Abstract
This paper presents a new multicriteria decision aid system (DAS) to obtain acceptable power line paths integrating the diverse socioeconomic interests of the different groups involved in the planning process, such as utilities, environmental agents, or local and regional authorities. The DAS is based on the intensive use of geographic information systems, as well as multicriteria weighting techniques reflecting all group interests. This new DAS can be used to overcome the problems raised by initially opposing positions among different groups stemming from diverse technological, economic, environmental, and/or social interests. The technique is illustrated by an intensive simulation example from a case study reproducing some of the phases of a negotiation process.

Abstract
The paper describes how mapping a neural network into a rule-based fuzzy inference system leads to knowledge extraction. This mapping makes explicit the knowledge implicitly captured by the neural network during the learning stage, by transforming it into a set of rules. By applying the method to transformer fault diagnosis using dissolved gas-in-oil analysis, one could not only develop intelligent diagnosis systems, providing better results than the application of the IIEC 60599 Table, but also generate a new rule table whose application also leads to better diagnosis results.

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.