지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- Korean Institute of Information Scientists and Engineers Journal of Computing Science and Engineering Journal of Computing Science and Engineering Vol.8 No.4
- 발행연도
- 2014.12
- 수록면
- 199 - 206 (8page)
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초록· 키워드
오류제보하기
The ant colony optimization (ACO) algorithm is a new heuristic algorithm that offers good robustness and searching ability. With in-depth exploration, the ACO algorithm exhibits slow convergence speed, and yields local optimization solutions. Based on analysis of the ACO algorithm and the genetic algorithm, we propose a novel hybrid genetic ant colony optimization (NHGAO) algorithm that integrates multi-population strategy, collaborative strategy, genetic strategy, and ant colony strategy, to avoid the premature phenomenon, dynamically balance the global search ability and local search ability, and accelerate the convergence speed. We select the traveling salesman problem to demonstrate the validity and feasibility of the NHGAO algorithm for solving complex optimization problems. The simulation experiment results show that the proposed NHGAO algorithm can obtain the global optimal solution, achieve self-adaptive control parameters, and avoid the phenomena of stagnation and prematurity.
목차
Abstract
Ⅰ. INTRODUCTION
Ⅱ. RELATED WORKS
Ⅲ. THE INTELLIGENT OPTIMIZATION ALGORITHMS
Ⅳ. MULTI-STRATEGY ADAPTIVE CONTROL METHOD
Ⅴ. THE IDEA AND A DESCRIPTION OF THE NHGAO ALGORITHM
Ⅵ. THEORETICAL ANALYSIS OF THE NHGAO ALGORITHM
Ⅶ. EXPERIMENTAL RESULTS AND ANALYSIS
Ⅷ. CONCLUSION
REFERENCES
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UCI(KEPA) : I410-ECN-0101-2016-569-001106204