Earticle

초록

영어

Particle Swarm Optimization is a popular heuristic search technique developed by Eberhart and Kennedy in 1995 which takes its inspiration from the social and cognitive learning of birds or fishes. This algorithm comprises the involvement of swarm intelligence technique for optimization. The most widely accepted variation of the basic PSO technique is PSO with Inertia weight which substantially controls the convergence behaviour and exploration exploitation trade-off in the basic PSO technique. From its initialization onwards a huge range of modifications of Inertia Weight strategy have been recommended. This paper involves the use of PSO with varying values of inertia weight for solving the Travelling Salesman Problem. An analysis of how different inertia weight values effect the solution in terms of time complexity, space complexity and convergence in carried out in order to know the value best suited for setting up the inertia weight.

목차

Abstract
 1. Introduction
 2. Problem Formulation
 3. Particle Swarm Optimization
 4. Inertia Weight Particle Swarm Optimization
 5. Experimental Parameter Setting
 6. Experimental Results
 7. Conclusion
 References

키워드

저자

• Ashima Chopra [ Computer Science Guru Nanak Dev University, RC Jalandhar, Punjab, India ]
• Mandeep Kaur [ Computer Science, Guru Nanak Dev University RC Jalandhar, Punjab, India ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605