scholarly journals Genetic Algorithm for Biobjective Urban Transit Routing Problem

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
J. S. C. Chew ◽  
L. S. Lee ◽  
H. V. Seow
Keyword(s):  
Genetic Algorithm ◽  
Feasible Solution ◽  
Benchmark Problems ◽  
Computational Results ◽  
Crossover Operator ◽  
Urban Transit ◽  
Routing Problem ◽  
Genetic Algorithm Approach ◽  
Better Than

This paper considers solving a biobjective urban transit routing problem with a genetic algorithm approach. The objectives are to minimize the passengers’ and operators’ costs where the quality of the route sets is evaluated by a set of parameters. The proposed algorithm employs an adding-node procedure which helps in converting an infeasible solution to a feasible solution. A simple yet effective route crossover operator is proposed by utilizing a set of feasibility criteria to reduce the possibility of producing an infeasible network. The computational results from Mandl’s benchmark problems are compared with other published results in the literature and the computational experiments show that the proposed algorithm performs better than the previous best published results in most cases.

A GENETIC ALGORITHM FOR URBAN TRANSIT ROUTING PROBLEM

2012 ◽  
Vol 09 ◽  
pp. 411-421 ◽  
Author(s):  
JOANNE SUK CHUN CHEW ◽  
LAI SOON LEE
Keyword(s):  
Genetic Algorithm ◽  
Optimization Technique ◽  
Urban Transit ◽  
Routing Problem ◽  
Evolutionary Optimization Technique ◽  
Route Network ◽  
Constrained Problem ◽  
Transit Route ◽  
Better Than

The Urban Transit Routing Problem (UTRP) involves solving a set of transit route networks, which proved to be a highly complex multi-constrained problem. In this study, a bus route network to find an efficient network to meet customer demands given information on link travel times is considered. An evolutionary optimization technique, called Genetic Algorithm is proposed to solve the UTRP. The main objective is to minimize the passenger costs where the quality of the route sets is evaluated by a set of parameters. Initial computational experiments show that the proposed algorithm performs better than the benchmark results for Mandl's problems.

GASBOR: A GENETIC ALGORITHM APPROACH FOR SOLVING THE SWITCHBOX ROUTING PROBLEM

1996 ◽  
Vol 06 (04) ◽  
pp. 359-373 ◽  
Author(s):  
JENS LIENIG ◽  
K. THULASIRAMAN
Keyword(s):  
Genetic Algorithm ◽  
Integrated Circuits ◽  
Design Process ◽  
Three Dimensional ◽  
Genetic Operators ◽  
Dimensional Representation ◽  
Routing Problem ◽  
Three Dimensional Representation ◽  
Genetic Algorithm Approach ◽  
Better Than

A new genetic algorithm for switchbox routing in the physical design process of integrated circuits is presented. Our algorithm, called GASBOR (Genetic Algorithm for SwitchBOx Routing), is based on a three-dimensional representation of the switchbox and problem-specific genetic operators. The performance of the algorithm is tested on different benchmarks and it is shown that the results obtained using the proposed algorithm are either qualitatively similar to or better than the best published results.

scholarly journals A Heuristics-Based Parthenogenetic Algorithm for the VRP with Potential Demands and Time Windows

2016 ◽  
Vol 2016 ◽  
pp. 1-12
Author(s):  
Chenghua Shi ◽  
Tonglei Li ◽  
Yu Bai ◽  
Fei Zhao
Keyword(s):  
Genetic Algorithm ◽  
Time Windows ◽  
Computation Time ◽  
Routing Problem ◽  
Split Delivery ◽  
Soft Time Windows ◽  
Comparison Results ◽  
Potential Demand ◽  
The Cost

We present the vehicle routing problem with potential demands and time windows (VRP-PDTW), which is a variation of the classical VRP. A homogenous fleet of vehicles originated in a central depot serves customers with soft time windows and deliveries from/to their locations, and split delivery is considered. Also, besides the initial demand in the order contract, the potential demand caused by conformity consuming behavior is also integrated and modeled in our problem. The objective of minimizing the cost traveled by the vehicles and penalized cost due to violating time windows is then constructed. We propose a heuristics-based parthenogenetic algorithm (HPGA) for successfully solving optimal solutions to the problem, in which heuristics is introduced to generate the initial solution. Computational experiments are reported for instances and the proposed algorithm is compared with genetic algorithm (GA) and heuristics-based genetic algorithm (HGA) from the literature. The comparison results show that our algorithm is quite competitive by considering the quality of solutions and computation time.

scholarly journals Orthogonal Genetic Algorithm for Planar Thinned Array Designs

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Li Zhang ◽  
Yong-Chang Jiao ◽  
Bo Chen ◽  
Hong Li
Keyword(s):  
Genetic Algorithm ◽  
Factor Analysis ◽  
Orthogonal Array ◽  
Orthogonal Design ◽  
Crossover Operator ◽  
Sidelobe Level ◽  
Thinned Array ◽  
Better Than ◽  
Peak Sidelobe Level

An orthogonal genetic algorithm (OGA) is applied to optimize the planar thinned array with a minimum peak sidelobe level. The method is a genetic algorithm based on orthogonal design. A crossover operator formed by the orthogonal array and the factor analysis is employed to enhance the genetic algorithm for optimization. In order to evaluate the performance of the OGA, 20×10-element planar thinned arrays have been designed to minimize peak sidelobe level. The optimization results by the OGA are better than the previously published results.

Study on Hybrid Genetic Algorithm for Capacitated Vehicle Routing Problem

2012 ◽  
Vol 178-181 ◽  
pp. 1769-1772
Author(s):  
Chun Yu Ren
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Hybrid Genetic Algorithm ◽  
Search Efficiency ◽  
Crossover Operator ◽  
Real Numbers ◽  
Routing Problem ◽  
Capacitated Vehicle Routing Problem ◽  
Capacitated Vehicle

Capacitated vehicle routing problem is logistics optimization indispensable part. The hybrid genetic algorithm is used to optimize the solution. Firstly, use sequence of real numbers coding so as to simplify the problem; Construct the initial solution to improve the feasibility; adopt some arithmetic crossover operator to enhance whole search ability of the chromosome. Secondly, use Boltzmann simulated annealing mechanism to improve the convergence speed and search efficiency. Finally, comparing to other algorithms, the results demonstrate the effectiveness and good quality.

HYBRID-FITNESS FUNCTION EVOLUTIONARY ALGORITHM BASED ON SIMPLEX CROSSOVER AND PSO MUTATION FOR CONSTRAINED OPTIMIZATION PROBLEMS

2009 ◽  
Vol 23 (01) ◽  
pp. 115-127 ◽  
Author(s):  
YIBO HU
Keyword(s):  
Constrained Optimization ◽  
Evolutionary Algorithm ◽  
Penalty Function ◽  
Feasible Solution ◽  
Optimization Problems ◽  
Fitness Function ◽  
Benchmark Problems ◽  
Crossover Operator ◽  
Hybrid Fitness ◽  
Constrained Optimization Problems

For constrained optimization problems, evolutionary algorithms often utilize a penalty function to deal with constraints, even if it is difficult to control the penalty parameters. To overcome this shortcoming, this paper presents a new penalty function which has no parameter and can effectively handle constraint first, after which a hybrid-fitness function integrating this penalty function into the objective function is designed. The new fitness function can properly evaluate not only feasible solution, but also infeasible one, and distinguish any feasible one from an infeasible one. Meanwhile, a new crossover operator based on simplex crossover operator and a new PSO mutation operator are also proposed, which can produce high quality offspring. Based on these, a new evolutionary algorithm for constrained optimization problems is proposed. The simulations are made on ten widely used benchmark problems, and the results indicate the proposed algorithm is effective.

Fuzzy Based Parameter Adaptation in ACO for Solving VRP

2019 ◽  
Vol 10 (2) ◽  
pp. 65-81 ◽  
Author(s):  
Sandhya ◽  
Rajiv Goel
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Control Mechanism ◽  
Optimization Problems ◽  
Adaptive Strategies ◽  
Benchmark Problems ◽  
Parameter Control ◽  
Computational Results ◽  
Parameter Adaptation ◽  
Routing Problem

Ant Colony Optimization, a popular class of metaheuristics, have been widely applied for solving optimization problems like Vehicle Routing Problem. The performance of ACO is affected by the values of parameters used. However, in literature, few methods are proposed for parameter adaptation of ACO. In this article, a fuzzy-based parameter control mechanism for ACO has been developed. Three adaptive strategies FACO-1, FACO-2, FACO-3 are proposed for determining values of parameters alpha and beta, and evaporation factor separately as well as for all three parameters simultaneously. The performance of proposed strategies is compared with standard ACS on TSP and VRP benchmarks. Computational results on standard benchmark problems shows the effectiveness of the strategies.

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