An experimental study of adaptive control for evolutionary algorithms

In this paper, we investigate how adaptive operator selection techniques are able to efficiently manage the balance between exploration and exploitation in an evolutionary algorithm, when solving combinatorial optimization problems. We introduce new high level reactive search strategies based on a generic algorithm's controller that is able to schedule the basic variation operators of the evolutionary algorithm, according to the observed state of the search. Our experiments on SAT instances show that reactive search strategies improve the performance of the solving algorithm.     

Evolutionary collective behavior decomposition model for time series data mining

In this research, we propose a novel framework referred to as collective game behavior decomposition where complex collective behavior is assumed to be generated by aggregation of several groups of agents following different strategies and complexity emerges from collaboration and competition of individuals. The strategy of an agent is modeled by certain simple game theory models with limited information. Genetic algorithms are used to obtain the optimal collective behavior decomposition based on history data. The trained model can be used for collective behavior prediction. For modeling individual behavior, two simple games, the minority game and mixed game are investigated in experiments on the real-world stock prices and foreign-exchange rate. Experimental results are presented to show the effectiveness of the new proposed model.     

Lagrangian relaxation hybrid with evolutionary algorithm for short-term generation scheduling

Short-term generation scheduling is an important function in daily operational planning of power systems. It is defined as optimal scheduling of power generators over a scheduling period while respecting various generator constraints and system constraints. Objective of the problem includes costs associated with energy production, start-up cost and shut-down cost along with profits. The resulting problem is a large scale nonlinear mixed-integer optimization problem for which there is no exact solution technique available. The solution to the problem can be obtained only by complete enumeration, often at the cost of a prohibitively computation time requirement for realistic power systems. This paper presents a hybrid algorithm which combines Lagrangian Relaxation (LR) together with Evolutionary Algorithm (EA) to solve the problem in cooperative and competitive energy environments. Simulation studies were carried out on different systems containing various numbers of units. The outcomes from different algorithms are compared with that from the proposed hybrid algorithm and the advantages of the proposed algorithm are briefly discussed.     

Covariance matrix adaptation evolution strategy based design of fixed structure robust H∞ loop shaping controller

This paper proposes the application of Covariance Matrix Adaptation Evolution Strategy (CMA-ES) in fixed structure H_∞ loop shaping controller design. Integral Time Absolute Error (ITAE) performance requirement is incorporated as a constraint with an objective of maximization of stability margin in the fixed structure H_∞ loop shaping controller design problem. Pneumatic servo system, separating tower process and F18 fighter aircraft system are considered as test systems. The CMA-ES designed fixed structure H_∞ loop-shaping controller is compared with the traditional H_∞ loop shaping controller, non-smooth optimization and Heuristic Kalman Algorithm (HKA) based fixed structure H_∞ loop shaping controllers in terms of stability margin. 20% perturbation in the nominal plant is used to validate the robustness of the CMA-ES designed H_∞ loop shaping controller. The effect of Finite Word Length (FWL) is considered to show the implementation difficulties of controller in digital processors. Simulation results demonstrated that CMA-ES based fixed structure H_∞ loop shaping controller is suitable for real time implementation with good robust stability and performance.     

Quantum computation based bundling optimization for combinatorial auction in freight service procurements

Combinatorial auction is a useful trade manner for transportation service procurements in e-marketplaces. To enhance the competition of combinatorial auction, a novel auction mechanism of two-round bidding with bundling optimization is proposed. As the recommended the auction mechanism, the shipper/auctioneer integrates the objects into several bundles based on the bidding results of first round auction. Then, carriers/bidders bid for the object bundles in second round. The bundling optimization is described as a multi-objective model with two criteria on price complementation and combination consistency. A Quantum Evolutionary Algorithm (QEA) with β-based rotation gate and the encoding scheme based on non-zero elements in complementary coefficient matrix is developed for the model solution. Comparing with a Contrast Genetic Algorithm, QEA can achieve better computational performances for small and middle size problems.     

The day of the week when you were born in 700 ms: Calendar computation in an autistic savant.

Some individuals are able to determine the weekday of a given date in a few seconds (finding for instance that June 12, 1900, was a Tuesday). This ability has fascinated scientists for many years because it is predominantly observed in people with limited intelligence and may appear very early in life. Exceptional visual memory, exceptional concentration abilities, or privileged access to lower levels of information not normally available through introspection have been advanced to explain such phenomena. In the present article, the authors show that a simple cognitive model can explain all aspects of the performance of Donny, a young autistic savant who is possibly the fastest and most accurate calendar prodigy ever described. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

基于可靠性评价的含缺陷核压力管道疲劳寿命预测方法

含缺陷压力管道经缺陷评定合乎使用后,其疲劳寿命的估计具有重要的工程意义。将整个含缺陷压力管道作为一个整体,分析了含缺陷压力管道的疲劳裂纹扩展特点,提出了相应的含缺陷压力管道疲劳寿命的计算过程,并在基于可靠性评价的基础上,给出了核压力管道的可接受失效概率,最终得到了含缺陷核压力管道疲劳寿命预测方法。     

Implementing Qubits with Superconducting Integrated Circuits

Superconducting qubits are solid state electrical circuits fabricated using techniques borrowed from conventional integrated circuits. They are based on the Josephson tunnel junction, the only non-dissipative, strongly non-linear circuit element available at low temperature. In contrast to microscopic entities such as spins or atoms, they tend to be well coupled to other circuits, which make them appealling from the point of view of readout and gate implementation. Very recently, new designs of superconducting qubits based on multi-junction circuits have solved the problem of isolation from unwanted extrinsic electromagnetic perturbations. We discuss in this review how qubit decoherence is affected by the intrinsic noise of the junction and what can be done to improve it. PACS: 03.67.-a, 03.65.Yz, 85.25.-j, 85.35.Gv     

ㄇ形梁剪力滞效应的解析解

精确分析ㄇ形梁在纵横向荷载共同作用下,其横断面上正应力分布规律对于计算其有效宽度有重要意义,应用力法原理,先将ㄇ形梁和翼板截开成矩形截面梁和平面应力板,在截面上代之以赘余的分布剪力,对于平面应力板,通过利用板变形的对称性来简化其边界条件,然后假设一个满足板的控制方程的Airy应力函数求得板的应力和位移,再利用Timoshenko梁理论求得梁的挠度和转角,根据截面上梁与板的纵向位移相等的变形协调条件便可最终确定截面上的分布剪力,给出的数值算例验证了方法的有效性,并与铜陵长江公路大桥主梁的模型有限元结果和试验结果作了对比,解析解法还可用来检验其他各种数值计算方法的精度,并可推广到其他多跨薄壁结构梁桥的膜应力分析中。