Article citations

    A. Auger, J. Bader, D. Brockhoff, and E. Zitzler, “Theory of the hypervolume indicator: optimal μ-distributions and the choice of the reference point,” in Proceedings of the 10th ACM SIGEVO Workshop on Foundations of Genetic Algorithms (FOGA '09), pp. 87–102, January 2009.

has been cited by the following article:

  • TITLE: Multiobjective Optimization of Evacuation Routes in Stadium Using Superposed Potential Field Network Based ACO
  • AUTHORS: Jialiang Kou,Shengwu Xiong,Zhixiang Fang,Xinlu Zong,Zhong Chen
  • JOURNAL NAME: Computational Intelligence and Neuroscience DOI: 10.1155/2013/369016 Sep 16, 2014
  • ABSTRACT: Multiobjective evacuation routes optimization problem is defined to find out optimal evacuation routes for a group of evacuees under multiple evacuation objectives. For improving the evacuation efficiency, we abstracted the evacuation zone as a superposed potential field network (SPFN), and we presented SPFN-based ACO algorithm (SPFN-ACO) to solve this problem based on the proposed model. In Wuhan Sports Center case, we compared SPFN-ACO algorithm with HMERP-ACO algorithm and traditional ACO algorithm under three evacuation objectives, namely, total evacuation time, total evacuation route length, and cumulative congestion degree. The experimental results show that SPFN-ACO algorithm has a better performance while comparing with HMERP-ACO algorithm and traditional ACO algorithm for solving multi-objective evacuation routes optimization problem. 1. Introduction The evacuation planning in large-scale public area usually possesses two difficult points:(1)large scale: the large-scale public area has a complex flat structure. And it can hold thousands of people.(2)multisource and multisink: in evacuation process, the evacuees often start at different places in public area and run away from different exits. In a word, the evacuation planning in large-scale public area is a challenging problem. For solving this problem, researchers have put forward some effective methods. Shi et al. [1] used agent-based model to simulate and analyze evacuation process in large public building under fire conditions. Chen and Miller-Hooks [2] employed Benders decomposition to determine a set of evacuation routes and the assignment of evacuees to these routes for large building. Tayfur and Taaffe [3] utilized linear programming relaxation to model and solve a resource requirements and scheduling problem during hospital evacuations with the objective of minimizing cost within a prespecified evacuation completion time. Fang et al. [4] modeled evacuation process in a teaching building with multiexits, simulated it by cellular automata, and analyzed the multiexits choice phenomenon to find out the optimal exits choice combination for all evacuees. Usually, multiple macroscopic objectives are required to be considered in actual evacuation planning, and a set of nondominated plans are needed for decision making. Thus, evacuation planning problem could be transformed into multi-objective optimization problem. However, just a few researches, such as the literature [5–7], focused on that. Among these pieces of literature, the literature [7] successfully solved the multi-objective