Neighborhood Strategies for QPSO Algorithms to Solve Benchmark Electromagnetic Problems

Several neighborhood strategies for QPSO algorithms are proposed and analyzed in order to improve the performances of the original methods. The proposed strategies are applied to some of the most well known QPSO algorithms such as the QPSO with random mean, the QPSO with Gaussian attractor and of course the basic QPSO. To prevent the premature convergence and to avoid being trapped in local minima the neighborhoods are dynamically changed during the optimization process. For testing the efficiency of the neighborhood techniques two benchmark optimization problems from the electromagnetic field computation have been chosen, Loney’s solenoid and TEAM22.

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