Abstract
A novel variant of a multi-objective particle swarm optimization algorithm is reported. The proposed multi-objective particle swarm optimization algorithm is based on the maximin technique previously proposed for a multi-objective genetic algorithm. The technique is applied to optimize two types of problems: firth to a set of benchmark functions and second to the design of PID controllers regarding the classical design objectives of set-point tracking and output disturbance rejection.

Abstract
This paper proposes a multi-objective genetic algorithm to optimize a manipulator trajectory. The planner has several objectives namely the minimization of the space and join arm displacements and the energy required in the trajectory, without colliding with any obstacles in the workspace. Simulations results are presented for robots with two and three degrees of freedom, considering the optimization of two and three objectives.

Abstract
The particle swarm optimization algorithm is proposed as a tool to solve the Posicast input command shaping problem. The design technique is addressed, in the context of a simulation teaching experiment, aiming to illustrate second-order system feedforward control. The selected experiment is the well known suspended load or gantry problem, relevant to the crane control. Preliminary simulation results for a quarter-cycle Posicast shaper, designed with the particle swarm algorithm are presented. Illustrating figures extracted from an animation of a gantry example which validate the Posicast design are presented.

Abstract
This paper presents a genetic algorithm to optimize uni-objective problems with an infinite number of optimal solutions. The algorithm uses the maximin concept and epsilon-dominance to promote diversity over the admissible space. The proposed algorithm is tested with two well-known functions. The practical results of the algorithm are in good agreement with the optimal solutions of these functions. Moreover, the proposed optimization method is also applied in two practical real-world engineering optimization problems, namely, in radio frequency circuit design and in kinematic optimization of a parallel robot. In all the cases, the algorithm draws a set of optimal solutions. This means that each problem can be solved in several different ways, all with the same maximum performance.

Abstract
Modeling discrete event systems with sequential behavior can be a very hard and complex task. Some formalisms are used in this context, such as: Petri Nets, Statecharts, Finite automata, Grafcet and others. Among these, Grafcet seems to be a good choice because it is easy: to learn, to understand and to use. Teaching Grafcet is then relevant within engineering courses concerned with Industrial Automation. A virtual laboratory, e-GRAFCET, developed and first tested in UTAD University; it is a new, easy-to-use multimedia e-educational tool to support the self-learning process of Grafcet. This paper, reports a study of e-GRAFCET use by the students of University of Minho. A questionnaire was prepared and students asked to fulfill it in a volunteer basis. The results were statistically analyzed and the scores compared. The overall objective is to understand how the tool helps students in their study, and consequently improve their learning off Grafcet, independently of their engineering background.

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.