Influence Of Parameter Settings Research Articles

A decomposition-based multi-objective Jaya algorithm for lot-streaming job shop scheduling with variable sublots and intermingling setting

This study examined the lot-streaming job shop scheduling problem (LSJSP) with variable sublots and intermingling setting, which has rarely been considered in the literature and is beneficial to real industry settings. Upon variable sublots and intermingling setting, a multi-objective mixed-integer linear programming model (MILP) is formulated to achieve a tradeoff between the shortest tardiness and the minimum number of transferred sublots. Considering the NP-hard characteristic of the problem, the MILP model would get restricted in solving medium and large scale instances. Thus building an efficient multi-objective optimization algorithm is desirable. Motivated by the Jaya algorithm and decomposition idea, we propose a new algorithm framework called a decomposition based multi-objective Jaya algorithm (MOJA/D) to achieve a balance between exploration and exploitation. This MOJA/D framework can provide some guidance in algorithm construction for other multi-objective optimization problems. Following the MOJA/D framework, a specific MOJA/D metaheuristic is further designed for coping with the proposed problem. First, a novel forward and backward decoding strategy is designed to implement coevolution among different sub-problems and enhance the population diversity. Second, the Jaya updating mechanism is designed based on problem knowledge to guide the solutions moving towards the best solution and away from the worst solution. Moreover, considering the tradeoff between two conflicting objectives, the problem-specific local search strategies are designed and integrated with the neighborhood based local search strategies to enhance exploitation in the search process. The influence of parameter setting on MOJA/D is investigated by using design of experiments. The statistical results from extensive computational experiments demonstrate the effectiveness of the specific algorithm designs and show that the MOJA/D metaheuristic is superior to the existing algorithms in solving the proposed problem.

Elite Archive-Assisted Adaptive Memetic Algorithm for a Realistic Hybrid Differentiation Flowshop Scheduling Problem

This research presents an original and efficient elite archive-assisted adaptive memetic algorithm (EAMA) to deal with a realistic hybrid differentiation flowshop scheduling problem (HDFSP) with the objective of total completion time minimization. In this scheduling problem, each job consists of multiple parts and the jobs are divided into different types. The manufacturing of a job is comprised of three consecutive stages: 1) parts fabrication on first-stage parallel machines; 2) parts assembly on second-stage single machine; and 3) job differentiation on one of third-stage dedicated machines. We provide a mixed-integer programming model, derive three lower bounds, and further present the EAMA metaheuristic for HDFSP. The EAMA is initialized heuristically, and its global exploration is performed by a differential evolution, which includes three newly designed operators: 1) elite-driven discretized differential mutation; 2) probability crossover; and 3) biased selection. To enhance the local search, an external elite archive is set and evolved in parallel with global exploration by a meta-Lamarckian learning-based adaptive multistage local search and a variable length-based adaptive block-insertion local search. After the global exploration and local exploitation, an elite sharing strategy is used to exchange the excellent information between population and elite archive, and an adaptive restart strategy is used to diversify the population. The influence of parameter setting on EAMA is surveyed by using an improved design-of-experiment. The statistical results from extensive computational experiments demonstrate the effectiveness of the special designs and show that EAMA performs more efficient than the existing algorithms in solving the problem under consideration.

Using a new phase-locked visual feedback protocol to affirm simpler models for alpha dynamics

Alpha band oscillations are the most prominent rhythmic oscillations in EEG, which are related to various types of mental diseases, such as attention deficit hyperactivity disorder, anxiety, and depression. However, the dynamics of alpha oscillations, especially how the endogenous alpha oscillations be entrained by exogenous stimulus, are still unclear. Recently, a newly-developed phase-locked visual feedback (PLVF) protocol has shown effectiveness in modulating alpha rhythm, which provides empirical evidence for the further investigation of the neural mechanism of alpha dynamics. In this work, extensive numerical simulations based on four well-studied models were used to investigate the questions that (1) What kind of dynamic model exhibits a modulation phenomenon of PLVF? (2) What is the dynamic mechanism of PLVF for alpha modulation? (3) Which factors affect the modulation effects in PLVF? The result indicates that the dynamics of endogenous alpha oscillations are close to a simpler dynamic structure, like fixed-point attractor or limit-cycle attractor, which shows a global consistent dynamic behavior at different phases of the alpha oscillation. The further analysis explains the dynamic mechanism of PLVF for amplitude and frequency modulation of the alpha rhythm, as well as the influence of parameter settings in the modulation. All these findings provide a deeper understanding of the endogenous alpha oscillations entrained by exogenous phased locked visual stimulus and lead in turn to the refinement of a control strategy for alpha modulation, which could potentially be used in developing new neural modulation methods for cognitive enhancement and mental diseases treatment.

A green scheduling algorithm for the distributed flowshop problem

In recent years, sustainable development and green manufacturing have attracted widespread attention to environmental problems becoming increasingly serious. Meanwhile, affected by the intensification of market competition and economic globalization, distributed manufacturing systems have become increasingly common. This paper addresses the energy-efficient scheduling of the distributed permutation flowshop (EEDPFSP) with the criteria of minimizing both total flow time and total energy consumption. Considering the distributed and multi-objective optimization complexity, an improved NSGAII algorithm (INSGAII) is proposed. First, we analyze the problem-specific characteristics and designed new operators based on the knowledge of the problem. Second, four constructive heuristic algorithms are proposed to produce high-quality initial solutions. Third, inspired by the artificial bee colony algorithm, we propose a new colony generation method using the operators designed. Fourth, a local intensification is designed for exploiting better non-dominated solutions. The influence of parameter settings is investigated by experiments to determine the optimal parameter configuration of the INSGAII. Finally, a large number of computational tests and comparisons have been carried out to verify the effectiveness of the proposed INSGAII in solving EEDPFSP.

Performance of Commercially Open Refrigerated Showcases with and without Ice Storage—A Case Study

This study examines the applicability of the ice storage systems in the small commercial refrigerated showcases through experimental analysis. R-404A is used as the working fluid and various influence of parameter settings and improvements, are discussed in details. In the ice storage system, the condenser is changed from air-cooling (refrigeration mode) to an immersion type that is placed in an ice storage tank, the corresponding condensing temperature and pressure are reduced appreciably. This increased the efficiency and can effectively reduce the peak power consumption. The reduction of the condensing temperature and pressure increased the coefficient of performance (COP) from 3.6 (refrigeration mode) to 6.35 (melting mode), effectively enhancing the refrigeration efficiency. The results indicated that the ice storage system could effectively increase the coefficient of performance from 3.6 to 6.35 during ice melting when compared to the conventional refrigeration system. It also can shift approximately 35% of the power during peak hours and lower the energy cost by USD$ 17.13 per month.

A three-dimensional numerical method for siphon drainage process simulation

Siphon drainage using inclined borehole and 4 mm inner diameter pipes is an efficient and stable measure for long-term landslide drainage. In order to compare alternative drainage arrangements more efficiently, a numerical method is proposed for siphon drainage process simulation. The novelty of this method is to simulate the dynamic process of siphon drainage by a series of iterative seepage analyses. The whole drainage process is divided into many small time steps and each one corresponds to an independent seepage analysis. The iterative operation is fully automated by reading and rewriting the calculation document with Python code. The siphon flow rate in each analysis is determined by the total head at the siphon pipe inlet obtained from the previous analysis result. An empirical formula based on experimental data is used for flow rate calculation. SEEP3D is adopted to perform the three-dimensional seepage analysis. BUILD3D along with computer-aided design software is used to build the three-dimensional model with a realistic topographic surface. The proposed method is illustrated and validated with a case, the Shaqiucun landslide, China. A parameter study is carried out and the influence of parameter setting on the drainage effect is discussed.

Cascading failures in spatial complex networks

In this paper, cascading failures in spatial complex networks are studied. The model we use, the spatial random geometric network, turns out to be robust to random attacks but very fragile to intentional attacks. Then, the relationship between the topology characteristics and the robustness of spatial networks is studied. By comparing with the Erdős–Rényi (ER) random network, the Barabási–Albert (BA) scale-free network and the Watts–Strogatz (WS) small-world network, the “robust yet fragile” property of spatial random geometric network is related to its heterogeneous betweenness distribution. Finally, the influence of parameter settings on the network robustness is studied. The results show that both the increase of the size and the expansion of the communication range of nodes will increase the average degree of the network, thus enhancing its robustness under intentional attacks.

Classification of hemodynamically significant stenoses from dynamic CT perfusion and CTA myocardial territories.

Myocardial blood flow (MBF) obtained by dynamic CT perfusion (CTP) has been recently introduced to assess hemodynamic significance of coronary stenosis in coronary artery disease. The diagnostic performance of dynamic CTP MBF is limited due to subjective interpretation of MBF maps and MBF variations caused by physiological, methodological, and technical issues. In this paper, we introduce a novel method to quantify the hypoperfused volume (HPV) in myocardial territories derived from CT angiography (CTA) to overcome the limitations of current dynamic CTP MBF analysis methods. The diagnostic performance of HPV in classifying significant stenoses was evaluated on 22 patients (57 vessels) that underwent CTA, CTP and invasive fractional flow reserve (FFR). FFR was used as the standard of reference to determine stenosis significance. The diagnostic performance was compared to that of the mean MBF computed in regions manually annotated by an expert (MA-MBF). HPV was derived by thresholding the MBF in myocardial territories constructed from CTA by locating the closest artery. Diagnostic performance was evaluated using leave-one-case out cross-validation. Inter-observer reproducibility was assessed by performing annotations of coronary seeds (HPV) and manual regions (MA-MBF) with two users. In addition, the influence of different parameter settings on the diagnostic performance of HPV was assessed. Leave-one-case out cross-validation showed that HPV has an accuracy of 72% (58-83%) with sensitivity of 72% (47-90%) and specificity of 72% (58-83%). The accuracy of MA-MBF was 70% (57-82%) with a sensitivity of 50% (26-74%) and a specificity of 79% (64-91%). The Spearman correlation and the kappa statistic was (ρ = 0.94, κ = 0.86) for HPV and (ρ = 0.72, κ = 0.82) for MA-MBF. The influence of parameter settings on HPV based diagnostic performance was not significant. The proposed HPV accurately classifies hemodynamically significant stenoses with a level of accuracy comparable to the mean MBF in regions annotated by an expert. HPV improves inter-observer reproducibility as compared to MA-MBF by providing a more objective criterion to associate the stenotic coronary with the supplied myocardial territory.

Research on Dental Ceramic Grinding: A Review

Dental ceramics has become dominant materials used in dental restorations. Dental ceramics have several advantages, such as stable performance, good bio-compatibility, little possibility to attach plaque, and similarity to the color of permanent tooth. However complex surface characteristics and hard and brittle properties of dental ceramic materials caused difficulties in the processing and subsequent grinding. The complicated craft and high failure rate of dental ceramics greatly limit its wide application in clinical. Thus, fully understanding the special cutting tools grinding performance and researching the material damage process caused by grinding temperature and grinding force are of great significance. Research on dental ceramic grinding was reviewed in this paper. The removal mechanism of dental ceramic materials and the influence of parameters settings on the grinding force, grinding temperature, and the surface quality have been studied. Besides the existing problems in dental ceramic grinding technology were pointed out.

Simulation of dynamics of hydraulic system with proportional control valve

Dynamics of a hydraulic system is influenced by several parameters, in this case mainly by proportional control valve, oil bulk modulus, oil viscosity, mass load etc. This paper will be focused on experimental measurement and mathematical simulation of dynamics of a hydraulic system with proportional control valve, linear hydraulic cylinder and mass load. The measurement is performed on experimental equipment that enables realization of dynamic processes of the hydraulic system. Linear hydraulic cylinder with mass load is equipped with position sensor of piston. The movement control of piston rod is ensured by the proportional control valve. The equipment enables to test an influence of parameter settings of regulator of the proportional control valve on position and pressure system responses. The piston position is recorded by magnetostrictive sensor that is located in drilled piston rod side of the linear hydraulic cylinder. Pressures are measured by piezoresistive sensors on the piston side and the piston rod side of the hydraulic cylinder. The measurement is performed during movement of the piston rod with mass load to the required position. There is realized and verified a mathematical model using Matlab SimHydraulics software for this hydraulic system.

A competitive memetic algorithm for the distributed two-stage assembly flow-shop scheduling problem

This article addresses the distributed two-stage assembly flow-shop scheduling problem (DTSAFSP) with makespan minimisation criterion. A mixed integer linear programming model is presented, and a competitive memetic algorithm (CMA) is proposed. When designing the CMA, a simple encoding scheme is proposed to represent the factory assignment and the job processing sequence; and a ring-based neighbourhood structure is designed for competition and information sharing. Moreover, some knowledge-based local search operators are developed to enhance the exploitation ability. The influence of parameter setting on the CMA is investigated using the analysis of variance method. Extensive computational tests and comparisons are carried out, which demonstrate the effectiveness of the proposed CMA in solving the DTSAFSP.

Performance analysis and parameter setting for Tong detection algorithm

Tong detection algorithm is a common used signal detection algorithm. But the overall conclusions about statistical performance and parameter setting of Tong detection algorithm are absent. Aiming at these problems,statistical and recursive analysis methods were used to carry on theoretical analysis about mean value and variance of dwell times in signal search unit in detail,the corresponding expressions were given,a maximum dwell times constraint method was offered according to the 3Sigma principle. The relationships between detection probability,average dwell times of noise search unit and signal search unit,initial value and maximum value of algorithm counter,pre-detection signal to noise ratio were analyzed. Analysis results indicate that the parameters of Tong detection algorithm should be set according to signal strength and search modes,and the influence of parameter setting on detection probability and average dwell times of noise search unit or signal search unit should be balanced.

A knowledge-based multi-agent evolutionary algorithm for semiconductor final testing scheduling problem

The final testing process ensures the quality of the products in the semiconductor manufacturing factory. Scheduling for the final testing process is crucial to the economic efficiency of production. In this paper, an effective knowledge-based multi-agent evolutionary algorithm (KMEA) is proposed for solving the semiconductor final testing scheduling problem (SFTSP). In the KMEA, each agent is represented by a solution, which is a combination of the operation sequence vector and the machine assignment vector. A hybrid initialization mechanism is proposed to balance the diversity and the quality of the initial agents. In each iteration of evolution, the agents evolve by mutual-learning and competition based on the model of agent lattice. Moreover, a knowledge base is employed to store the useful information during the search process. The knowledge base is used to generate new agents in the competition phase. The computational complexity of the KMEA is analyzed, and the influence of parameter setting is also investigated. Finally, numerical simulation and comparisons are provided to demonstrate the effectiveness and efficiency of the KMEA in solving the test instances.

Influence of Parameter Settings in Voxel-based Morphometry 8

To investigate whether reproducibility of gray matter volumetry is influenced by parameter settings for VBM 8 using Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL) with region-of-interest (ROI) analyses. We prepared three-dimensional T1-weighted magnetic resonance images (3D-T1WIs) of 21 healthy subjects. All subjects were imaged with each of five MRI systems. Voxel-based morphometry 8 (VBM 8) and WFU PickAtlas software were used for gray matter volumetry. The bilateral ROI labels used were those provided as default settings with the software: Frontal Lobe, Hippocampus, Occipital Lobe, Orbital Gyrus, Parietal Lobe, Putamen, and Temporal Lobe. All 3D-T1WIs were segmented to gray matter with six parameters of VBM 8, with each parameter having between three and eight selectable levels. Reproducibility was evaluated as the standard deviation (mm³) of measured values for the five MRI systems. Reproducibility was influenced by 'Bias regularization (BiasR)', 'Bias FWHM', and 'De-noising filter' settings, but not by 'MRF weighting', 'Sampling distance', or 'Warping regularization' settings. Reproducibility in BiasR was influenced by ROI. Superior reproducibility was observed in Frontal Lobe with the BiasR1 setting, and in Hippocampus, Parietal Lobe, and Putamen with the BiasR3*, BiasR1, and BiasR5 settings, respectively. Reproducibility of gray matter volumetry was influenced by parameter settings in VBM 8 using DARTEL and ROI. In multi-center studies, the use of appropriate settings in VBM 8 with DARTEL results in reduced scanner effect.

An order-based estimation of distribution algorithm for stochastic hybrid flow-shop scheduling problem

In view of the stochastic nature of the data in real-world manufacturing systems, it is crucial to develop effective algorithms to solve the scheduling problems with uncertainty. In this paper, an order-based estimation of distribution algorithm (OEDA) is proposed to solve the hybrid flow-shop scheduling problem (HFSP) with stochastic processing times. Considering the effectiveness and robustness of a schedule, it aims to minimise the makespan of the initial scenario as well as the deviation of all results of the stochastic scenarios and the initial one. To be specific, a bi-objective function is used to evaluate the individuals of the population, and a probability model is designed to describe the probability distribution of the solution space. Meanwhile, optimal computing budget allocation (OCBA) technique is employed to provide a reliable identification to the good solutions among the population. A mechanism is also presented to update the probability model with the superior individuals that are identified by the OCBA. The new individuals are generated by sampling the probability model to track the area with promising solutions. In addition, the influence of parameter setting is investigated based on Taguchi method of design-of-experiment (DOE), and a suitable parameter setting is suggested. Extensive numerical testing results and comparisons with the existing algorithm are provided, which demonstrate the effectiveness of the proposed OEDA.

Real-time α/γ pulse shape discrimination in CsI(Tl) scintillators

A real-time α/γ pulse shape discriminator based on CsI(Tl) scintillators is developed. The charge comparison method is adopted as an inserted algorithm. In this paper, the design scheme of the system is introduced and the real-time system is tested with a 60Co-γ source and a 241Am-α source. The influence of parameter setting on the discrimination result is investigated and the best parameter setting is provided. The results show that the designed real-time system has a small size. The best figure of merits is above 1.4. The system can discriminate α/γ events exactly and its counting rate of events can achieve 3×105 per second.

An effective hybrid immune algorithm for solving the distributed permutation flow-shop scheduling problem

In this article, an effective hybrid immune algorithm (HIA) is presented to solve the distributed permutation flow-shop scheduling problem (DPFSP). First, a decoding method is proposed to transfer a job permutation sequence to a feasible schedule considering both factory dispatching and job sequencing. Secondly, a local search with four search operators is presented based on the characteristics of the problem. Thirdly, a special crossover operator is designed for the DPFSP, and mutation and vaccination operators are also applied within the framework of the HIA to perform an immune search. The influence of parameter setting on the HIA is investigated based on the Taguchi method of design of experiment. Extensive numerical testing results based on 420 small-sized instances and 720 large-sized instances are provided. The effectiveness of the HIA is demonstrated by comparison with some existing heuristic algorithms and the variable neighbourhood descent methods. New best known solutions are obtained by the HIA for 17 out of 420 small-sized instances and 585 out of 720 large-sized instances.

A hybrid artificial bee colony algorithm for the fuzzy flexible job-shop scheduling problem

A hybrid artificial bee colony (HABC) algorithm is proposed in this paper for solving the fuzzy flexible job-shop scheduling problem (FFJSP). First, the HABC utilises multiple strategies in a combined way to generate the initial solutions with certain quality and diversity as the food sources, and applies the left-shift decoding scheme to convert solutions to active schedules. Second, the exploitation search procedures based on the crossover operators for machine assignment and operation sequence in the employed bee phase are designed to generate the new neighbouring food sources. Third, the exploitation search procedures are also used to update the old food source in the onlooker bee phase with the new source based on the best-so-far source instead of the neighbouring sources. Fourth, to prevent premature convergence in the scout bee phase, the population is updated by the new source with an adjustable search radius. Meanwhile, a local search based on the variable neighbourhood search (VNS) is performed on the best-so-far solution to enhance the local intensification. Based on the Taguchi method of design of experiment, the influence of parameter setting is investigated and suitable parameter values are suggested. Numerical testing results and the comparisons with some existing algorithms demonstrate the effectiveness of the proposed HABC. Besides, the comparison between the HABC with and without VNS local search demonstrates the effectiveness of hybridising ABC-based exploration and VNS-based exploitation.

An effective estimation of distribution algorithm for the flexible job-shop scheduling problem with fuzzy processing time

Considering the fuzzy nature of the data in real-world scheduling, an effective estimation of distribution algorithm (EDA) is proposed to solve the flexible job-shop scheduling problem with fuzzy processing time. A probability model is presented to describe the probability distribution of the solution space. A mechanism is provided to update the probability model with the elite individuals. By sampling the probability model, new individuals can be generated among the search region with promising solutions. Moreover, a left-shift scheme is employed for improving schedule solution when idle time exists on the machine. In addition, some fuzzy number operations are used to calculate scheduling objective value. The influence of parameter setting is investigated based on the Taguchi method of design of experiment, and a suitable parameter setting is suggested. Numerical testing results and comparisons with some existing algorithms are provided, which demonstrate the effectiveness of the proposed EDA.

Adaptive unequal protection for wireless video transmission over IEEE 802.11e networks

Packet loss of video streams cannot be avoided at wireless links for limited wireless bandwidth and frequently changed environments. To provide differentiated Quality of Service (QoS) guarantees between multimedia and data services, IEEE 802.11e was proposed. However, its performance and flexibility need to be further improved. In this paper, after a survey on various modifications of IEEE 802.11e, we formulate the problem of video transmission over IEEE 802.11e networks to help scheme design and performance analysis. Then accompanied with in-depth analysis, an adaptive unequal protection schema is proposed, which is composed of three mechanisms: (1) Insert each video packet into the access category (AC) with the minimum relative queuing delay; (2) Assign each packet dynamically to a proper AC based on several parameters to guarantee the transmission of high priority frames; (3) Apply fuzzy logic controllers to adjust parameters so as to reply quickly to the variation of video data rate, coding structure and network load. Finally, regarding MPEG-4 codec as the example, we perform extensive evaluations and validate the effectiveness and flexibility of proposed scheme. Simulations are divided into WLAN and multihop parts, involving different video sequences and various traffic modes of data streams. Beside performance comparison between proposed scheme and other ones, influence of parameter setting and combination with routing algorithms are also evaluated.