By M. A. Abido (auth.), Bijaya Ketan Panigrahi, Ajith Abraham, Swagatam Das (eds.)

Computational Intelligence (CI) is without doubt one of the most vital robust instruments for examine within the assorted fields of engineering sciences starting from conventional fields of civil, mechanical engineering to large sections of electric, electronics and computing device engineering and principally the organic and pharmaceutical sciences. the prevailing box has its beginning within the functioning of the human mind in processing details, spotting trend, studying from observations and experiments, storing and retrieving details from reminiscence, and so on. specifically, the facility being at the verge of epoch altering because of deregulation, the ability engineers require Computational intelligence instruments for correct making plans, operation and regulate of the facility procedure. lots of the CI instruments are definitely formulated as a few kind of optimization or determination making difficulties. those CI innovations give you the energy utilities with leading edge ideas for effective research, optimum operation and regulate and clever choice making. This edited quantity bargains with diverse CI concepts for fixing genuine global strength difficulties. The technical contents could be super necessary for the researchers in addition to the practising engineers within the strength industry.

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ACO has been successfully applied to a large number of combinatorial optimization problems, including traveling salesman problems [42-43]; vehicle routing problems [44], [45]; and quadratic assignment problems [46]. ACO also has been applied successfully to the scheduling problems, such as single machine problems [47]; flow shop problems [48]; and graph coloring problems [49]. The section ahead presents the generic mathematical model used for ACO. 1 Mathematical Model of ACO Let us consider that an ant has completed a partial sequence, that is, it has visited a set ξ of the nodes.

The Pareto Optimality framework described in this section has been used for solving multi-objective distribution system reconfiguration problem. The next section presents the multi-objective distribution system reconfiguration problem in detail. 3 Multi-objective Reconfiguration Problem Distribution Network Reconfiguration can be used to simultaneously optimize several objectives like minimization of real loss, load balancing on transformers and feeders. Moreover, the information gained from optimizing these objectives can be used for restoration during contingencies and can also be used for distribution planning and expansion purposes.

The graphical model for this small system is obtained by replacing substation buses with root nodes. The Figure 2. below shows the graphical model for this system. 1 3 2 16 11 22 8 17 24 21 4 13 18 12 19 9 15 5 10 14 20 11 23 13 12 14 6 7 26 Root Node Closed Edge 25 16 15 Node Open Edge Fig. 2. 2 Multi-objective Optimization Framework Multi-objective optimization is defined as the problem of finding a vector of decision variables that satisfies all the constraints and simultaneously optimizes a vector function whose elements represent the objective functions.

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