Biogeography-based optimization （BBO） is a new evolutionary optimization method inspired by biogeography. In this paper, BBO is extended to a multi-objective optimization, and a biogeography-based multi-objective op...Biogeography-based optimization （BBO） is a new evolutionary optimization method inspired by biogeography. In this paper, BBO is extended to a multi-objective optimization, and a biogeography-based multi-objective optimization （BBMO） is introduced, which uses the cluster attribute of islands to naturally decompose the problem. The proposed algorithm makes use of nondominated sorting approach to improve the convergence ability efficiently. It also combines the crowding distance to guarantee the diversity of Pareto optimal solutions. We compare the BBMO with two representative state-of-the-art evolutionary multi-objective optimization methods, non-dominated sorting genetic algorithm-II （NSGA-II） and archive-based micro genetic algorithm （AMGA） in terms of three metrics. Simulation results indicate that in most cases, the proposed BBMO is able to find much better spread of solutions and converge faster to true Pareto optimal fronts than NSGA-II and AMGA do.展开更多
Biogeography-based optimization(BBO),a natureinspired optimization algorithm(NIOA),has exhibited a huge potential in optimization.In BBO,the good solutions have a large probability to share information with poor solut...Biogeography-based optimization(BBO),a natureinspired optimization algorithm(NIOA),has exhibited a huge potential in optimization.In BBO,the good solutions have a large probability to share information with poor solutions,while poor solutions have a large probability to accept the information from others.In original BBO,calculating for migration rates is based on solutions' ranking.From the ranking,it can be known that which solution is better and which one is worse.Based on the ranking,the migration rates are calculated to help BBO select good features and poor features.The differences among results can not be reflected,which will result in an improper migration rate calculating.Two new ways are proposed to calculate migration rates,which is helpful for BBO to obtain a suitable assignment of migration rates and furthermore affect algorithms ' performance.The ranking of solutions is no longer integers,but decimals.By employing the strategies,the ranking can not only reflect the orders of solutions,but also can reflect more details about solutions' distances.A set of benchmarks,which include 14 functions,is employed to compare the proposed approaches with other algorithms.The results demonstrate that the proposed approaches are feasible and effective to enhance BBO's performance.展开更多
In order to improve the global search ability of biogeography-based optimization(BBO)algorithm in multi-threshold image segmentation,a multi-threshold image segmentation based on improved BBO algorithm is proposed.Whe...In order to improve the global search ability of biogeography-based optimization(BBO)algorithm in multi-threshold image segmentation,a multi-threshold image segmentation based on improved BBO algorithm is proposed.When using BBO algorithm to optimize threshold,firstly,the elitist selection operator is used to retain the optimal set of solutions.Secondly,a migration strategy based on fusion of good solution and pending solution is introduced to reduce premature convergence and invalid migration of traditional migration operations.Thirdly,to reduce the blindness of traditional mutation operations,a mutation operation through binary computation is created.Then,it is applied to the multi-threshold image segmentation of two-dimensional cross entropy.Finally,this method is used to segment the typical image and compared with two-dimensional multi-threshold segmentation based on particle swarm optimization algorithm and the two-dimensional multi-threshold image segmentation based on standard BBO algorithm.The experimental results show that the method has good convergence stability,it can effectively shorten the time of iteration,and the optimization performance is better than the standard BBO algorithm.展开更多
基金supported by Zhejiang Provincial Natural Science Foundation of China (No.Y1090866)supported by Dan Simon and Dawei Du of Cleveland State University, and Jeff Abell of General Motors, whose ideas were instrumental in the development of this research
文摘Biogeography-based optimization （BBO） is a new evolutionary optimization method inspired by biogeography. In this paper, BBO is extended to a multi-objective optimization, and a biogeography-based multi-objective optimization （BBMO） is introduced, which uses the cluster attribute of islands to naturally decompose the problem. The proposed algorithm makes use of nondominated sorting approach to improve the convergence ability efficiently. It also combines the crowding distance to guarantee the diversity of Pareto optimal solutions. We compare the BBMO with two representative state-of-the-art evolutionary multi-objective optimization methods, non-dominated sorting genetic algorithm-II （NSGA-II） and archive-based micro genetic algorithm （AMGA） in terms of three metrics. Simulation results indicate that in most cases, the proposed BBMO is able to find much better spread of solutions and converge faster to true Pareto optimal fronts than NSGA-II and AMGA do.
基金National Natural Science Foundations of China(Nos.61503287,71371142,61203250)Program for Young Excellent Talents in Tongji University,China(No.2014KJ046)+1 种基金Program for New Century Excellent Talents in University of Ministry of Education of ChinaPh.D.Programs Foundation of Ministry of Education of China(No.20100072110038)
文摘Biogeography-based optimization(BBO),a natureinspired optimization algorithm(NIOA),has exhibited a huge potential in optimization.In BBO,the good solutions have a large probability to share information with poor solutions,while poor solutions have a large probability to accept the information from others.In original BBO,calculating for migration rates is based on solutions' ranking.From the ranking,it can be known that which solution is better and which one is worse.Based on the ranking,the migration rates are calculated to help BBO select good features and poor features.The differences among results can not be reflected,which will result in an improper migration rate calculating.Two new ways are proposed to calculate migration rates,which is helpful for BBO to obtain a suitable assignment of migration rates and furthermore affect algorithms ' performance.The ranking of solutions is no longer integers,but decimals.By employing the strategies,the ranking can not only reflect the orders of solutions,but also can reflect more details about solutions' distances.A set of benchmarks,which include 14 functions,is employed to compare the proposed approaches with other algorithms.The results demonstrate that the proposed approaches are feasible and effective to enhance BBO's performance.
基金Science and Technology Plan of Gansu Province(No.144NKCA040)
文摘In order to improve the global search ability of biogeography-based optimization(BBO)algorithm in multi-threshold image segmentation,a multi-threshold image segmentation based on improved BBO algorithm is proposed.When using BBO algorithm to optimize threshold,firstly,the elitist selection operator is used to retain the optimal set of solutions.Secondly,a migration strategy based on fusion of good solution and pending solution is introduced to reduce premature convergence and invalid migration of traditional migration operations.Thirdly,to reduce the blindness of traditional mutation operations,a mutation operation through binary computation is created.Then,it is applied to the multi-threshold image segmentation of two-dimensional cross entropy.Finally,this method is used to segment the typical image and compared with two-dimensional multi-threshold segmentation based on particle swarm optimization algorithm and the two-dimensional multi-threshold image segmentation based on standard BBO algorithm.The experimental results show that the method has good convergence stability,it can effectively shorten the time of iteration,and the optimization performance is better than the standard BBO algorithm.
