The following information was submitted:
Transactions: INTERNATIONAL JOURNAL of MATHEMATICS AND COMPUTERS IN SIMULATION
Transactions ID Number: 17-209
Full Name: Suraya Masrom
Position: Senior Lecturer
Age: ON
Sex: Female
Address: Faculty of Computer and Mathematical Science, Universiti Teknologi MARA
Country: MALAYSIA
Tel:
Tel prefix:
Fax:
E-mail address: suray078@gmail.com
Other E-mails: suray078@perak.uitm.edu.my
Title of the Paper: An Investigation of Hybrid Particle Swarm Optimization Techniques to Vehicle Routing Problem with Time Windows
Authors as they appear in the Paper: S. Masrom, Siti Z. Z. Abidin, A.M. Nasir, A.S.A. Rahman
Email addresses of all the authors: suray078@perak.uitm.edu.my,sitizaleha533@salam.uitm.edu.my,arfah485@perak.uitm.edu.my,sanirahman@petronas.com.my
Number of paper pages: 8
Abstract: Particle Swarm Optimization (PSO) is a well known technique for solving many kind of combinatorial optimization problems including scheduling, resource allocation and vehicle routing. However, basic PSO suffers from premature convergence problem due to its lack in diversity while it traverses the search spaces. This problem can directly reduce the quality of optimal solution produced by PSO. In order to slow down the PSO degeneration, mutation operator from the Genetic Algorithm (GA) has been incorporated into the basic PSO algorithm. This paper describes some investigations that have been carried out on the effect of implementing different hybrid techniques involving GA mutation and PSO. Results were obtained from empirical experiments. Each hybrid PSO has been incorporated with different types of mutation operators. Every type has been tested in different sets of implementation techniques such as constant mutation, linear decreasing mutation and mutation when sta!
gnant. All the hybrid techniques were tested on some datasets of Vehicle Routing Problem with Time Windows (VRPTW). The results show that all the mutation operators are useful in helping the basic PSO to achieve better optimization results and the best hybridization technique is implementing the mutation when PSO particles have stagnated.
Keywords: Genetic Algorithm, Hybridization, Particle Swarm Optimization, Mutation, Vehicle Routing
EXTENSION of the file: .pdf
Special (Invited) Session: Hybrid Particle Swarm Optimization for Vehicle Routing Problem with Time Windows
Organizer of the Session: 658-233
How Did you learn about congress: Dr Nasiroh Omah - nasiroh@tmsk.uitm.edu.my
IP ADDRESS: 60.51.14.115
