A knowledge-based archive multi-objective simulated annealing algorithm to optimize series–parallel system with choice of redundancy strategies

Redundancy allocation problem (RAP) is one of the best-developed problems in reliability engineering studies. This problem follows to optimize the reliability of a system containing s sub-systems under different constraints, including cost, weight, and volume restrictions using redundant components for each sub-system. Various solving methodologies have been used to optimize this problem, including exact, heuristic, and meta-heuristic algorithms. In this paper, an efficient multi-objective meta-heuristic algorithm based on simulated annealing (SA) is developed to solve multi-objective RAP (MORAP). This algorithm is knowledge-based archive multi-objective simulated annealing (KBAMOSA). KBAMOSA applies a memory matrix to reinforce the neighborhood structure to achieve better quality solutions. The results analysis and comparisons demonstrate the performance of the proposed algorithm for solving MORAP.     

A modified particle swarm optimization with multiple subpopulations for multimodal function optimization problems

In this paper, a modified particle swarm optimization (PSO) algorithm is developed for solving multimodal function optimization problems. The difference between the proposed method and the general PSO is to split up the original single population into several subpopulations according to the order of particles. The best particle within each subpopulation is recorded and then applied into the velocity updating formula to replace the original global best particle in the whole population. To update all particles in each subpopulation, the modified velocity formula is utilized. Based on the idea of multiple subpopulations, for the multimodal function optimization the several optima including the global and local solutions may probably be found by these best particles separately. To show the efficiency of the proposed method, two kinds of function optimizations are provided, including a single modal function optimization and a complex multimodal function optimization. Simulation results will demonstrate the convergence behavior of particles by the number of iterations, and the global and local system solutions are solved by these best particles of subpopulations.     

Fundamental Evaluation of Power Supply and Rectifiers for Wireless Power Transfer Using Magnetic Resonant Coupling

This paper provides a fundamental analysis of a power supply and rectifiers for wireless power transfer using magnetic resonant coupling (MRC). MRC enables efficient wireless power transfer over middle‐range transfer distances. MRC for wireless power transfer should operate at a high frequency in the industry science medical band, such as 13.56 MHz, because the size of the transfer device decreases at higher transfer frequencies. Therefore, the output frequency of the power supply on the transmitting side should be 13.56 MHz. In addition, the rectifier on the receiving side is operated at a high frequency. This paper focuses on the reflected power in the power supply and rectifiers. Thus, the parametric design method is clarified for the power supply, including a low‐pass filter to match the output, the impedance of the power supply with the characteristic impedance of the transmission line. In addition, the effects on the rectifiers of silicon carbide and gallium nitride diodes are confirmed by performing an experiment and a loss analysis.     

Teaching and Learning Electrochemistry

Numerous conceptual difficulties and misconceptions have been reported in the science and chemistry education literature about electrochemistry concepts such as electrolytes, redox equations, and about electrochemical (galvanic and electrolytic) cells. Other studies have considered teaching approaches aimed at improving learning and at overcoming misconceptions. This paper reviews these studies and considers strategies and approaches for the effective teaching of electrochemistry. Then, the review focuses on problem solving in equilibrium electrochemistry, especially problems involving the Nernst equation. The main findings of four studies with Greek university chemistry students, dealing with: (i) algorithmic problem‐solving ability; (ii) practice on problem solving; (iii) the effect of the format of a problem; and (iv) the construction and evaluation of a website devoted to electrochemistry problem solving, are reported.     

Control theory for stochastic distributed parameter systems,an engineering perspective

The main purpose of this paper is to survey some recent progresses on control theory for stochastic distributed parameter systems, i.e., systems governed by stochastic differential equations in infinite dimensions, typically by stochastic partial differential equations. We will explain the new phenomenon and difficulties in the study of controllability and optimal control problems for one dimensional stochastic parabolic equations and stochastic hyperbolic equations. In particular, we shall see that both the formulation of corresponding stochastic control problems and the tools to solve them may differ considerably from their deterministic/finite-dimensional counterparts. More importantly, one has to develop new tools, say, the stochastic transposition method introduced in our previous works, to solve some problems in this field.     

An adaptive local search algorithm for vehicle routing problem with simultaneous and mixed pickups and deliveries

The Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD) is an extension to the classical Vehicle Routing Problem (VRP), where customers may both receive and send goods simultaneously. The Vehicle Routing Problem with Mixed Pickup and Delivery (VRPMPD) differs from the VRPSPD in that the customers may have either pickup or delivery demand. However, the solution approaches proposed for the VRPSPD can be directly applied to the VRPMPD. In this study, an adaptive local search solution approach is developed for both the VRPSPD and the VRPMPD, which hybridizes a Simulated Annealing inspired algorithm with Variable Neighborhood Descent. The algorithm uses an adaptive threshold function that makes the algorithm self-tuning. The proposed approach is tested on well-known VRPSPD and VRPMPD benchmark instances derived from the literature. The computational results indicate that the proposed algorithm is effective in solving the problems in reasonable computation time.     

Photopolymerization synthesis of polyacrylic acid dispersant with methoxysilicon end groups and its application in a nano‐SiO2 aqueous system

In order to improve the dispersity and stability of the nano‐SiO₂ aqueous system with high solid content, a kind of polyacrylic acid dispersant with methoxysilicon end groups (KH590‐PAA) was synthesized by photopolymerization of acrylic acid (AA) initiated with (3‐mercaptopropyl)trimethoxysilane (KH590). After adding KH590‐PAA into the nano‐SiO₂ aqueous dispersion system (20 wt% solid content), the viscosity and the curing time of the system were measured with a rotational viscometer and the inverted bottle method. Moreover, the dispersion mechanism of KH590‐PAA for the nano‐SiO₂ aqueous system was researched by measuring the adsorption capacity, the particle size and the zeta potential of the nanoparticles with a conductivity meter, dynamic light scattering, SEM and TEM, respectively. The results showed that the methoxysilicon groups in KH590‐PAA could react with hydroxyl groups on the surface of nano‐SiO₂ in the process of stirring, which enhanced the adsorption capacity of the dispersant and then increased the surface charge of the particles. Therefore, electrostatic repulsion and steric hindrance effects between the SiO₂ nanoparticles could be further enhanced by adding the KH590‐PAA dispersant, and then the nano‐SiO₂ aqueous system exhibited better dispersity and stability. Besides, the dispersion properties of SiO₂ nanoparticles in water were closely related to the addition amount and the molecular weight of the KH590‐PAA dispersant. © 2018 Society of Chemical Industry     

A globally convergent BFGS method for pseudo-monotone variational inequality problems

In this paper, we propose a globally convergent BFGS method to solve Variational Inequality Problems (VIPs). In fact, a globalization technique on the basis of the hyperplane projection method is applied to the BFGS method. The technique, which is independent of any merit function, is applicable for pseudo-monotone problems. The proposed method applies the BFGS direction and tries to reduce the distance of iterates to the solution set. This property, called Fejer monotonicity of iterates with respect to the solution set, is the basis of the convergence analysis. The method applied to pseudo-monotone VIP is globally convergent in the sense that subproblems always have unique solutions, and the sequence of iterates converges to a solution to the problem without any regularity assumption. Finally, some numerical simulations are included to evaluate the efficiency of the proposed algorithm.     

Multiple Empirical Kernel Learning with Majority Projection for imbalanced problems

Traditional Multiple Empirical Kernel Learning (MEKL) expands the expressions of the sample and brings better classification ability by using different empirical kernels to map the original data space into multiple kernel spaces. To make MEKL suit for the imbalanced problems, this paper introduces a weight matrix and a regularization term into MEKL. The weight matrix assigns high misclassification cost to the minority samples to balanced misclassification cost between minority and majority class. The regularization term named Majority Projection (MP) is used to make the classification hyperplane fit the distribution shape of majority samples and enlarge the between-class distance of minority and majority class. The contributions of this work are: (i) assigning high cost to minority samples to deal with imbalanced problems, (ii) introducing a new regularization term to concern the property of data distribution, (iii) and modifying the original PAC-Bayes bound to test the error upper bound of MEKL-MP. Through analyzing the experimental results, the proposed MEKL-MP is well suited to the imbalanced problems and has lower generalization risk in accordance with the value of PAC-Bayes bound.     

The generalized discrete ‐centroid problem

The ‐centroid problem or leader–follower problem is generalized considering different customer choice rules where a customer may use facilities belonging to different firms, if the difference in travel distance (or time) is small enough. Assuming essential goods, some particular customer choice rules are analyzed. Linear programming formulations for the generalized ‐medianoid and ‐centroid problems are presented and an exact solution approach is applied. Some computational examples are included.