T. Warren Liao

Two hybrid differential evolution algorithms for engineering design optimization

This paper presents two hybrid differential evolution algorithms for optimizing engineering design problems. One hybrid algorithm enhances a basic differential evolution algorithm with a local search operator, i.e., random walk with direction exploitation, to strengthen the exploitation ability, while the other adding a second metaheuristic, i.e., harmony search, to cooperate with the differential evolution algorithm so as to produce the desirable synergetic effect. For comparison, the differential evolution algorithm that the two hybrids are based on is also implemented. All algorithms incorporate a generalized method to handle discrete variables and Debs parameterless penalty method for handling constraints. Fourteen engineering design problems selected from different engineering fields are used for testing. The test results show that: (i) both hybrid algorithms overall outperform the differential evolution algorithms; (ii) among the two hybrid algorithms, the cooperative hybrid overall outperforms the other hybrid with local search; and (iii) the performance of proposed hybrid algorithms can be further improved with some effort of tuning the relevant parameters.

A dynamic threshold decision system for stock trading signal detection

Trading signal detection has become a very popular research topic in financial investment area. This paper develops a model using the Piecewise Linear Representations (PLR) and Artificial Neural Networks (ANNs) to analyze the nonlinear relationships between the stock closed price and various technical indexes, and uncovering the knowledge of trading signals hidden in historical data. Piecewise Linear Representation tools are applied to find the best stock turning points (trading signals) based on the historical data. These turning points represent short-term trading signals for selling or buying stocks from the market. This study further applies an Artificial Neural Network model to learn the connection weights from these historical turning points, and afterwards an exponential smoothing based dynamic threshold model is used to forecast the future trading signals. The stock trading signal is predicted using the neural network on a daily basis. The dynamic threshold bounds generated provide a guide for triggering a buy or sell decision when the ANN-predicted trading signal goes above or under the threshold bounds. Through a series of experiments, this research shows superior results than our previous research (Chang et al., 2009 [1]) and other benchmark researches.

BGM-BLA: A New Algorithm for Dynamic Migration of Virtual Machines in Cloud Computing

Cloud computing is getting more prevalent and finding a way to reduce the cost of cloud computing platform through the migration of virtual machines (VM) is a concerned issue. In this paper, the problem of dynamic migration of VMs (DM-VM) in the cloud computing platform (or simply the cloud) is investigated. A triple-objective optimization model for DM-VM is established, which takes energy consumption, communication between VMs, and migration cost into account under the situation that the platform works normally. The DM-VM problem is divided into two parts: (i) forming VMs into groups, and (ii) determining the best way to place the groups into certain physical nodes. A binary graph matching-based bucket-code learning algorithm (BGM-BLA) is designed for solving the DM-VM problem. In BGM-BLA, bucket-coding and learning is employed for finding the optimal solutions, and binary graph matching is used for evaluating the candidate solutions. The computational results demonstrate that the proposed BGM-BLA algorithm performs relatively well in terms of the Pareto sets obtained and computational time in comparison with two optimization algorithms, i.e., Non-dominated Sorting Genetic Algorithm (NSGA-II) and binary graph matching-based common-coding algorithm.

Three improved hybrid metaheuristic algorithms for engineering design optimization

This paper presents three hybrid metaheuristic algorithms that further improve the two hybrid differential evolution (DE) metaheuristic algorithms described in Liao [1]. The three improved algorithms are: (i) MDE-HJ, which is a modification of MA-MDE in Liao [1] by replacing the random walk with direction exploitation local search with the Hooke and Jeeves (HJ) method; (ii) MDE-IHS-HJ, which is constructed by adding the Hooke and Jeeves method to the original cooperative hybrid, i.e., MDE-IHS; and (iii) PSO-MDE-HJ, which is a variation of MDE-IHS-HJ by replacing improved harmony search (IHS) with particle search optimization (PSO). A comprehensive comparative study was carried out to compare the three improved hybrids with the three algorithms presented by Liao [1] in terms of average success rate, average function evaluations taken, average elapsed CPU time, and convergence profiles. A total of 18 problems, 4 more than those used in Liao [1], were selected from different engineering domains for testing. The test results indicate that all three new hybrids can achieve higher success rate in much less CPU time. Among these three hybrids, MDE-IHS-HJ is the best one in terms of success rate, better than the best hybrid in Liao [1] by over 15% and better than the second best, PSO-MDE-HJ, by nearly 10%.

Impact of Friction Stir Welding (FSW) Process Parameters on Thermal Modeling and Heat Generation of Aluminum Alloy Joints

Friction stir welding (FSW) is a solid-state joining process, where joint properties largely depend on the amount of heat generation during the welding process. The objective of this paper was to develop a numerical thermomechanical model for FSW of aluminum–copper alloy AA2219 and analyze heat generation during the welding process. The thermomechanical model has been developed utilizing ANSYS® APDL. The model was verified by comparing simulated temperature profile of three different weld schedules (i.e., different combinations of weld parameters in real weld situations) from simulation with experimental results. Furthermore, the verified model was used to analyze the effect of different weld parameters on heat generation. Among all the weld parameters, the effect of rotational speed on heat generation is the highest.

A Fully Coupled Thermomechanical Model of Friction Stir Welding (FSW) and Numerical Studies on Process Parameters of Lightweight Aluminum Alloy Joints

This paper presents a new thermomechanical model of friction stir welding which is capable of simulating the three major steps of friction stir welding (FSW) process, i.e., plunge, dwell, and travel stages. A rate-dependent Johnson–Cook constitutive model is chosen to capture elasto-plastic work deformations during FSW. Two different weld schedules (i.e., plunge rate, rotational speed, and weld speed) are validated by comparing simulated temperature profiles with experimental results. Based on this model, the influences of various welding parameters on temperatures and energy generation during the welding process are investigated. Numerical results show that maximum temperature in FSW process increases with the decrease in plunge rate, and the frictional energy increases almost linearly with respect to time for different rotational speeds. Furthermore, low rotational speeds cause inadequate temperature distribution due to low frictional and plastic dissipation energy which eventually results in weld defects. When both the weld speed and rotational speed are increased, the contribution of plastic dissipation energy increases significantly and improved weld quality can be expected.

Parallel machine scheduling in fuzzy environment with hybrid ant colony optimization including a comparison of fuzzy number ranking methods in consideration of spread of fuzziness

Display Omitted Evaluate 9 fuzzy ranking methods in consideration of spread of fuzziness.Develop the first hybrid ACO for fuzzy parallel machine scheduling incorporated with the best fuzzy ranking method found.Show that the proposed hybrid ACO can handle fuzzy as nonfuzzy cases.Show that the proposed hybrid ACO outperform DPSO, SA, and a hybrid of SA and TS. This paper studies parallel machine scheduling problems in consideration of real world uncertainty quantified based on fuzzy numbers. Although this study is not the first to study the subject problem, it advances this area of research in two areas: (1) Rather than arbitrarily picking a method, it chooses the most appropriate fuzzy number ranking method based on an in-depth investigation of the effect of spread of fuzziness on the performance of fuzzy ranking methods; (2) It develops the first hybrid ant colony optimization for fuzzy parallel machine scheduling. Randomly generated datasets are used to test the performance of fuzzy ranking methods as well as the proposed algorithm, i.e. hybrid ant colony optimization. The proposed hybrid ant colony optimization outperforms a hybrid particle swarm optimization published recently and two simulated annealing based algorithms modified from our previous work.

Stress and Springback Analyses of API X70 Pipeline Steel Under 3-Roller Bending via Finite Element Method

Abstract3-Roller bending is a widely applied manufacturing process, particularly in structural steel pipe industry. However, due to the difficulty and high cost of measuring stress distribution inside sheet material via traditional method, internal stress/strain response during forming is largely unexplored. The focuses of this study are two: (1) to map the radii of curvature as well as the stress inside the work piece during forming by utilizing the meshing mechanism of finite element method, and (2) to further provide some numeric guidelines for the configuration of the rolling system in order to improve production efficiency and product quality. The results of this study indicate that: (1) it is crucial to properly choose forming parameter in order to produce product with desired radii; (2) much like a gradual springback process, the radii of curvature gradually increase from the top roller to the exit-side bottom roller; (3) under the assumptions made in this study, to produce pipes with a specified diameter with varying configurations of the 3-roller system will not significantly change the final residual stress; and (4) finally, shifting of the neutral axis up to 2.0% of the thickness toward the compressing side during the forming process is observed.