By M. A. Abido (auth.), Bijaya Ketan Panigrahi, Ajith Abraham, Swagatam Das (eds.)
Computational Intelligence (CI) is likely one of the most crucial robust instruments for study within the various fields of engineering sciences starting from conventional fields of civil, mechanical engineering to great sections of electric, electronics and computing device engineering and particularly the organic and pharmaceutical sciences. the present box has its beginning within the functioning of the human mind in processing details, spotting trend, studying from observations and experiments, storing and retrieving info from reminiscence, and so on. particularly, the ability being at the verge of epoch altering because of deregulation, the ability engineers require Computational intelligence instruments for correct making plans, operation and keep an eye on of the ability approach. lots of the CI instruments are certainly formulated as a few kind of optimization or choice making difficulties. those CI strategies give you the strength utilities with leading edge ideas for effective research, optimum operation and regulate and clever determination making. This edited quantity offers with diversified CI thoughts for fixing actual international strength difficulties. The technical contents might be super precious for the researchers in addition to the training engineers within the energy industry.
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Not like conventional computing, Computational Intelligence is tolerant of vague details, partial fact and uncertainty. This e-book provides a particular choice of contributions on a targeted remedy of significant components of CI, focused on its key aspect: studying. the entire individuals of this quantity have direct bearing with this factor.
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Extra resources for Computational Intelligence in Power Engineering
With three objectives, the enclosing rectangle is equivalent to a cuboid in three dimensions. Based on this value, all the Pareto solutions are sorted. Low value of this metric represents that a solution is located in a crowded area and high value represents that a solution is in less crowded area. In this work, the diversity measure is used to trim the non-dominated solution set. We have fixed the maximum number of non-dominated solutions that will be preserved during iterations to 50. It was observed that a large number of nondominated solutions were being found during the middle stages of the algorithm 42 A.
The justification behind this choice has been presented in the section on pheromone reinforcement later in this section. 2 Initial Solution Generation The AIS-ACO hybrid algorithm proposed in this work for multi objective distribution network reconfiguration starts with an initial population of randomly generated network topologies. The algorithm used to generate this initial set of solutions has been derived from graph theory and is called Prim’s Algorithm . It is a very well known algorithm and uses a set of weights attached to the edges to generate a spanning tree.
On the other hand, Artificial Immune System uses only mutation within the feasible solution space as its primary search mechanism, so there is no need to design such an operator for it. This makes AIS the preferred choice over other multi-objective evolutionary algorithms. Further, using the pheromones for directing search processes in AIS-ACO hybrid brings about another advantage for the multi-objective distribution system reconfiguration problem. The information learned by pheromones while solving the reconfiguration problem can also be used for restoring the distribution network under contingencies.