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
This paper presents a tool for automating the design of road tunnels lighting systems. The tunnel lighting system must guarantee some minimal luminance values in order to ensure a easy driving and visual perception. The lights distribution, in different tunnel zones, is obtained in the proposed technique by using a genetic algorithm. The developed software framework automatically selects the best light type and its localization, according to a specified design objective, along the tunnel independently of the light manufacturer. © 2009 IEEE.

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.