Abstract
The magnitude optimum (MO) method provides a relatively fast and non-oscillatory closed-loop tracking response for a large class of process models frequently encountered in the process and chemical industries. However, the deficiency of the method is poor disturbance rejection performance of some processes. in this paper, disturbance rejection performance of the PID controller is improved by applying the "disturbance rejection magnitude optimum" (DRMO) optimisation method, while the tracking performance has been improved by a set-point weighting and set-point filtering PID controller structure. The DRMO tuning method requires numerical optimisation for the calculation of PID controller parameters. The method was applied to two different 2-degrees-of-freedom PID controllers and has been tested on several different representatives of process models and one laboratory set-up. A comparison with some other tuning methods has shown that the proposed tuning method, with a set-point filtering PID controller, is quite efficient in improving disturbance rejection performance, while retaining tracking performance comparable with the original MO method.

Abstract
This paper proposes a novel method for controlling the convergence rate of a particle swarm optimization algorithm using fractional calculus (FC) concepts. The optimization is tested for several well-known functions and the relationship between the fractional order velocity and the convergence of the algorithm is observed. The FC demonstrates a potential for interpreting evolution of the algorithm and to control its convergence.

Abstract
A genetic algorithm used to design radio-frequency binary-weighted differential switched capacitor arrays (RFDSCAs) is presented in this article. The algorithm provides a set of circuits all having the same maximum performance. Thus article also describes the design. implementation. and measurements results of a 0 25 mu m BiCMOS 3-bit RFDSCA. The experimented results show that the circuit presents the expected performance up to 40 GHz. The similarity between the evolutionary solutions, circuit simulations, and measured results indicates that the genetic synthesis method is a very useful tool for designing opitmum performance RFDSCAs (C) 2010 Wiley Periodicals, Inc Microwave Opt Technol Lett 52, 629-634, 2010. Published online in Wiley InterScience (www.interscience.wiley.com) DOI 10.1002/mop.25009

Abstract
Parallel manipulators have attracted the attention of researchers from different areas such as: high-precision robotics, machine-tools, simulators and haptic devices. The choice of a particular structural configuration and its dimensioning is a central issue to the performance of these manipulators. A solution to the dimensioning problem, normally involves the definition of performance criteria as part of an optimization process. In this paper the kinematic design of a 6-dof parallel robotic manipulator is analyzed. Three performance criteria are formulated and optimal solutions are found through a particle swarm formulation.

Abstract
This paper addresses the optimal involvement in derivatives electricity markets of a power producer to hedge against the pool price volatility. To achieve this aim, a swarm intelligence meta-heuristic optimization technique for long-term risk management tool is proposed. This tool investigates the long-term opportunities for risk hedging available for electric power producers through the use of contracts with physical (spot and forward contracts) and financial (options contracts) settlement. The producer risk preference is formulated as a utility function (U) expressing the trade-off between the expectation and the variance of the return. Variance of return and the expectation are based on a forecasted scenario interval determined by a long-term price range forecasting model. This model also makes use of particle swarm optimization (PSO) to find the best parameters allow to achieve better forecasting results. On the other hand, the price estimation depends on load forecasting. This work also presents a regressive long-term load forecast model that make use of PSO to find the best parameters as well as in price estimation. The PSO technique performance has been evaluated by comparison with a Genetic Algorithm (GA) based approach. A case study is presented and the results are discussed taking into account the real price and load historical data from mainland Spanish electricity market demonstrating the effectiveness of the methodology handling this type of problems. Finally, conclusions are dully drawn. Crown Copyright

Abstract
Recent developed button heat pulse probes (BHPP) demonstrated a great potential for soil water content measurements. This new probe compared to conventional heat pulse probes (HPP), does not use needles, and measurement accuracy is significantly improved. This new design, with the possibility to assembly the probe and electronics in the same package, with low-cost, and with less power consumption compared to conventional HPP, make it suitable to be connected to wireless data acquisition systems in precision agriculture. The probe was tested in agar to demonstrate the potential advantages of the button heat pulse sensor for soil water content measurements. It was possible to have an 0.5 °C temperature rise with only 156mW of power consumption, a ten times power reduction in heat-pulse soil water content measurements. These tests showed the potential use of the button heat pulse sensor for the determination of soil water content. © 2010 IEEE.