An incremental particle swarm for large-scale continuous optimization problems: an example of tuning-in-the-loop (re)design of optimization algorithms

The development cycle of high-performance optimization algorithms requires the algorithm designer to make several design decisions. These decisions range from implementation details to the setting of parameter values for testing intermediate designs. Proper parameter setting can be crucial for the effective assessment of algorithmic components because a bad parameter setting can make a good algorithmic component perform poorly. This situation may lead the designer to discard promising components that just happened to be tested with bad parameter settings. Automatic parameter tuning techniques are being used by practitioners to obtain peak performance from already designed algorithms. However, automatic parameter tuning also plays a crucial role during the development cycle of optimization algorithms. In this paper, we present a case study of a tuning-in-the-loop approach for redesigning a particle swarm-based optimization algorithm for tackling large-scale continuous optimization problems. Rather than just presenting the final algorithm, we describe the whole redesign process. Finally, we study the scalability behavior of the final algorithm in the context of this special issue.     

Optimal design of power semiconductor devices

The interactive multicriterion optimization of power semiconductor devices is described. The technique is implemented in an integrated system of compatible program packages built up on the basis of the programming system PRIZ. Several problem-oriented languages are available for a designer of power semiconductor devices for interaction with the program packages.The experience gained from the exploitation of the system shows that such an approach to the multicriterion design of power semiconductor devices leads to a better understanding of the physical contents of the problem and a more precise coincidence with the optimal solution. Examples illustrating the considered method of optimal design are given.     

Interactive computer methods for design optimization

Two interactive methods for design optimization are discussed in this paper. The first method is implemented by the computer program GDOPT and is an essentially manual search procedure based on graphical searches of the design space within user-defined planes. In contrast, the computer program ADOPT consists of the more effective nonlinear programming techniques available and the search is mostly under computer control. The user observes progress on a graphical display and is able to modify the search procedure as desired. These two interactive methods make an extensive use of computer graphics to display search results in the form of graphs, contour plots, sensitivity curves, and search trajectories. The designer interacts with the programs through these displays and is an active participant in the decision making process. It is believed that this integrated approach to design optimization can result in an enhanced understanding of the design problem and an effective convergence to the optimum design. An example is presented which illustrates the effectiveness of this interactive technique.     

粒子群优化算法在Acrobot平衡控制中的应用

粒子群优化算法(PSO)是基于群体的演化算法,本质上是一种随机搜索算法,并能以较大概率收敛到全局最优。本文针对欠驱动Acrobot机械臂系统,利用模糊控制原理设计平衡控制器,运用粒子群算法对模糊控制器的量化因子进行在线优化,获得平衡控制器参数的最优值,以实现降低系统超调量,减少系统振荡和平衡时间的目的。仿真实验结果验证了该方法的有效性。     

Physical programming for preference driven evolutionary multi-objective optimization

Preference articulation in multi-objective optimization could be used to improve the pertinency of solutions in an approximated Pareto front. That is, computing the most interesting solutions from the designer's point of view in order to facilitate the Pareto front analysis and the selection of a design alternative. This articulation can be achieved in an a priori, progressive, or a posteriori manner. If it is used within an a priori frame, it could focus the optimization process toward the most promising areas of the Pareto front, saving computational resources and assuring a useful Pareto front approximation for the designer. In this work, a physical programming approach embedded in an evolutionary multi-objective optimization is presented as a tool for preference inclusion. The results presented and the algorithm developed validate the proposal as a potential tool for engineering design by means of evolutionary multi-objective optimization.     

Graph-based evolutionary design of arithmetic circuits

We present an efficient graph-based evolutionary optimization technique, called evolutionary graph generation (EGG), and the proposed approach is applied to the design of combinational and sequential arithmetic circuits based on parallel counter-tree architecture. The fundamental idea of EGG is to employ general circuit graphs as individuals and manipulate the circuit graphs directly using new evolutionary graph operations without encoding the graphs into other indirect representations, such as the bit strings used in genetic algorithm (GA) proposed by Holland (1992) and trees used in genetic programming (GP) proposed by Koza et al. (1997). In this paper, the EGG system is applied to the design of constant-coefficient multipliers and the design of bit-serial data-parallel adders. The results demonstrate the potential capability of EGG to solve the practical design problems for arithmetic circuits with limited knowledge of computer arithmetic algorithms. The proposed EGG system can help to simplify and speed up the process of designing arithmetic circuits and can produce better solutions to the given problem     

磁耦合谐振式无线传能系统输出电压GSSA-MPC控制

针对磁耦合谐振式(MCR)无线传能系统中负载变化引起输出电压实时调节的需求,以全桥LCCL系统为研究对象,提出了一种基于状态方程的模型预测控制器(GGSA-MPC).通过广义状态空间平均法(GSSA)对全桥LCCL系统建立等效电路方程并将原副边电压电流进行傅里叶变换,建立了 MCR系统的动态状态空间模型;为了加快求解速...     

Optimum design of fractional order PID controller for AVR system using chaotic ant swarm

Fractional-order PID (FOPID) controller is a generalization of standard PID controller using fractional calculus. Compared to PID controller, the tuning of FOPID is more complex and remains a challenge problem. This paper focuses on the design of FOPID controller using chaotic ant swarm (CAS) optimization method. The tuning of FOPID controller is formulated as a nonlinear optimization problem, in which the objective function is composed of overshoot, steady-state error, raising time and settling time. CAS algorithm, a newly developed evolutionary algorithm inspired by the chaotic behavior of individual ant and the self-organization of ant swarm, is used as the optimizer to search the best parameters of FOPID controller. The designed CAS-FOPID controller is applied to an automatic regulator voltage (AVR) system. Numerous numerical simulations and comparisons with other FOPID/PID controllers show that the CAS-FOPID controller can not only ensure good control performance with respect to reference input but also improve the system robustness with respect to model uncertainties.     

Optimum design of nonlinear structures with path dependent reponse

Optimum design of structures with path dependent response is studied in this paper. The direct differentiation and the adjoint structure methods of design sensitivity analysis are summarized. The reference volume concept is used to unify the conventional and shape design problems. It is concluded that the direct differentiation method is more effective for this class of problems. The design sensitivity analysis — developed with continuum formulation — is discretized using the finite element method. Two cases for an example problem are optimized using a sequential quadratic programming algorithm to demonstrate how the developed procedures work and to study the optimization process for the problems with path dependent response.     

基于Pt100铂热电阻的测温电路设计

针对传统的铂热电阻测温方式存在测量结果受线路阻抗影响有误差、电路接线复杂的问题,设计了一种基于Pt100铂热电阻的测温电路;详细介绍了该电路的硬件设计及参数计算。该电路采用差分方式消除线路阻抗引起的测量偏差,并通过改变电路内参考电压的方式调节测温范围。仿真结果验证了该电路设计的合理性与可靠性。