Earticle

초록

영어

Although meta-heuristic optimization algorithms have been used to solve many optimization problems, they still suffer from two main difficulties: What are the best parameters for a particular problem? How do we escape from the local optima? In this paper, a new, efficient meta-heuristic optimization algorithm inspired by wild dog packs is proposed. The main idea involves using three self-competitive parameters that are similar to the smell strength. The parameters are used to control the movement of the alpha dogs and, consequently, the movement of the whole pack. The rest of the pack is used to explore the neighboring area of the alpha dog, while the hoo procedure is used to escape from the local optima. The suggested method is applied to several unimodal and multimodal benchmark problems and is compared to five modern meta-heuristic algorithms. The experimental results show that the new algorithm outperforms other peer algorithms.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Wild Dog Packs
 4. Wild Dog Pack Optimization
 5. Benchmark Problem
 6. Experimental Results
 7. Conclusion
 References

키워드

저자

• Essam Al Daoud [ Computer Science Department, Zarqa University Zarqa, Jordan ]
• Rafat Alshorman [ Computer Science Department, Zarqa University Zarqa, Jordan ]
• Feras Hanandeh [ Department of Computer Information Systems, Faculty of Prince Al-Hussein Bin Abdallah II For Information Technology, Hashemite University, Jordan. ]

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