Ions motion algorithm for solving optimization problems

This paper proposes a novel optimization algorithm inspired by the ions motion in nature. In fact, the proposed algorithm mimics the attraction and repulsion of anions and cations to perform optimization. The proposed algorithm is designed in such a way to have the least tuning parameters, low computational complexity, fast convergence, and high local optima avoidance. The performance of this algorithm is benchmarked on 10 standard test functions and compared to four well-known algorithms in the literature. The results demonstrate that the proposed algorithm is able to show very competitive results and has merits in solving challenging optimization problems.     

A novel approach to adaptive relational association rule mining

The paper focuses on the adaptive relational association rule mining problem. Relational association rules represent a particular type of association rules which describe frequent relations that occur between the features characterizing the instances within a data set. We aim at re-mining an object set, previously mined, when the feature set characterizing the objects increases. An adaptive relational association rule method, based on the discovery of interesting relational association rules, is proposed. This method, called ARARM (Adaptive Relational Association Rule Mining) adapts the set of rules that was established by mining the data before the feature set changed, preserving the completeness. We aim to reach the result more efficiently than running the mining algorithm again from scratch on the feature-extended object set. Experiments testing the method's performance on several case studies are also reported. The obtained results highlight the efficiency of the ARARM method and confirm the potential of our proposal.     

Demand response for home energy management system

The optimization of energy consumption, with consequent cost reduction, is one of the main challenges for the present and future smart grid. Demand response (DR) program is expected to be vital in home energy management system (HEMS) which aims to schedule the operation of appliances to save energy costs by considering customer convenience as well as characteristics of electric appliances. The DR program is a challenging optimization problem especially when the formulations are non-convex or NP-hard problems. In order to solve this challenging optimization problem efficiently, an effective heuristic approach is proposed to achieve a near optimal solution with low computational costs. Different from previously proposed methods in literatures which are not suitable to be run in embedded devices such as a smart meter. The proposed algorithm can be implemented in an embedded device which has severe limitations on memory size and computational power, and can get an optimal value in real-time. Numerical studies were carried out with the data simulating practical scenarios are provided to demonstrate the effectiveness of the proposed method.     

Artificial bee colony based algorithm for maximum power point tracking (MPPT) for PV systems operating under partial shaded conditions

Artificial bee colony (ABC) algorithm has several characteristics that make it more attractive than other bio-inspired methods. Particularly, it is simple, it uses fewer control parameters and its convergence is independent of the initial conditions. In this paper, a novel artificial bee colony based maximum power point tracking algorithm (MPPT) is proposed. The developed algorithm, does not allow only overcoming the common drawback of the conventional MPPT methods, but it gives a simple and a robust MPPT scheme. A co-simulation methodology, combining Matlab/Simulink™ and Cadence/Pspice™, is used to verify the effectiveness of the proposed method and compare its performance, under dynamic weather conditions, with that of the Particle Swarm Optimization (PSO) based MPPT algorithm. Moreover, a laboratory setup has been realized and used to experimentally validate the proposed ABC-based MPPT algorithm. Simulation and experimental results have shown the satisfactory performance of the proposed approach.     

Collapse load of composite laminates: Lower bound evaluation by stress field analytical approximation

The paper proposes a limit analysis approach to define the ultimate load capacity of orthotropic composite laminates under biaxial loading and plane stress conditions. A lower bound to the collapse load multiplier is computed by solving a maximization nonlinear problem, according to the static theorem of limit analysis. To set up the optimization problem a stress field distribution is hypothesized at lamina level, moreover inter-lamina stresses are also considered. The effectiveness and validity of the proposed approach is shown by comparing the obtained numerical predictions both with available experimental data and with other numerical results carried out by means of a different numerical lower bound approach.     

A two stage heuristic algorithm for the integrated aircraft and crew schedule recovery problems

Airline disruptions incurred huge cost for airlines and serious inconvenience for travelers. In this paper, we study the integrated aircraft and crew schedule recovery problem. A two stage heuristic algorithm for the integrated recovery problem is proposed. In the first stage, the integrated aircraft recovery and flight-rescheduling model with partial crew consideration is built. This model is based on the traditional multi-commodity network model for the aircraft schedule recovery problem. The objective of this model also includes minimization of the original crew connection disruption. In the second stage, the integrated crew schedule recovery and flight re-scheduling model with partial aircraft consideration is built. We proposed a new multi-commodity model for the crew schedule recovery. The main advantage of such model is that it is much more efficient to integrate the flight-scheduling and aircraft consideration. New constraints are incorporated to guarantee that the aircraft connections generated in the stage 1 are still feasible. Two stages are run iteratively until no improvement can be achieved. Experimental results show that our method can provide better recovery solutions compared with the benchmark algorithms.     

开关磁阻直线电机的设计及其推力优化

设计了一种开关磁阻直线电机,使用JMAG软件对其进行了仿真和优化。进行了推力公式的推导,研究了导通顺序和初始位置对电机推力的影响,并分析了三种典型情况的磁力线分布。对电机的定子和动子的齿部和轭部高度、宽度及铁耗进行仿真优化,得出了最优参数。将有取向硅钢应用于该电机,研究了推力与轧制角的关系,并与使用普通硅钢的开关磁阻直线电机进行了对比,推力有一定提升。提出在动子齿部开切向槽的方案,结果表明,开切向槽对推力的影响较小,并能显著减小推力波动。     

Optimal filtering and control for wireless networked closed-loop control systems

This article studies the optimal filtering and control for wireless networked control systems (WNCSs). In WNCSs, packets may be lost in both control and feedback channels and user datagram protocol is usually used to improve the performance of the real-time control. Relevant literature indicates that the conventional optimal filtering for such a system cannot be applied in practice due to the complex calculation with Gaussian mixtures. This paper proposes a novel scheme to realize the optimal filtering and the linear quadratic Gaussian control for WNCSs, in which the controlled node performs a local estimation and the remote-control node performs the final estimation and control, and a synchronization of two estimators is guaranteed by a communication mechanism. An optimal filtering algorithm is developed, the stability condition of the filtering error covariance is obtained, optimal finite-horizon and infinite-horizon control are derived, and the stability of the closed-loop control system is proved. Numerical simulations show the validity and feasibility of the theoretical results.