An Improved Multi-objective Evolutionary Algorithm Based on Gaussian Distribution Estimation
When an emergency occurs,how to specify a reasonable resource scheduling scheme significantly affects disaster relief efficiency.However,most actual existing schemes lack considering satisfaction of potential disaster sites,and lack a scheduling model with 3 or more optimization goals,which makes it difficult to apply to complex scenarios.In this paper,we propose a four-objective resource scheduling optimization model that additionally considers potential disaster sites satisfaction.And we have designed an improved NSGA-III-GD algorithm to optimize this model.First,we introduce NSGA-III,an algorithm that has a great advantage in multi-objective optimization problems.And more importantly,we use Gaussian estimation distribution instead of traditional cross mutation operators to extract the overall characteristics of the population,which improves the search accuracy of the optimal solution and greatly improves the convergence speed.The experimental results clearly show that the algorithm proposed in this paper has achieved very good performance.
Potential disaster sites Resource scheduling Multi-objective optimization NSGA-Ⅲ-GD
Li-yang HOU Xiao-yong LI Yan-rong LI Wen-ping KONG Hai-feng CHANG
Key Laboratory of Trustworthy Distributed Computing and Service,Ministry of Education,Beijing Univer National Engineering Laboratory for Integrated Command and Dispatch Technology
国际会议
武汉
英文
189-198
2020-01-12（万方平台首次上网日期，不代表论文的发表时间）