Optimization can play an important role in supporting agricultural community not only in designing and manufacturing
mechanical equipment but also in optimal crop planning. The related optimization models are not necessarily linear due to
varying resources and complex environmental processes. The traditional linear programming techniques may not be practical
in such situations. Metaheuristics are powerful approaches to solve complex nonlinear models. Metaheuristics are developed
by transforming dynamics of natural phenomena to artificial intelligence computational environment. Realizing the potential
adaptability of working principles of irrigation tools, this paper develops a novel optimization algorithm called Targeted
Showering Optimization (TSO) algorithm which aims to solve linear, nonlinear and multi-objective optimization problems
arising in agriculture, engineering and other scientific areas. In the present work, the design of TSO algorithm has been
elaborated in detail and is followed by the performance evaluation of TSO algorithm by applying it to six well-known
benchmark functions. The obtained results reveal that the developed method finds the best quality solutions of at least four
benchmark functions in just 100 iterations and in additional 100 iterations it supersedes other nature inspired algorithms. To
show the applicability of the proposed method in agriculture, a case study regarding the model of optimal crop rotation in
Slovenian organic farming has been solved by TSO. The results of optimization models of crop rotation produced by TSO are
also promising and provide a clear trade-off between total income and the nitrogen off-take when the maximization of total
income and minimization of nitrogen off-take are dealt simultaneously.
