Genetic Algorithm (GA) is a biologically inspired technique and widely used to solve numerous combinational optimization problems. It works on a population of individuals, not just one single solution. As a result, it...Genetic Algorithm (GA) is a biologically inspired technique and widely used to solve numerous combinational optimization problems. It works on a population of individuals, not just one single solution. As a result, it avoids converging to the local optimum. However, it takes too much CPU time in the late process of GA. On the other hand, in the late process Simulated Annealing (SA) converges faster than GA but it is easily trapped to local optimum. In this letter, a useful method that unifies GA and SA is introduced, which utilizes the advantage of the global search ability of GA and fast convergence of SA. The experimental results show that the proposed algorithm outperforms GA in terms of CPU time without degradation of performance. It also achieves highly comparable placement cost compared to the state-of-the-art results obtained by Versatile Place and Route (VPR) Tool.展开更多
As Public Transport(PT)becomes more dynamic and demand-responsive,it increasingly depends on predictions of transport demand.But how accurate need such predictions be for effective PT operation?We address this questio...As Public Transport(PT)becomes more dynamic and demand-responsive,it increasingly depends on predictions of transport demand.But how accurate need such predictions be for effective PT operation?We address this question through an experimental case study of PT trips in Metropolitan Copenhagen,Denmark,which we conduct independently of any specific prediction models.First,we simulate errors in demand prediction through unbiased noise distributions that vary considerably in shape.Using the noisy predictions,we then simulate and optimize demand-responsive PT fleets via a linear programming formulation and measure their performance.Our results suggest that the optimized performance is mainly affected by the skew of the noise distribution and the presence of infrequently large prediction errors.In particular,the optimized performance can improve under non-Gaussian vs.Gaussian noise.We also find that dynamic routing could reduce trip time by at least 23%vs.static routing.This reduction is estimated at 809,000€/year in terms of Value of Travel Time Savings for the case study.展开更多
基金Supported by School of Engineering, Napier University, United Kingdom, and partially supported by the National Natural Science Foundation of China (No.60273093).
文摘Genetic Algorithm (GA) is a biologically inspired technique and widely used to solve numerous combinational optimization problems. It works on a population of individuals, not just one single solution. As a result, it avoids converging to the local optimum. However, it takes too much CPU time in the late process of GA. On the other hand, in the late process Simulated Annealing (SA) converges faster than GA but it is easily trapped to local optimum. In this letter, a useful method that unifies GA and SA is introduced, which utilizes the advantage of the global search ability of GA and fast convergence of SA. The experimental results show that the proposed algorithm outperforms GA in terms of CPU time without degradation of performance. It also achieves highly comparable placement cost compared to the state-of-the-art results obtained by Versatile Place and Route (VPR) Tool.
文摘As Public Transport(PT)becomes more dynamic and demand-responsive,it increasingly depends on predictions of transport demand.But how accurate need such predictions be for effective PT operation?We address this question through an experimental case study of PT trips in Metropolitan Copenhagen,Denmark,which we conduct independently of any specific prediction models.First,we simulate errors in demand prediction through unbiased noise distributions that vary considerably in shape.Using the noisy predictions,we then simulate and optimize demand-responsive PT fleets via a linear programming formulation and measure their performance.Our results suggest that the optimized performance is mainly affected by the skew of the noise distribution and the presence of infrequently large prediction errors.In particular,the optimized performance can improve under non-Gaussian vs.Gaussian noise.We also find that dynamic routing could reduce trip time by at least 23%vs.static routing.This reduction is estimated at 809,000€/year in terms of Value of Travel Time Savings for the case study.
