scholarly journals Determining Optimal Link Capacity Expansions in Road Networks Using Cuckoo Search Algorithm with Lévy Flights

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Ozgur Baskan
Keyword(s):  
Traffic Congestion ◽  
Road Network ◽  
Search Algorithm ◽  
Road Networks ◽  
Cuckoo Search ◽  
User Equilibrium ◽  
Network Design Problem ◽  
Lévy Flights ◽  
Link Capacity ◽  
Levy Flights

During the last two decades, Continuous Network Design Problem (CNDP) has received much more attention because of increasing trend of traffic congestion in road networks. In the CNDP, the problem is to find optimal link capacity expansions by minimizing the sum of total travel time and investment cost of capacity expansions in a road network. Considering both increasing traffic congestion and limited budgets of local authorities, the CNDP deserves to receive more attention in order to use available budget economically and to mitigate traffic congestion. The CNDP can generally be formulated as bilevel programming model in which the upper level deals with finding optimal link capacity expansions, whereas at the lower level, User Equilibrium (UE) link flows are determined by Wardrop’s first principle. In this paper, cuckoo search (CS) algorithm with Lévy flights is introduced for finding optimal link capacity expansions because of its recent successful applications in solving such complex problems. CS is applied to the 16-link and Sioux Falls networks and compared with available methods in the literature. Results show the potential of CS for finding optimal or near optimal link capacity expansions in a given road network.

scholarly journals Cuckoo Search Algorithm with Lévy Flights for Global-Support Parametric Surface Approximation in Reverse Engineering

Symmetry ◽  
2018 ◽  
Vol 10 (3) ◽  
pp. 58 ◽  
Author(s):  
Andrés Iglesias ◽  
Akemi Gálvez ◽  
Patricia Suárez ◽  
Mikio Shinya ◽  
Norimasa Yoshida ◽  
...  
Keyword(s):  
Reverse Engineering ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Parametric Surface ◽  
Lévy Flights ◽  
Surface Approximation ◽  
Levy Flights

Improving Cuckoo Search Algorithm With Mittag-Leffler Distribution

2019 ◽  
Author(s):  
Jiamin Wei ◽  
YangQuan Chen ◽  
Yongguang Yu ◽  
Yuquan Chen
Keyword(s):  
Search Algorithm ◽  
Cuckoo Search ◽  
Search Space ◽  
Cuckoo Search Algorithm ◽  
Lévy Flights ◽  
Lévy Distribution ◽  
Levy Flights ◽  
Levy Distribution ◽  
Heavy Tailed ◽  
The Impact

Abstract Cuckoo search (CS), as one of the recent nature-inspired metaheuristic algorithms, has proved to be an efficient approach due to the combination of Lévy flights, local search capabilities and guaranteed global convergence. CS uses Lévy flights in global random walk to explore the search space. The Lévy step is taken from the Lévy distribution which is a heavy-tailed probability distribution. In this case, a fraction of large steps are generated, which plays an important role in enhancing search capability of CS. Besides, although many foragers and wandering animals have been shown to follow a Lévy distribution of steps, investigation into the impact of other different heavy-tailed probability distributions on CS is still insufficient up to now. Based on the above considerations, we are motivated to apply the well-known Mittag-Leffler distribution to the standard CS algorithm, and proposed an improved cuckoo search algorithm (CSML) in this paper, where a more efficient search is supposed to take place in the search space thanks to the long jumps. In order to verify the performance of CSML, experiments are carried out on a test suite of 20 benchmark functions. In terms of the observations and results analysis, CSML can be regarded as a new potentially promising algorithm for solving optimization problems.

scholarly journals Analysis of Traffic Evolution on Road Networks of a Roundabout

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Tolesa Hundesa Muleta ◽  
Legesse Lemecha Obsu
Keyword(s):  
Traffic Congestion ◽  
Road Network ◽  
Road Networks ◽  
Point Of View ◽  
Traffic Dynamics ◽  
Demand And Supply ◽  
The Road ◽  
On The Road ◽  
Detailed Mathematical Analysis ◽  
Control Traffic

In this paper, the analyses of traffic evolution on the road network of a roundabout having three entrances and three exiting legs are conducted from macroscopic point of view. The road networks of roundabouts are modeled as a merging and diverging types 1×2 and 2×1 junctions. To study traffic evolution at junction, two cases have been considered, namely, demand and supply limited cases. In each case, detailed mathematical analysis and numerical tests have been presented. The analysis in the case of demand limited showed that rarefaction wave fills the portion of the road network in time. In the contrary, in supply limited case, traffic congestion occurs at merging junctions and shock wave propagating back results in reducing the performance of a roundabout to control traffic dynamics. Also, we illustrate density and flux profiles versus space discretization at different time steps via numerical simulation with the help of Godunov scheme.

scholarly journals α-Probabilistic flexible aggregate nearest neighbor search in road networks using landmark multidimensional scaling

2020 ◽  
Author(s):  
Moonyoung Chung ◽  
Woong-Kee Loh
Keyword(s):  
Multidimensional Scaling ◽  
Shortest Path ◽  
Road Network ◽  
Euclidean Distance ◽  
Nearest Neighbor ◽  
Search Algorithm ◽  
Road Networks ◽  
Path Distance ◽  
Aggregate Nearest Neighbor ◽  
Shortest Path Distance

AbstractIn spatial database and road network applications, the search for the nearest neighbor (NN) from a given query object q is the most fundamental and important problem. Aggregate nearest neighbor (ANN) search is an extension of the NN search with a set of query objects $$Q = \{ q_0, \dots , q_{M-1} \}$$ Q = { q 0 , ⋯ , q M - 1 } and finds the object $$p^*$$ p ∗ that minimizes $$g \{ d(p^*, q_i), q_i \in Q \}$$ g { d ( p ∗ , q i ) , q i ∈ Q } , where g (max or sum) is an aggregate function and d() is a distance function between two objects. Flexible aggregate nearest neighbor (FANN) search is an extension of the ANN search with the introduction of a flexibility factor $$\phi \, (0 < \phi \le 1)$$ ϕ ( 0 < ϕ ≤ 1 ) and finds the object $$p^*$$ p ∗ and the set of query objects $$Q^*_\phi $$ Q ϕ ∗ that minimize $$g \{ d(p^*, q_i), q_i \in Q^*_\phi \}$$ g { d ( p ∗ , q i ) , q i ∈ Q ϕ ∗ } , where $$Q^*_\phi $$ Q ϕ ∗ can be any subset of Q of size $$\phi |Q|$$ ϕ | Q | . This study proposes an efficient $$\alpha $$ α -probabilistic FANN search algorithm in road networks. The state-of-the-art FANN search algorithm in road networks, which is known as IER-$$k\hbox {NN}$$ k NN , used the Euclidean distance based on the two-dimensional coordinates of objects when choosing an R-tree node that most potentially contains $$p^*$$ p ∗ . However, since the Euclidean distance is significantly different from the actual shortest-path distance between objects, IER-$$k\hbox {NN}$$ k NN looks up many unnecessary nodes, thereby incurring many calculations of ‘expensive’ shortest-path distances and eventually performance degradation. The proposed algorithm transforms road network objects into k-dimensional Euclidean space objects while preserving the distances between them as much as possible using landmark multidimensional scaling (LMDS). Since the Euclidean distance after LMDS transformation is very close to the shortest-path distance, the lookup of unnecessary R-tree nodes and the calculation of expensive shortest-path distances are reduced significantly, thereby greatly improving the search performance. As a result of performance comparison experiments conducted for various real road networks and parameters, the proposed algorithm always achieved higher performance than IER-$$k\hbox {NN}$$ k NN ; the performance (execution time) of the proposed algorithm was improved by up to 10.87 times without loss of accuracy.

Sign in / Sign up

Export Citation Format

Share Document