Earticle

초록

영어

Cat swarm optimization (CSO) is a novel meta-heuristic for evolutionary optimization algorithms based on swarm intelligence. CSO imitates the behavior of cats through two sub-modes: seeking and tracing. Previous studies have indicated that CSO algorithms outperform other well-known meta-heuristics, such as genetic algorithms and particle swarm optimization, because of complexity, sometimes the pure CSO takes a long time to converge to reach to optimal solution. For improving the convergence of CSO with better accuracy and less computational time, this study presents an improvement structure of cat swarm optimization (ICSO), capable of improving search efficiency within the problem space under the conditions of a small population size and a few iteration numbers. In this paper, an improved algorithm is presented by mixing two concepts, first concept found in parallel cat swarm optimization (PCSO) method for solving numerical optimization problems. The parallel cat swarm optimization (PCSO) method is an optimization algorithm designed to solve optimization problems Based on cats’ cooperation and competition for improving the convergence of Cat Swarm Optimization,, the second concept found in Average-Inertia Weighted CSO (AICSO) by adding a new parameter to the velocity update equation as an inertia weight and used a new form of the position update equation in the tracing mode of algorithm. The performance of ICSO is sensitive to the control parameters selection. The experimental results show that the proposed algorithm gets higher accuracy than the existing methods and requires less computational time and has much better convergence than pure CSO, and the proposed effective algorithm can provide the optimum block matching in a very short time, finding the best solution in less iteration and suitable for video tracking applications.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Cat Swarm Optimization (CSO)
  3.1. Seeking Mode: Resting and Observing
  3.2. Tracing Mode: Running After a Target
 4. CSO Movement = Seeking Mode + Tracing Mode
 5. Parallel Cat Swarm Optimization (PCSO)
  5.1. Parallel Tracing Mode Process
  5.2. Information Exchanging Process
 6. Average-Inertia Weighted Cat Swarm Optimization (AICSO)
 7. Proposed Algorithm
 8. Simulation Results
 9. Conclusion
 References

키워드

저자

• Prof. Dr. Israa Hadi [ Professor College of Information Technology University of Babylon ]
• Mustafa Sabah [ Ph.D. Student, College of Information Technology University of Babylon ]

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