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Enormous work has been reported in literature to enhance the performance of metaheuristics by modifying their internal mechanisms via intervening their control equations. Usually, these population based techniques are initiated through random creation of individuals (tentative solutions) to preserve adequate diversity in population and then attempts have been made to maintain a better balance between exploration and exploitation of the problem search space. However, it would be much better if some strategy is employed that could divert tentative solutions toward the promising region. This can be possible if the algorithm has some mechanism to develop certain knowledge (super sense) about the quality of decision variables of the problem. This paper presents super sense genetic algorithm (SSGA) that gradually develops super sense during successive genetic evolutions. The accumulated genetic information so obtained is stored and used to divert individuals near the promising region while preserving adequate diversity. SSGA differs to standard genetic algorithm (GA) only on this aspect. SSGA is applied to solve complex combinatorial network reconfiguration problem of radial distribution systems. The application results highlight the effectiveness of proposed GA.

Network Reconfiguration of Radial Active Distribution Systems in Uncertain Environment Using Super Sense Genetic Algorithm