An improved harmony search（IHS） algorithm is proposed to solve the problem that given a set of target points and positions with different deployment costs finding minimum cost of potential positions to place sensor nodes fulfilling both multiple targets coverage and multiple nodes connectivity requirements. The proposed IHS algorithm introduced learning automata which has the ability to interact with environment to enhance the selfadaptive ability of parameters and improve optimization performance. Simulation results show that compared with the primitive harmony search algorithm and the proposed greedy algorithm

the proposed IHS algorithm achieves the goal of minimum deployment cost under the condition of ensuring multiple connectivity of nodes and multiple coverage of targets

which proves the effectiveness of the proposed IHS algorithm.