The dung beetle optimizer(DBO)is a metaheuristic algorithm with fast convergence and powerful search capabilities,which has shown excellent performance in solving various optimization problems.However,it suffers from ...The dung beetle optimizer(DBO)is a metaheuristic algorithm with fast convergence and powerful search capabilities,which has shown excellent performance in solving various optimization problems.However,it suffers from the problems of easily falling into local optimal solutions and poor convergence accuracy when dealing with large-scale complex optimization problems.Therefore,we propose an adaptive DBO(ADBO)based on an elastic annealing mechanism to address these issues.First,the convergence factor is adjusted in a nonlinear decreasing manner to balance the requirements of global exploration and local exploitation,thus improving the convergence speed and search quality.Second,a greedy difference optimization strategy is introduced to increase population diversity,improve the global search capability,and avoid premature convergence.Finally,the elastic annealing mechanism is used to perturb the randomly selected individuals,helping the algorithm escape local optima and thereby improve solution quality and algorithm stability.The experimental results on the CEC 2017 and CEC 2022 benchmark function sets and MCNC benchmark circuits verify the effectiveness,superiority,and universality of ADBO.展开更多
基金Project supported by the National Natural Science Foundation of China(No.62102130)the Central Government Guides Local Science and Technology Development Fund Project of China(No.226Z0201G)+4 种基金the Natural Science Foundation of Hebei Province of China(Nos.F2020204003 and F2024204001)the Hebei Youth Talents Support Project of China(No.BJ2019008)the Science and Technology Research Projects of Higher Education Institutions in Hebei Province of China(No.QN2024138)the Basic Scientific Research Funds Research Project of Hebei Provincial Colleges and Universities of China(No.KY2022073)the Hebei Province Higher Education Institution Scientific Research Project of China(No.QN2025192)。
文摘The dung beetle optimizer(DBO)is a metaheuristic algorithm with fast convergence and powerful search capabilities,which has shown excellent performance in solving various optimization problems.However,it suffers from the problems of easily falling into local optimal solutions and poor convergence accuracy when dealing with large-scale complex optimization problems.Therefore,we propose an adaptive DBO(ADBO)based on an elastic annealing mechanism to address these issues.First,the convergence factor is adjusted in a nonlinear decreasing manner to balance the requirements of global exploration and local exploitation,thus improving the convergence speed and search quality.Second,a greedy difference optimization strategy is introduced to increase population diversity,improve the global search capability,and avoid premature convergence.Finally,the elastic annealing mechanism is used to perturb the randomly selected individuals,helping the algorithm escape local optima and thereby improve solution quality and algorithm stability.The experimental results on the CEC 2017 and CEC 2022 benchmark function sets and MCNC benchmark circuits verify the effectiveness,superiority,and universality of ADBO.
