Commun Nonlinear Sci Numer Simulat 42 (2017) 358–369 Contents lists available at ScienceDirect Commun Nonlinear Sci Numer Simulat journal homepage: www.elsevier.com/locate/cnsns Research paper Kidney-inspired algorithm for optimization problems Najmeh Sadat Jaddi a,* , Jafar Alvankarian b , Salwani Abdullah a a Data Mining and Optimization Research Group, Centre for Artificial Intelligence Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Malaysia b Institute of Microengineering and Nanoelectronics, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia a r t i c l e i n f o Article history: Received 2 May 2015 Revised 18 April 2016 Accepted 2 June 2016 Available online 3 June 2016 Keywords: Artificial intelligence Kidney-inspired algorithm Optimization Meta-heuristics a b s t r a c t 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 excre- tion 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 so- lution 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 algo- rithm 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 statis- tical analysis further confirms the ability of this algorithm to produce good-quality results. © 2016 Elsevier B.V. All rights reserved. 1. Introduction In recent years, the researchers are trying to imitate nature in technology due to the nature is the best trainer for technology and its designs and capabilities are enormous. In addition, these two fields have much stronger connection since a lot of new problems in computer science are categorized as nature problems. Therefore, easy mapping is possible between nature and technology in real world. Although, there are many nature-inspired algorithms in the literature in recent years, we still believe that there is room to improve this mapping between nature and computer science by both enhancement of the existing nature- inspired algorithms and introducing new ones. This can be as motivation of introducing kidney- inspired algorithm in this study. * Corresponding author. E-mail addresses: najmehjaddi@gmail.com (N.S. Jaddi), alvankarian@gmail.com (J. Alvankarian), salwani@ukm.edu.my (S. Abdullah). http://dx.doi.org/10.1016/j.cnsns.2016.06.006 1007-5704/© 2016 Elsevier B.V. All rights reserved.