When simple small world algorithm is adopted to optimize complex functions
the searching nodes are prone to be trapped in these local optimums. Aiming at the above
the following strategy is suggested: adding the tracking and replacing mechanism to every searching node; counting the stagnation times of a node at a certain station in its searching route; if the times oversteps the allowable value
a new node randomly arises in the solution space to replace the stagnating one for improving the searching efficiency. The improved method is tested via a few benchmark test functions in a simulation and the corresponding results show the efficiency for solving stagnation. Compared with the simple small world algorithm
the modified small world algorithm is endowed with better robustness and faster convergence to solve complex optimization problems.
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references
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