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WSN coverage optimization is an important problem. Considering that the artificial fish algorithm is easy to fall into local optimum and of slow convergence, an improved algorithm has been proposed in this paper. The chaos strategy is used to carry out the initialization of the foraging behavior, which makes the fish swarm evenly distributed in space, to avoid the randomness of the initialized individual. At the same time, the concept of diversity is introduced in the swarm behavior, which makes its avoidance of congestion further improved. Through standard test functions and simulated network coverage testing, the algorithm presented in this paper improves the WSN network node coverage, which effectively reduces the network cost and further improves the network coverage optimization.

The WSN Coverage Optimization of the Diversified AFSA Based on Chaos Learning Strategy