Abstract
This paper presents a new methodology for auto- mated route selection for the construction of new power lines, based on geographic information systems (GIS). It uses a dynamic programming model for route optimization. Environmental restrictions are taken into account together with all of the operating, maintenance, and equipment installation costs, including a new approach to the costs associated with the slope of the terrain crossed by the power lines. The computing and visual representation capacities of GIS are exploited for the selection of economic corridors, keeping the total costs under a threshold imposed by the user. Intensive simulation examples illustrate the power and flexibility of the proposed methodology.

Abstract
Decision problems cannot be fully represented without underlying assumptions about the Decision Maker motivations and behavior. This paper describes one technique to build a rule model representing the interaction of preferences of a Decision Maker, by training a Fuzzy Inference System based on examples. © 2005 ISAP.

Abstract
This paper presents a comparison in performance of 3 variants of Genetic Algorithms (GA) vs. 2 variants of Evolutionary Particle Swarm Optimization (EPSO), made in the extremely complex context of a multi-energy market simulation where the behavior of energy retailers is observed. The simulations are on JADE, a FIPA compliant platform based on intelligent autonomous agents running in a cluster of PCs. Each agent formulates its strategy by an inner complex simulation process using a meta-heuristic that tries to define optimum decisions. The results suggest that an EPSO approach is more efficient than GA. © 2005 ISAP.

Abstract
This text introduces a family of Evolutionary Algorithms named EPSO - Evolutionary Particle Swarm Optimization. EPSO algorithms are evolutionary methods that borrow the movement rule from Particle Swarm Optimization methods (PSO) and use it as a recombination operator that evolves under the pressure of selection. This hybrid approach builds up an algorithm that, in several cases, in application to complex problems in Power Systems, has already proven to be more efficient, accurate and robust than classical evolutionary methods or classical PSO. The text presents the description of the method, didactic examples and examples of applications in real world problems. © 2005 ISAP.

Abstract
Expansion planning of electric power or natural gas networks has become a consuming time engineering task due to the multiple factors that must be taken into account: technical, economic, environmental or social factors. This paper presents an advanced model of natural gas distribution networks based on Geographic Information Systems (GIS) methodologies, to evaluate the cost associated to the expansion of these networks in order to meet a demand imposed by the user in any location of a region. The experimental results show that this approach produces visual and useful information for planning the expansion of natural gas distribution networks.

Abstract
One major practical problem when applying traditional metaheuristics seems to be their strong dependency on parameter tuning. This issue is frequently pointed out as a major shortcoming of metaheuristics and is often a reason for Decision-Makers to reject using this type of approach in practical situations. In this paper we present a new search strategy - Constraint Oriented Neighbourhoods - that tries to overcome the referred drawback. The aim is to control the grade of randomness of metaheuristics, by defining "special" neighbourhood movements, that lead to a more robust heuristic, less dependent on parameter tuning. This is achieved by selecting and applying particular movements that take into account the potential violation of problem constraints. The strategy is illustrated in a real problem arising in the area of Power Systems Management - the Unit Commitment Problem, the computational experiments on a set of problem instances systematically outperforming those presented in the literature, both in terms of efficiency, quality of the solution and robustness of the algorithm.