Comparison Study of Swarm Intelligence Techniques for the Annual Crop Planning Problem

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.

[1]  Bennie Grové,et al.  Stochastic efficiency optimisation of alternative agricultural water use strategies , 2006 .

[2]  Nordin Hj,et al.  A mathematical programming approach to crop mix problem , 2011 .

[3]  Robin Wardlaw,et al.  Application of genetic algorithms for irrigation water scheduling , 2004 .

[4]  Niels Schütze,et al.  Irrigation control: towards a new solution of an old problem , 2007 .

[5]  A. E. Eiben,et al.  Introduction to Evolutionary Computing , 2003, Natural Computing Series.

[6]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[7]  Christian Blum,et al.  Swarm Intelligence: Introduction and Applications , 2008, Swarm Intelligence.

[8]  Debasish Ghose,et al.  Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions , 2009, Swarm Intelligence.

[9]  Xin-She Yang,et al.  Nature-Inspired Metaheuristic Algorithms: Second Edition , 2010 .

[10]  Alan Greer,et al.  Agriculture, Forestry and Fisheries , 2004 .

[11]  P. Georgiou,et al.  Optimization model of an irrigation reservoir for water allocation and crop planning under various weather conditions , 2008, Irrigation Science.

[12]  C. Brouwer,et al.  Irrigation water management training manual no.3 irrigation water needs , 1985 .

[13]  T. Ray,et al.  An improved evolutionary algorithm for solving multi-objective crop planning models , 2009 .

[14]  Debasish Ghose,et al.  Glowworm swarm optimisation: a new method for optimising multi-modal functions , 2009, Int. J. Comput. Intell. Stud..

[15]  Jorge A. Ramírez,et al.  Optimal Stochastic Multicrop Seasonal and Intraseasonal Irrigation Control , 1997 .

[16]  Josiah Adeyemo,et al.  Differential evolution algorithm for crop planning: Single and multi-objective optimization model , 2010 .

[17]  Xin-She Yang,et al.  Nature-Inspired Metaheuristic Algorithms , 2008 .

[18]  J. Adeyemo,et al.  Maximum Irrigation Benefit Using Multiobjective Differential Evolution Algorithm (MDEA) , 2010 .

[19]  Ramatsimele Jacqueline Maisela.  Realizing agricultural potential in land reform: The case of Vaalharts irrigation scheme in the Northern Cape Province , 2007 .

[20]  Xin-She Yang,et al.  Chapter 8 – Firefly Algorithms , 2014 .