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A modified farmland fertility algorithm for solving constrained engineering problems
Author(s) -
Gharehchopogh Farhad Soleimanian,
Farnad Behnam,
Alizadeh Ali
Publication year - 2021
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.6310
Subject(s) - algorithm , mathematical optimization , convergence (economics) , computer science , function (biology) , variance (accounting) , pairwise comparison , mathematics , artificial intelligence , business , accounting , evolutionary biology , economics , biology , economic growth
Solving constrained engineering optimization problems is a highly significant issue, and many different approaches have been proposed in this regard. In this article, a modified farmland fertility algorithm (FFA) has been proposed. This algorithm improves new solutions by benefiting from neighborhoods produced by the new method. In the proposed algorithm, the FFA algorithm phases are modified in the update functions, and some of its variables are replaced with new ones. In the first phase of the algorithm, a mutation is also used to improve the solution of the problem with a particular rule. The results on CEC2019 standard functions were examined to determine the impact of the new parameters. Experiments were performed on 26 standard functions and two constrained engineering optimization problems. To make a better comparison, the analysis of variance, without parameters such as the Friedman test and Pairwise test, was used to evaluate and compare the algorithms. These experiments showed that the proposed algorithm is capable of high‐speed convergence and can minimize most standard functions and engineering constraints within the minimum time and the least function evaluation to the optimum value. The proposed FFA could outperform the previous version of the FFA with minimum modification in convergence, CPU‐time, and complexity.