A constrained multi-objective biogeography-based optimization algorithm （CMBOA） was proposed to solve robot path planning （RPP）. For RPP, the length and smoothness of path were taken as the optimization objectives...A constrained multi-objective biogeography-based optimization algorithm （CMBOA） was proposed to solve robot path planning （RPP）. For RPP, the length and smoothness of path were taken as the optimization objectives, and the distance from the obstacles was constraint. In CMBOA, a new migration operator with disturbance factor was designed and applied to the feasible population to generate many more non-dominated feasible individuals; meanwhile, some infeasible individuals nearby feasible region were recombined with the nearest feasible ones to approach the feasibility. Compared with classical multi-objective evolutionary algorithms, the current study indicates that CM- BOA has better performance for RPP.展开更多
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
The recent development of phasor measurement technique opens the way for real-time post-disturbance transient stability assessment(TSA).Following a disturbance,since the transient instability can occur very fast,there...The recent development of phasor measurement technique opens the way for real-time post-disturbance transient stability assessment(TSA).Following a disturbance,since the transient instability can occur very fast,there is an urgent need for fast TSA with sufficient accuracy.This paper first identifies the tradeoff relationship between the accuracy and speed in post-disturbance TSA,and then proposes an optimal self-adaptive TSA method to optimally balance such tradeoff.It uses ensemble learning and credible decision-making rule to progressively predict the post-disturbance transient stability status,and models a multi-objective optimization problem to search for the optimal balance between TSA accuracy and speed.With such optimally balanced TSA performance,the TSA decision can be made as fast as possible while maintaining an acceptable level of accuracy.The proposed method is tested on New England 10-machine 39-bus system,and the simulation results verify its high efficacy.展开更多
基金Supported by the National Natural Science Foundation of Chi- na（61075113） the Excellent Young Teacher Foundation of Heilongjiang Province of China （1155G18） the Fundamental Research Funds for the Central Universities （HEUCFZl209）
文摘A constrained multi-objective biogeography-based optimization algorithm （CMBOA） was proposed to solve robot path planning （RPP）. For RPP, the length and smoothness of path were taken as the optimization objectives, and the distance from the obstacles was constraint. In CMBOA, a new migration operator with disturbance factor was designed and applied to the feasible population to generate many more non-dominated feasible individuals; meanwhile, some infeasible individuals nearby feasible region were recombined with the nearest feasible ones to approach the feasibility. Compared with classical multi-objective evolutionary algorithms, the current study indicates that CM- BOA has better performance for RPP.
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.
文摘The recent development of phasor measurement technique opens the way for real-time post-disturbance transient stability assessment(TSA).Following a disturbance,since the transient instability can occur very fast,there is an urgent need for fast TSA with sufficient accuracy.This paper first identifies the tradeoff relationship between the accuracy and speed in post-disturbance TSA,and then proposes an optimal self-adaptive TSA method to optimally balance such tradeoff.It uses ensemble learning and credible decision-making rule to progressively predict the post-disturbance transient stability status,and models a multi-objective optimization problem to search for the optimal balance between TSA accuracy and speed.With such optimally balanced TSA performance,the TSA decision can be made as fast as possible while maintaining an acceptable level of accuracy.The proposed method is tested on New England 10-machine 39-bus system,and the simulation results verify its high efficacy.
