Optimal multi-objective distribution system reconfiguration with multi criteria decision making-based solution ranking and enhanced genetic operators

On the impact of topological properties of smart grids in power losses optimization problems

“…To overcome this issue, McDermott et al [21] use a genetic algorithm providing a good compromise between computational burden and quality of the optimization result. More recently, novel meta-heuristic methods based on evolutionary optimization algorithms are introduced for the same context, showing good experimental results [19,20,25,26,29].…”

Multi-Objective Invasive Weed Optimization – An application to optimal network reconfiguration in radial distribution systems

“…Shuaib et al [26] implemented Gravitational Search Algorithm for minimization of loss reduction and to improve voltage deviation. Mazza et al [27] implemented multi-objective for reconfiguration which enhanced genetic operators to minimize network loss and energy not supplied. Barbosa et al [28] proposed an Interval Multi-Objective Evolutionary Algorithm to minimize power loss, average current index, average node voltage deviation and number of switching operations.…”

Restructuration of distribution power system using Genetics Algorithms

“…These algorithms permit the transition between local optima of the feasible region, as well as a more focused search in each subspace. Algorithms based on meta-heuristics, such as Genetic Algorithms [12][13][14][15][16], Simulated Annealing [17,18], Artificial Ant Colony [19] and Tabu Search [20,21], have been used to solve the problem of EDS reconfiguration. With the same purpose in mind, Ref.…”