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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 determining solutions to an ACP problem at a new Irrigation Scheme. These three new LS metaheuristic techniques are the Best Performance Algorithm (BPA), Iterative Best Performance Algorithm (IBPA), and the Largest Absolute Difference Algorithm (LADA). The solutions determined by these LS metaheuristic techniques are compared against the solutions of two other well-known LS metaheuristic techniques in the literature. These techniques are Tabu Search (TS) and Simulated Annealing (SA). The comparison with TS and SA was to determine the relative merits of the solutions found by BPA, IBPA, and LADA. The results show that TS performed as the overall best. However, LADA determined the best solution that was the most economically feasible.

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This paper investigates the capabilities of three new local search (LS) metaheuristic algorithms in determining solutions to an annual crop planning (ACP) problem at an existing Irrigation Scheme. ACP is an optimisation problem in agricultural planning which involves determining resource allocation solutions amongst the various crops that are required to be grown at an irrigation scheme, within a year. The LS algorithms investigated are the best performance algorithm (BPA), the iterative best performance algorithm (IBPA) and the largest absolute difference algorithm (LADA). To determine the relative merits of the solutions found by these algorithms, their solutions have been compared against the solutions of two well-known LS metaheuristic algorithms and four population-based metaheuristic algorithms in the literature. The results show that BPA, IBPA and LADA were competitive in determining solutions for this particular optimisation problem.