인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
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
- 저자정보
피인용 0
초록·키워드
The whale optimization algorithm (WOA) is one of the most powerful swarm-based, nature-inspired metaheuristic algorithms, developed by mimicking the bubble-net hunting maneuver technique of humpback whales to solve complex optimization problems. The WOA has been widely adopted in various real-world optimization fields due to its simple structure, minimal parameter requirements, and fast convergence rate. This article proposes a novel modified version of the original WOA, named the random-flight whale optimization algorithm (RFWOA), which incorporates three types of random-flight mechanisms, i.e., uniform distribution, Rayleigh flights, and Lévy flights. These mechanisms are employed to generate elite solutions and enhance the search performance. The proposed RFWOA helps obtaining a better tradeoff between the exploration and exploitation properties of the original WOA. To evaluate its effectiveness, the RFWOA is tested against ten benchmark functions and applied to solve ten multi-depot multi-vehicle routing problems (MDMVRP) for global optimization. The results obtained by RFWOA are compared with those obtained by other well-known nature-inspired algorithms, including the original WOA, particle swarm optimization (PSO), and genetic algorithm (GA). The experimental results demonstrated that the proposed RFWOA significantly outperformed the competing algorithms in solving both the benchmark functions and the MDMVRP optimization tasks. These results confirm the superiority of the RFWOA over the original WOA, PSO, and GA.
본문·목차
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