folding problem, using the 3D-HP side-chain model (3D-HP-SC). This model is sparsely studied in the literature, although more expressive than other lattice models. The fitness function proposed includes information not only about the free-energy of the conformation, but also compactness of the side-chains. Since there is no benchmark available to date for this model, a set of 15 sequences was used, based on a simpler model. Results show that the parallel GA achieved a good level of efficiency and obtained biologically coherent results, suggesting the adequacy of the methodology. Future work will include new biologically-inspired genetic operators and more experiments to create new benchmarks.

Parallel Genetic Algorithms for calibrating Cellular Automata models: Application to lava flows

Cellular Automata are highly nonlinear dynamical systems which are suitable for simulating natural phenomena whose behaviour may be specified in terms of local interactions. The Cellular Automata model SCIARA, developed for the simulation of lava flows, demonstrated to be able to reproduce the behaviour of Etnean events. However, in order to apply the model for the prediction of future scenarios, a thorough calibrating phase is required. This work presents the application of Genetic Algorithms, general-purpose search algorithms inspired to natural selection and genetics, for the parameters optimisation of the modelSCIARA. Difficulties due to the elevated computational time suggested the adoption a Master-Slave Parallel Genetic Algorithm for the calibration of the model with respect to the 2001 Mt. Etna eruption. Results demonstrated the usefulness of the approach, both in terms of computing time and quality of performed simulations.