Earticle

초록

영어

Mine Blast Algorithm (MBA) is newly developed metaheuristic technique. It has outperformed Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and their variants when solving various engineering optimization problems. MBA has been improved by IMBA, which is modified in this paper to accelerate its convergence speed furthermore. The proposed variant, so called Accelerated MBA (AMBA), replaces the previous best solution with the available candidate solution in IMBA. ANFIS accuracy depends on the parameters it is trained with. Keeping in view the drawbacks of gradients based learning of ANFIS using gradient descent and least square methods in two-pass learning algorithm, many have trained ANFIS using metaheuristic algorithms. In this paper, for getting high performance, the parameters of ANFIS are trained by the proposed AMBA. The experimental results of real-world benchmark problems reveal that AMBA can be used as an efficient optimization technique. Moreover, the results also indicate that AMBA converges earlier than its other counterparts MBA and IMBA.

목차

Abstract
 1. Introduction
 2. The Concept of ANFIS
 3. Mine Blast Algorithm – MBA
 4. Proposed Accelerated Mine Blast Algorithm – AMBA
 5. ANFIS Training using AMBA
 6. Experimental Results
 7. Conclusion
 Acknowledgements
 References

키워드

저자

• Mohd Najib Mohd Salleh [ Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja, Batu Pahat, Johor, Malaysia. ]
• Kashif Hussain [ Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja, Batu Pahat, Johor, Malaysia. ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Software Engineering and Its Applications

• 간기

  월간

• pISSN

  1738-9984

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605