### Abstract

In this paper, a population-based algorithm inspired by the kidney process in the human body is proposed. In this algorithm the solutions are filtered in a rate that is calculated based on the mean of objective functions of all solutions in the current population of each iteration. The filtered solutions as the better solutions are moved to filtered blood and the rest are transferred to waste representing the worse solutions. This is a simulation of the glomerular filtration process in the kidney. The waste solutions are reconsidered in the iterations if after applying a defined movement operator they satisfy the filtration rate, otherwise it is expelled from the waste solutions, simulating the reabsorption and excretion functions of the kidney. In addition, a solution assigned as better solution is secreted if it is not better than the worst solutions simulating the secreting process of blood in the kidney. After placement of all the solutions in the population, the best of them is ranked, the waste and filtered blood are merged to become a new population and the filtration rate is updated. Filtration provides the required exploitation while generating a new solution and reabsorption gives the necessary exploration for the algorithm. The algorithm is assessed by applying it on eight well-known benchmark test functions and compares the results with other algorithms in the literature. The performance of the proposed algorithm is better on seven out of eight test functions when it is compared with the most recent researches in literature. The proposed kidney-inspired algorithm is able to find the global optimum with less function evaluations on six out of eight test functions. A statistical analysis further confirms the ability of this algorithm to produce good-quality results.

Original language | English |
---|---|

Pages (from-to) | 358-369 |

Number of pages | 12 |

Journal | Communications in Nonlinear Science and Numerical Simulation |

Volume | 42 |

DOIs | |

Publication status | Published - 1 Jan 2017 |

### Fingerprint

### Keywords

- Artificial intelligence
- Kidney-inspired algorithm
- Meta-heuristics
- Optimization

### ASJC Scopus subject areas

- Modelling and Simulation
- Numerical Analysis
- Applied Mathematics

### Cite this

*Communications in Nonlinear Science and Numerical Simulation*,

*42*, 358-369. https://doi.org/10.1016/j.cnsns.2016.06.006

**Kidney-inspired algorithm for optimization problems.** / Jaddi, Najmeh Sadat; Alvankarian, Jafar; Abdullah, Salwani.

Research output: Contribution to journal › Article

*Communications in Nonlinear Science and Numerical Simulation*, vol. 42, pp. 358-369. https://doi.org/10.1016/j.cnsns.2016.06.006

}

TY - JOUR

T1 - Kidney-inspired algorithm for optimization problems

AU - Jaddi, Najmeh Sadat

AU - Alvankarian, Jafar

AU - Abdullah, Salwani

PY - 2017/1/1

Y1 - 2017/1/1

N2 - In this paper, a population-based algorithm inspired by the kidney process in the human body is proposed. In this algorithm the solutions are filtered in a rate that is calculated based on the mean of objective functions of all solutions in the current population of each iteration. The filtered solutions as the better solutions are moved to filtered blood and the rest are transferred to waste representing the worse solutions. This is a simulation of the glomerular filtration process in the kidney. The waste solutions are reconsidered in the iterations if after applying a defined movement operator they satisfy the filtration rate, otherwise it is expelled from the waste solutions, simulating the reabsorption and excretion functions of the kidney. In addition, a solution assigned as better solution is secreted if it is not better than the worst solutions simulating the secreting process of blood in the kidney. After placement of all the solutions in the population, the best of them is ranked, the waste and filtered blood are merged to become a new population and the filtration rate is updated. Filtration provides the required exploitation while generating a new solution and reabsorption gives the necessary exploration for the algorithm. The algorithm is assessed by applying it on eight well-known benchmark test functions and compares the results with other algorithms in the literature. The performance of the proposed algorithm is better on seven out of eight test functions when it is compared with the most recent researches in literature. The proposed kidney-inspired algorithm is able to find the global optimum with less function evaluations on six out of eight test functions. A statistical analysis further confirms the ability of this algorithm to produce good-quality results.

AB - In this paper, a population-based algorithm inspired by the kidney process in the human body is proposed. In this algorithm the solutions are filtered in a rate that is calculated based on the mean of objective functions of all solutions in the current population of each iteration. The filtered solutions as the better solutions are moved to filtered blood and the rest are transferred to waste representing the worse solutions. This is a simulation of the glomerular filtration process in the kidney. The waste solutions are reconsidered in the iterations if after applying a defined movement operator they satisfy the filtration rate, otherwise it is expelled from the waste solutions, simulating the reabsorption and excretion functions of the kidney. In addition, a solution assigned as better solution is secreted if it is not better than the worst solutions simulating the secreting process of blood in the kidney. After placement of all the solutions in the population, the best of them is ranked, the waste and filtered blood are merged to become a new population and the filtration rate is updated. Filtration provides the required exploitation while generating a new solution and reabsorption gives the necessary exploration for the algorithm. The algorithm is assessed by applying it on eight well-known benchmark test functions and compares the results with other algorithms in the literature. The performance of the proposed algorithm is better on seven out of eight test functions when it is compared with the most recent researches in literature. The proposed kidney-inspired algorithm is able to find the global optimum with less function evaluations on six out of eight test functions. A statistical analysis further confirms the ability of this algorithm to produce good-quality results.

KW - Artificial intelligence

KW - Kidney-inspired algorithm

KW - Meta-heuristics

KW - Optimization

UR - http://www.scopus.com/inward/record.url?scp=84973442773&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84973442773&partnerID=8YFLogxK

U2 - 10.1016/j.cnsns.2016.06.006

DO - 10.1016/j.cnsns.2016.06.006

M3 - Article

VL - 42

SP - 358

EP - 369

JO - Communications in Nonlinear Science and Numerical Simulation

JF - Communications in Nonlinear Science and Numerical Simulation

SN - 1007-5704

ER -