Ana Maria A. C. Rocha

Computational Science and Its Applications – ICCSA 2012

The neoclassical production function assumes that economic growth depends on exogenous factors of production centred on capital, labour and technology. However, residual variables, notably social capabilities and knowledge, are neglected. This study seeks to highlight that, in fact, they are key variables for understanding the economic growth and recent structural changes of an industrial cluster, both in technical and organizational terms. In this work, the peculiarity of knowledge and in particular of tacit knowledge form a crucial element in the social capabilities that are associated with enlarging knowledge learning processes and network diffusion. The aim of this research is to analyse the key role that knowledge and innovations play in the local wedding system of Bari in Puglia. They are the decisive factors in the survival of firms in a global market for the creation of competitive advantage and provide a basis for continuous innovation. The relationship between innovation and knowledge is discussed in the theoretical part of the paper, while the empirical aspect remains based upon results of consumer and producer surveys. The objective is to show how innovation, including demand-driven, can influence companies’ behaviours.

An augmented Lagrangian fish swarm based method for global optimization

This paper presents an augmented Lagrangian methodology with a stochastic population based algorithm for solving nonlinear constrained global optimization problems. The method approximately solves a sequence of simple bound global optimization subproblems using a fish swarm intelligent algorithm. A stochastic convergence analysis of the fish swarm iterative process is included. Numerical results with a benchmark set of problems are shown, including a comparison with other stochastic-type algorithms.

Hybridizing the electromagnetism-like algorithm with descent search for solving engineering design problems

In this paper, we present a new stochastic hybrid technique for constrained global optimization. It is a combination of the electromagnetism-like (EM) mechanism with a random local search, which is a derivative-free procedure with high ability of producing a descent direction. Since the original EM algorithm is specifically designed for solving bound constrained problems, the approach herein adopted for handling the inequality constraints of the problem relies on selective conditions that impose a sufficient reduction either in the constraints violation or in the objective function value, when comparing two points at a time. The hybrid EM method is tested on a set of benchmark engineering design problems and the numerical results demonstrate the effectiveness of the proposed approach. A comparison with results from other stochastic methods is also included.

Improved binary artificial fish swarm algorithm for the 0–1 multidimensional knapsack problems

The 0–1 multidimensional knapsack problem (MKP) arises in many fields of optimization and is NP-hard. Several exact as well as heuristic methods exist. Recently, an artificial fish swarm algorithm has been developed in continuous global optimization. The algorithm uses a population of points in space to represent the position of fish in the school. In this paper, a binary version of the artificial fish swarm algorithm is proposed for solving the 0–1 MKP. In the proposed method, a point is represented by a binary string of 0/1 bits. Each bit of a trial point is generated by copying the corresponding bit from the current point or from some other specified point, with equal probability. Occasionally, some randomly chosen bits of a selected point are changed from 0 to 1, or 1 to 0, with an user defined probability. The infeasible solutions are made feasible by a decoding algorithm. A simple heuristic add_item is implemented to each feasible point aiming to improve the quality of that solution. A periodic reinitialization of the population greatly improves the quality of the solutions obtained by the algorithm. The proposed method is tested on a set of benchmark instances and a comparison with other methods available in literature is shown. The comparison shows that the proposed method gives a competitive performance when solving this kind of problems.

Modified movement force vector in an electromagnetism-like mechanism for global optimization

This paper presents an algorithm for solving global optimization problems with bounded variables. The algorithm is a modification of the electromagnetism-like mechanism proposed by Birbil and Fang [An electromagnetism-like mechanism for global optimization, J. Global Optim. 25 (2003), pp. 263–282]. The differences are mainly on the local search procedure and on the force vector used to move each point in the population. Several widely-used benchmark problems were solved in a performance evaluation of the new algorithm when compared with the original one. A comparison with other stochastic methods is also included. The algorithm seems appropriate for large dimension problems.

Novel fish swarm heuristics for bound constrained global optimization problems

The heuristics herein presented are modified versions of the artificial fish swarm algorithm for global optimization. The new ideas aim to improve solution accuracy and reduce computational costs, in particular the number of function evaluations. The modifications also focus on special point movements, such as the random, search and the leap movements. A local search is applied to refine promising regions. An extension to bound constrained problems is also presented. To assess the performance of the two proposed heuristics, we use the performance profiles as proposed by Dolan and More in 2002. A comparison with three stochastic methods from the literature is included.

Head motion stabilization during quadruped robot locomotion: Combining dynamical systems and a genetic algorithm

The head shaking that results from robot locomotion is important because it difficults stable image acquisition and the possibility to rely on that information to act accordingly, for instance, to achieve visually-guided locomotion.

Study of vital sign monitoring with textile sensors in swimming pool environment

This paper presents the results of a series of experiments aiming at the optimisation of vital sign monitoring using textile electrodes to be used in a swimsuit. The swimsuit will integrate sensors for the measurement of several physiological and biomechanical signals; this paper will focus on ECG and respiratory movement analysis. The data obtained is mainly intended to provide tools for evaluation of high-performance swimmers, although applications can be derived for leisure sports and other situations. A comparison between electrodes based on different materials and structures, behaviour in dry and wet environments, as well as the behaviour in different extension states, will be presented. The influence of movement on the signal quality, both by the muscular electrical signals as well as by the displacement of the electrodes, will be discussed. The final objective is the integration of the electrodes in the swimsuit by knitting them directly in the suits fabric in a seamless knitting machine.

Feasibility and Dominance Rules in the Electromagnetism-Like Algorithm for Constrained Global Optimization

This paper presents the use of a constraint-handling technique, known as feasibility and dominance rules, in a electromagnetism-like (ELM) mechanism for solving constrained global optimization problems. Since the original ELM algorithm is specifically designed for solving bound constrained problems, only the inequality and equality constraints violation together with the objective function value are used to select points and to progress towards feasibility and optimality. Numerical experiments are presented, including a comparison with other methods recently reported in the literature.

A simplified binary artificial fish swarm algorithm for 0-1 quadratic knapsack problems

This paper proposes a simplified binary version of the artificial fish swarm algorithm (S-bAFSA) for solving 0-1 quadratic knapsack problems. This is a combinatorial optimization problem, which arises in many fields of optimization. In S-bAFSA, trial points are created by using crossover and mutation. In order to make the points feasible, a random heuristic drop_item procedure is used. The heuristic add_item is also implemented to improve the quality of the solutions, and a cyclic reinitialization of the population is carried out to avoid convergence to non-optimal solutions. To enhance the accuracy of the solution, a swap move heuristic search is applied on a predefined number of points. The method is tested on a set of benchmark 0-1 knapsack problems.