Abstract
Distribution network reconfiguration is an important aspect of automation and optimization of distribution network system. To handle massive binary code infeasible solutions in distribution network reconfiguration, a kind of sequence code is presented in which a loop is a gene and the label of each switch in the loop is the gene value. To solve mutation probability and slow the convergence of clonal genetic algorithm (CGA) in the later stage, in this paper particle clonal genetic algorithm (PCGA) is proposed, in which we build particle swarm algorithm (PSO) mutation operator. PCGA avoids the premature convergence of PSO and the blindness of CGA. It ensures evolution direction and range based on historical records and swarm records. The global optimal solution can be obtained with fewer generations and shorter searching time. Compared with CGA and clonal genetic simulated annealing algorithm (CGSA), IEEE33 and IEEE69 examples show that PCGA can cut the calculation time and promote the search efficiency obviously.
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