Research On Parallel Firefly Algorithm And Its Application In WSN Coverage Optimization

In recent years,with the proposal of swarm intelligence optimization algorithm,optimization problems have been solved to a certain extent,which also promotes the development of many industrial problems.As one of swarm intelligence algorithms,Firefly Algorithm(FA)has been widely used in various optimization problems due to its advantages of simple model structure and good Algorithm performance.However,with the continuous exploration and analysis of researchers,FA still has problems such as easy to fall into local optimization,high time complexity in solving complex problems,and poor convergence performance of the algorithm.Therefore,this paper conducts the following studies on the defects of FA,and the main work is as follows:(1)In view of the poor convergence performance of the traditional FA,this paper adjusts its step factor a and proposes an update formula for ?,which makes it no longer a fixed constant,but a quantity decreasing gradually with the increase of the number of iterations.At the same time,aiming at the defects that FA is easy to fall into local optimal and the algorithm runs for a long time when solving large-scale complex problems,this paper proposes three different parallel methods.Dynamic Communication Parallel Firefly Algorithm(DCPFA)family algorithms based on Dynamic Communication strategy are proposed.(2)In view of the high time complexity of the algorithm,the population size of FA is compacted in this paper,and a compact idea based on FA is proposed,and this idea is introduced into the research of DCPFA series algorithms.Dynamic Communication Parallel Compact Firefly Algorithm(DCPCFA)family algorithms are proposed to further improve the overall performance of the algorithms.(3)The CEC2013 test function set is used to test the performance of the proposed DCPCFA algorithm,and the advantages of the proposed algorithm are verified.In addition,DCPCFA was analyzed according to the three types of functions in CEC2013 respectively,and the algorithm DCPCFA-B was found to be the most suitable algorithm for WSN coverage optimization among the three algorithms included in the DCPCFA,and this algorithm was applied to WSN coverage optimization.It can be seen from the test experiment of CEC2013 test function set that,compared with other comparison algorithms,in the test results of Unimodal Functions,DCPCFA-B and DCPCFAA obtained the optimal result after searching twice.Although DCPCFA-R did not perform as well as the other two DCPCFA algorithms,but compared with other comparison algorithms,they still have better searching ability.In the test results of Multimodal Functions,the optimal values of DCPCFA-B,DCPCFA-A and DCPCFA-R were found for 11 times,4 times and 1 time respectively.Among the Composition Functions,DCPCFA-B and DCPCFA-A find the optimal result 5 times and 3 times respectively in terms of the optimization accuracy.Then,DCPCFA-B with the best performance among the Multimodal Functions was selected and applied to the coverage optimization problem of WSN.The results of the two groups of simulation experiments show that compared with the optimized results after using PSO,FA,PFA and CFA,the optimized results after using DCPCFA-B are effectively improved.It can be seen that the optimization method proposed in this paper provides a feasible reference scheme for the follow-up study of FA.