Abstract: Aiming at the problems of rapid degradation of diversity,poor optimization accuracy,and susceptibility to local optima in the Red billed Blue Magpie Optimization Algorithm(RBMO),a hybrid strategy based adaptive Red billed Blue Magpie Optimization Algorithm(JRBMO) is proposed.Firstly,the Hammersley sequence is introduced to initialize the population,making the initial solution distribution more uniform and providing a foundation for optimization.Secondly,during the exploration phase,an adaptive spiral capture strategy is proposed to improve the search capability of RBMO by dynamically controlling the exploration range and direction of individuals.In the exploitation phase,the Levy flight strategy is introduced to locally perturb the current optimal solution and enhance the algorithm’s local development capability.Finally,an adaptive dimension mutation strategy is proposed to perform dimension mutation on individuals based on changes in population fitness distribution,avoiding the algorithm from getting stuck in local optima.The algorithm performance was evaluated on the CEC2017 and CEC2019 test sets,and the results showed that JRBMO had average win rates of 88.9% and 70%,respectively,verifying the effectiveness of JRBMO.In addition,applying JRBMO to the tension(compression) spring design problem and the three-dimensional wireless sensor network(WSN) node coverage problem,JRBMO achieves the optimal results,in which the WSN node mean coverage is 6.3% higher than that of the original algorithm,which demonstrates the universality of JRBMO in practical applications.

Key words: Red billed blue magpie optimization algorithm, Adaptive, Hammersley sequence, Spiral capture, Lévy flight, Dimension mutation