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Multi‐objective crop planning using pareto‐based evolutionary algorithms
Author(s) -
Márquez Antonio L.,
Baños Raúl,
Gil Consolación,
Montoya María G.,
ManzanoAgugliaro Francisco,
Montoya Francisco G.
Publication year - 2011
Publication title -
agricultural economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.29
H-Index - 82
eISSN - 1574-0862
pISSN - 0169-5150
DOI - 10.1111/j.1574-0862.2011.00546.x
Subject(s) - multi objective optimization , evolutionary algorithm , pareto principle , scarcity , mathematical optimization , set (abstract data type) , computer science , pareto optimal , process (computing) , population , mathematics , economics , operating system , demography , sociology , microeconomics , programming language
Abstract The scarcity of water is a growing problem worldwide. The increasing use of water in industrial, urban, and agricultural applications together with the continuous increase in population require the proposal of efficient solutions. In the case of agricultural use, it is necessary to not only maximize the economic benefits, but also to establish optimal water‐saving crop planning, especially for water‐deficient regions. Due to the multi‐objective nature of these problems, the decision‐making process is complex. Fortunately, the increase in computational resources available in recent years has allowed researchers to develop efficient computational algorithms to deal with real and complex optimization problems, including agricultural ones. In particular, multi‐objective evolutionary algorithms (MOEAs) are known for their ability to optimize several objective functions simultaneously to provide a representative set of the Pareto front, which is a set of problem solutions representing a trade‐off between the best values of each of the objectives. This article proposes solving a multi‐objective crop planning problem using two Pareto‐based MOEAs. Results obtained when solving this problem using real data collected from a large number of greenhouses in Spain to show the advantages of using these multi‐objective approaches.