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We design a method to optimize the global mean first-passage time (GMFPT) ofmultiple random walkers searching in complex networks for a general target,without specifying the property of the target node. According to the Laplacetransformed formula of the GMFPT, we can equivalently minimize the overlapbetween the probability distribution of sites visited by the random walkers. Weemploy a mutation only genetic algorithm to solve this optimization problemusing a population of walkers with different starting positions and acorresponding mutation matrix to modify them. The numerical experiments on twokinds of random networks (WS and BA) show satisfactory results in selecting theorigins for the walkers to achieve minimum overlap. Our method thus providesguidance for setting up the search process by multiple random walkers oncomplex networks.

Selection of Random Walkers that Optimizes the Global Mean First-Passage Time for Search in Complex Networks