Farmland fertility: A new metaheuristic algorithm for solving continuous optimization problems

•In this paper, a new metaheuristic algorithm that is inspired by farmland fertility in nature is presented which is evaluated by using large number of mathematical problem of standard benchmark.•We compared it with other powerful metaheuristic algorithms such as: ABC, FA, HS, PSO, DA, BA and improv...

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Bibliographic Details
Published in:Applied soft computing Vol. 71; pp. 728 - 746
Main Authors: Shayanfar, Human, Gharehchopogh, Farhad Soleimanian
Format: Journal Article
Language:English
Published: Elsevier B.V 01.10.2018
Subjects:
Farmland fertility
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Metaheuristic algorithms
Optimization
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Farmland fertility
Optimization
Metaheuristic algorithms
ISSN:1568-4946, 1872-9681
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Summary:•In this paper, a new metaheuristic algorithm that is inspired by farmland fertility in nature is presented which is evaluated by using large number of mathematical problem of standard benchmark.•We compared it with other powerful metaheuristic algorithms such as: ABC, FA, HS, PSO, DA, BA and improved PSO.•The proposed metaheuristic algorithm is very simple and very flexible than other available metaheuristic algorithms. And with smaller dimensions problems has been able to act as a strong metaheuristic algorithm and it has optimized problems nicely. Nowadays, the use of metaheuristic algorithms has dramatically increased in order to achieve the optimal solution in solving continuous optimization problems. In this paper, a new metaheuristic algorithm that is inspired by farmland fertility in nature is presented; this algorithm divides into several parts of the farmland, and to optimize solutions of each section with optimal efficiency of two types in internal and external memory. In order to evaluate the farmland fertility, we simulated it on 20 main function of mathematical optimization that is important to evaluate this type of algorithms and the results displayed. This farmland fertility has been compared with other metaheuristic algorithms such as; artificial bee colony (ABC), firefly algorithm (FA), harmony search (HS), particle swarm optimization (PSO), differential evolution (DE), bat algorithm (BA), and improved PSO and the results are displayed clearly. Simulations show that the farmland fertility often acts better than other metaheuristic algorithms. The farmland fertility in problems with smaller dimensions problems has been able to act as a strong metaheuristic algorithm and it has optimized problems nicely. Furthermore, the farmland fertility in problems with larger dimensions has been able to perform better than other algorithms. The effectiveness of other algorithms decreases significantly with number of dimensions and the farmland fertility obtains better results than other algorithms.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2018.07.033