Studies in swarm intelligence techniques for annual crop planning problem in a new irrigation scheme

Aderemi Oluyinka Adewumi, Sivashan Chetty

Abstract


Annual crop planning (ACP) is an NP-Hard type optimisation problem in agricultural planning. It involves finding optimal solutions for the seasonal allocations of a limited amount of agricultural land among the various competing crops that need to be grown on it. This study investigates the effectiveness of employing three relatively new Swarm Intelligence (SI) techniques in determining solutions to an ACP problem at a new irrigation scheme. The SI metaheuristics studied include Cuckoo Search (CS), Firefly Algorithm (FA), and Glow-worm Swarm Optimisation (GSO). The solutions determined by these SI techniques are compared against the solutions of Genetic Algorithm (GA), another population-based metaheuristic technique. This helps to determine the relative merits of the solutions found by the SI techniques. The results show that the SI algorithms delivered solutions superior to those of GA in determining solutions to the ACP problem at a new irrigation scheme.


Keywords


Annual Crop Planning; Irrigated Water Requirements; Optimization; Cuckoo Search; Firefly Algorithm; Glowworm Swarm Optimization; Genetic Algorithm

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DOI: https://doi.org/10.7166/24-3-626

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Copyright (c) 2015 The South African Journal of Industrial Engineering


ISSN 2224-7890 (on-line) ; ISSN 1012-277X (print)


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