Particle swarm optimization with partial search to solve traveling salesman problem
Particle Swarm Optimization (PSO) is population based optimization technique on metaphor of social behavior of flocks of birds and/or schools of fishes. For better solution, at every step each particle changes its velocity based on its current velocity with respect to its previous best position and...
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2012
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| Online Access: | http://irep.iium.edu.my/24983/ http://irep.iium.edu.my/24983/1/1058C.pdf |
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iium-249832012-09-18T02:17:23Z http://irep.iium.edu.my/24983/ Particle swarm optimization with partial search to solve traveling salesman problem Akhand, M.A.H. Akter, Shahina Rahman, S. Sazzadur Rahman, M.M. Hafizur TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices Particle Swarm Optimization (PSO) is population based optimization technique on metaphor of social behavior of flocks of birds and/or schools of fishes. For better solution, at every step each particle changes its velocity based on its current velocity with respect to its previous best position and position of the current best particle in the population. PSO has found as an efficient method for solving function optimization problems, and recently it also studied to solve combinatorial problems such as Traveling Salesman Problem (TSP). Existing method introduced the idea of Swap Operator (SO) and Swap Sequence (SS) in PSO to handle TSP. For TSP, each particle represents a complete tour and velocity is measured as a SS consisting with several SOs. A SO indicates two positions in the tour that might be swap. In the existing method, a new tour is considered after applying a complete SS with all its SOs. Whereas, every SO implantation on a particle (i.e., a solution or a tour) gives a new solution and there might be a chance to get a better tour with some of SOs instead of all the SOs. The objective of the study is to achieve better result introducing using such partial search option for solving TSP. The proposed PSO with Partial Search (PSOPS) algorithm is shown to produce optimal solution within a less number of generation than standard PSO, Genetic Algorithm in solving benchmark TSP. 2012-07-03 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/24983/1/1058C.pdf Akhand, M.A.H. and Akter, Shahina and Rahman, S. Sazzadur and Rahman, M.M. Hafizur (2012) Particle swarm optimization with partial search to solve traveling salesman problem. In: International Conference on Computer and Communication Engineering (ICCCE 2012), 3-5 July 2012, Seri Pacific Hotel Kuala Lumpur. |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
International Islamic University Malaysia |
| building |
IIUM Repository |
| collection |
Online Access |
| language |
English |
| topic |
TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices |
| spellingShingle |
TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices Akhand, M.A.H. Akter, Shahina Rahman, S. Sazzadur Rahman, M.M. Hafizur Particle swarm optimization with partial search to solve traveling salesman problem |
| description |
Particle Swarm Optimization (PSO) is population
based optimization technique on metaphor of social behavior of flocks of birds and/or schools of fishes. For better solution, at every step each particle changes its velocity based on its current velocity with respect to its previous best position and position of the current best particle in the population. PSO has found as an efficient method for solving function optimization problems, and recently it also studied to solve combinatorial problems such as
Traveling Salesman Problem (TSP). Existing method introduced
the idea of Swap Operator (SO) and Swap Sequence (SS) in PSO
to handle TSP. For TSP, each particle represents a complete tour and velocity is measured as a SS consisting with several SOs. A SO indicates two positions in the tour that might be swap. In the existing method, a new tour is considered after applying a complete SS with all its SOs. Whereas, every SO implantation on a particle (i.e., a solution or a tour) gives a new solution and there
might be a chance to get a better tour with some of SOs instead of all the SOs. The objective of the study is to achieve better result introducing using such partial search option for solving TSP. The proposed PSO with Partial Search (PSOPS) algorithm is shown to produce optimal solution within a less number of generation than standard PSO, Genetic Algorithm in solving benchmark TSP. |
| format |
Conference or Workshop Item |
| author |
Akhand, M.A.H. Akter, Shahina Rahman, S. Sazzadur Rahman, M.M. Hafizur |
| author_facet |
Akhand, M.A.H. Akter, Shahina Rahman, S. Sazzadur Rahman, M.M. Hafizur |
| author_sort |
Akhand, M.A.H. |
| title |
Particle swarm optimization with partial search to solve traveling salesman problem |
| title_short |
Particle swarm optimization with partial search to solve traveling salesman problem |
| title_full |
Particle swarm optimization with partial search to solve traveling salesman problem |
| title_fullStr |
Particle swarm optimization with partial search to solve traveling salesman problem |
| title_full_unstemmed |
Particle swarm optimization with partial search to solve traveling salesman problem |
| title_sort |
particle swarm optimization with partial search to solve traveling salesman problem |
| publishDate |
2012 |
| url |
http://irep.iium.edu.my/24983/ http://irep.iium.edu.my/24983/1/1058C.pdf |
| first_indexed |
2023-09-18T20:37:21Z |
| last_indexed |
2023-09-18T20:37:21Z |
| _version_ |
1777409134861221888 |