Enhanced Differential Evolution Based on Adaptive Mutation and Wrapper Local Search Strategies for Global Optimization Problems

AbstractDifferential evolution (DE) is a simple, powerful optimization algorithm, which has been widely used in many areas. However, the choices of the best mutation and search strategies are difficult for the specific issues. To alleviate these drawbacks and enhance the performance of DE, in this paper, the hybrid framework based on the adaptive mutation and Wrapper Local Search (WLS) schemes, is proposed to improve searching ability to efficiently guide the evolution of the population toward the global optimum. Furthermore, the effective particle encoding representation named Particle Segment Operation-Machine Assignment (PSOMA) that we previously published is applied to always produce feasible candidate solutions for solving the Flexible Job-shop Scheduling Problem (FJSP). Experiments were conducted on comprehensive set of complex benchmarks including the unimodal, multimodal and hybrid composition function, to validate performance of the proposed method and to compare with other state-of-the art DE variants such as jDE, JADE, MDE_pBX etc. Meanwhile, the hybrid DE model incorporating PSOMA is used to solve different representative instances based on practical data for multi-objective FJSP verifications. Simulation results indicate that the proposed method performs better for the majority of the single-objective scalable benchmark functions in terms of the solution accuracy and convergence rate. In addition, the wide range of Pareto-optimal solutions and more Gantt chart decision-makings can be provided for the multi-objective FJSP combinatorial optimizations

Impact of body-mass factors on setup displacement in patients with head and neck cancer treated with radiotherapy using daily on-line image guidance

BACKGROUND: To determine the impact of body-mass factors (BMF) before radiotherapy and changes during radiotherapy on the magnitude of setup displacement in patients with head and neck cancer (HNC). METHODS: The clinical data of 30 patients with HNC was analyzed using the alignment data from daily on-line on-board imaging from image-guided radiotherapy. BMFs included body weight, body height, and the circumference and bilateral thickness of the neck. Changes in the BMFs during treatment were retrieved from cone beam computed tomography at the 10th and 20th fractions. Setup errors for each patient were assessed by systematic error (SE) and random error (RE) through the superior-inferior (SI), anterior-posterior (AP), and medial-lateral (ML) directions, and couch rotation (CR). Using the median values of the BMFs as a cutoff, the impact of the factors on the magnitude of displacement was assessed by the Mann–Whitney U test. RESULTS: A higher body weight before radiotherapy correlated with a greater AP-SE (p = 0.045), SI-RE (p = 0.023), and CR-SE (p = 0.033). A longer body height was associated with a greater SI-RE (p = 0.002). A performance status score of 1 or 2 was related to a greater AP-SE (p = 0.043), AP-RE (p = 0.015), and SI-RE (p = 0.043). Among the ratios of the BMFs during radiotherapy, the values at the level of mastoid tip at the 20(th) fraction were associated with greater setup errors. CONCLUSIONS: To reduce setup errors in patients with HNC receiving RT, the use of on-line image-guided radiotherapy is recommended for patients with a large body weight or height, and a performance status score of 1–2. In addition, adaptive planning should be considered for those who have a large reduction ratio in the circumference (<1) and thickness (<0.94) over the level of the mastoid tip during the 20(th) fraction of treatment

Quantum correlation generation capability of experimental processes

Einstein-Podolsky-Rosen (EPR) steering and Bell nonlocality illustrate two different kinds of correlations predicted by quantum mechanics. They not only motivate the exploration of the foundation of quantum mechanics, but also serve as important resources for quantum-information processing in the presence of untrusted measurement apparatuses. Herein, we introduce a method for characterizing the creation of EPR steering and Bell nonlocality for dynamical processes in experiments. We show that the capability of an experimental process to create quantum correlations can be quantified and identified simply by preparing separable states as test inputs of the process and then performing local measurements on single qubits of the corresponding outputs. This finding enables the construction of objective benchmarks for the two-qubit controlled operations used to perform universal quantum computation. We demonstrate this utility by examining the experimental capability of creating quantum correlations with the controlled-phase operations on the IBM Quantum Experience and Amazon Braket Rigetti superconducting quantum computers. The results show that our method provides a useful diagnostic tool for evaluating the primitive operations of nonclassical correlation creation in noisy intermediate scale quantum devices.Comment: 5 figures, 3 appendice

Learning to predict expression efficacy of vectors in recombinant protein production

<p>Abstract</p> <p>Background</p> <p>Recombinant protein production is a useful biotechnology to produce a large quantity of highly soluble proteins. Currently, the most widely used production system is to fuse a target protein into different vectors in <it>Escherichia coli </it>(<it>E. coli</it>). However, the production efficacy of different vectors varies for different target proteins. Trial-and-error is still the common practice to find out the efficacy of a vector for a given target protein. Previous studies are limited in that they assumed that proteins would be over-expressed and focused only on the solubility of expressed proteins. In fact, many pairings of vectors and proteins result in no expression.</p> <p>Results</p> <p>In this study, we applied machine learning to train prediction models to predict whether a pairing of vector-protein will express or not express in <it>E. coli</it>. For expressed cases, the models further predict whether the expressed proteins would be soluble. We collected a set of real cases from the clients of our recombinant protein production core facility, where six different vectors were designed and studied. This set of cases is used in both training and evaluation of our models. We evaluate three different models based on the support vector machines (SVM) and their ensembles. Unlike many previous works, these models consider the sequence of the target protein as well as the sequence of the whole fusion vector as the features. We show that a model that classifies a case into one of the three classes (no expression, inclusion body and soluble) outperforms a model that considers the nested structure of the three classes, while a model that can take advantage of the hierarchical structure of the three classes performs slight worse but comparably to the best model. Meanwhile, compared to previous works, we show that the prediction accuracy of our best method still performs the best. Lastly, we briefly present two methods to use the trained model in the design of the recombinant protein production systems to improve the chance of high soluble protein production.</p> <p>Conclusion</p> <p>In this paper, we show that a machine learning approach to the prediction of the efficacy of a vector for a target protein in a recombinant protein production system is promising and may compliment traditional knowledge-driven study of the efficacy. We will release our program to share with other labs in the public domain when this paper is published.</p

Fabrication and Application of Iron(III)-Oxide Nanoparticle/Polydimethylsiloxane Composite Cone in Microfluidic Channels

This paper presented the fabrication and applications of an iron(III)-oxide nanoparticle/polydimethylsiloxane (PDMS) cone as a component integrated in lab on a chip. The two main functions of this component were to capture magnetic microbeads in the microfluid and to mix two laminar fluids by generating disturbance. The iron(III)-oxide nanoparticle/PDMS cone was fabricated by automatic dispensing and magnetic shaping. Three consecutive cones of 300 μm in height were asymmetrically placed along a microchannel of 2 mm in width and 1.1 mm in height. Flow passing the cones was effectively redistributed for Renolds number lower than . Streptavidin-coated magnetic microbeads which were bound with biotin were successfully captured by the composite cones as inspected under fluorescence microscope. The process parameters for fabricating the composite cones were investigated. The fabricated cone in the microchannel could be applied in lab on a chip for bioassay in the future