Studies in Swarm Intelligence Techniques for Annual Crop Planning Problem in a New Irrigation Scheme
نویسندگان
چکیده
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.
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Three New Stochastic Local Search Metaheuristics for the Annual Crop Planning Problem Based on a New Irrigation Scheme
Annual Crop Planning (ACP) is an NP-hard-type optimization problem in agricultural planning. It involves finding optimal solutions concerning the seasonal allocations of a limited amount of agricultural land amongst the various competing crops that are required to be grown on it. This study investigates the effectiveness of employing three new local search (LS) metaheuristic techniques in deter...
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