Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Using the ACO algorithm for path searches in social networks
Applied Intelligence
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Strategical planning is one of many research fields in the design of electrical distribution systems. The problem of strategical planning is a multiobjective combinatorial problem and the search space may often be quite large concerning to the options. The aim is to identify a strategy of expansion of a given distribution system in a given timeframe. For this problem, the search space is created beforehand by running a multiobjective optimisation algorithm for the optimal design of distribution networks for different load levels related to different years. The sets of Pareto-optimal solutions obtained for each load level at each year are equivalent in terms of the considered objectives, these being minimum losses, installation costs, and minimum unavailability. The problem of the identification of the optimal expansion strategy through these chronologically intermediate solutions leading to the final target configuration at the last year has been solved herein using an ACS (Ant Colony Search) algorithm. In order to verify the efficiency of the ACS algorithm, a small size application has been carried out and results have been compared to those obtained with enumeration. Then, a Simulated Annealing (SA) approach was used for a larger size test problem and results were compared to those obtained using the ACS. For this problem, the ACS demonstrated to be more robust than SA with higher quality results.