期刊
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
卷 18, 期 2, 页码 258-268出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEVC.2013.2256427
关键词
Annual crop planning (ACP); computational intelligence; cuckoo search (CS); firefly algorithm; genetic algorithm (GA); glowworm optimization method; irrigation water requirements
资金
- College of Agriculture, Engineering and Science, University of KwaZulu-Natal, Durban, South Africa
Annual crop planning (ACP) is an NP-hard type optimization problem in agricultural planning. It involves finding the optimal solution for the seasonal hectare allocations of a limited amount of agricultural land, among various competing crops that are required to be grown on it. This study investigates the effectiveness of employing three relatively new swarm intelligence (SI) metaheuristic techniques in determining the solutions to the ACP problem with case study from an existing irrigation scheme. The SI metaheuristics studied are cuckoo search (CS), firefly algorithm (FA), and glowworm swarm optimization (GSO). Solutions obtained from these techniques are compared with that of a similar population-based technique, namely, genetic algorithm (GA). Results obtained show that each of the three SI algorithms provides superior solutions for the case studied.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据