Research On Discrete Teaching-learning-based Optimization Algorithm And Its Application

Teaching-learning-based optimization(TLBO)algorithm is a kind of modern heuristic optimization algorithms based on swarm intelligence,and this algorithm has the advantages of without algorithm-specific parameters,rapid convergence and easy implementation yet effectiveness.Since its introduction in 2010,TLBO has aroused wide concern of domestic and foreign scholars.However,a theoretical analysis of convergence properties and dynamics of TLBO needs to be investigated.It is also necessary for the TLBO algorithm to be extended to the discrete variant to solve discrete optimization problems.This paper attempted to provide some discrete variants for extending TLBO to more application fields.The main contents are as follows:(1)An effective learner representation scheme is redefined based on the characteristics of Traveling Salesman problem(TSP).Moreover,all learners are randomly divided into several sub-swarms with equal amounts of learners so as to increase the diversity of population and reduce the probability of falling into local optimum.In each sub-swarm,the new trail learners in the teaching phase and the learning phase are generated by the crossover operation,the legality detection and mutation operation,and then the offspring learners are determined based on greedy selection.Finally,to verify the performance of the proposed algorithm,some benchmark TSP problems are examined and the experimental results indicate that DTLBO is effective compared with other algorithms used for TSP problems.(2)According to the community detection characteristics of the problem,the learner representation scheme is redefined,and the updating rules for learners are also redesigned.Considering the characteristics of complex network,a problem based population initialization method and a neighborhood search operator are proposed to maintain the diversity of the population.The experimental results on data sets in the real world show that the DTLBO algorithm is good and effective in community detection problems.(3)Based on the characteristics of Wireless Sensor Network(WSN)covering problems,a suitable encoding for WSN coverage problem is designed and circular neighborhood search mechanism is introduced into the discrete TLBO variant to maintain the diversity of population and reduce the probability of falling into local optimum.Moreover,in the proposed method,a new DTLBO variant determines which sensor nodes should be switched to sleeping modes and awakens some of the sensor nodes when sensing coverage declines so as to save network energy and prolong the life cycle of network.The simulation results show that the DTLBO algorithm is good and effective for the coverage problem of wireless sensor networks.