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

By Human Shayanfar, Farhad Soleimanian Gharehchopogh. Applied Soft Computing, 2018

318 citations recorded by the source corpus.

Abstract 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.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Soil Science

View Paper: https://www.semanticscholar.org/paper/d23ad4879ff1c50e6dd1df235fec1d92f15e31e1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Mohammad Namazi
2. Laya Ebrahimi
3. Benyamin Abdollahzadeh
4. Farhad Soleimanian Gharehchopogh

In Computational Theory and Mathematics

1. Mohammad Namazi
2. Laya Ebrahimi
3. Benyamin Abdollahzadeh
4. Farhad Soleimanian Gharehchopogh

In Soil Science

1. Benyamin Abdollahzadeh
2. Mohammad Namazi
3. Laya Ebrahimi
4. Farhad Soleimanian Gharehchopogh

Citations in the corpus

• Advances in Sparrow Search Algorithm: A Comprehensive Survey (2022, methodology): “Examples include Farmland Fertility Algorithm [14], African Vultures Optimization Algorithm (AVOA) [15], Starling murmuration optimizer [16], Sparrow Search Algorıthm [17], and Artificial Gorilla Troops Optimizer [18].”
• Puma optimizer (PO): a novel metaheuristic optimization algorithm and its application in machine learning (2024, unknown): “They are based on evolutionary algorithms (EA) [9], swarm intelligence (SI) [10, 11], physics-based methods (PH) [12, 13], and human-based (HU) [14, 15] methods.”