REVIEW PAPER Flower pollination algorithm development: a state of art review Sangeeta Pant 1 • Anuj Kumar 1 • Mangey Ram 2 Received: 8 December 2016 / Revised: 1 April 2017 Ó The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2017 Abstract The journey of a modern man from a troglodyte is due to human nature to try to unfold the mysteries of nature to improve the lives of human beings. A few years back we even can’t think that school of fish, genes, nature of bat or ant can be used to design optimization algorithms. As nature has the solution of every problem. Researchers working on optimization theory are developing optimiza- tion techniques which are inspired by nature and could be utilized as optimization tools for engineering problems. Recently, flower pollination algorithm, which is inspired by the pollination characteristics of flowering plants and associated flower constancy of some pollinating insects, caught the eye of many researchers in the world of opti- mization. This paper presents a brief review about the algorithm its developments and applications. In the last part of this paper, the authors have listed the limitations and topics within FPA that the authors consider as promising areas of future research. Keywords Flower pollination algorithm  Nature inspired optimization  Levy distribution  Hybrid algorithms 1 Introduction A decision maker uses a rational process for selecting the best of several alternatives when it comes on making a decision. In real life, decisions are often made on the basis of multiple, conflicting and non-commensurable criteria/ objectives in uncertain/imprecise environments. Opti- mization is the act of determining the value of certain parameters subject to constraints, so that some measure of optimality is satisfied. Optimization is everywhere, in every activity related to engineering, mathematics or sci- ences in which numerical information is processed. For example, civil engineers might be interested in designing structures of dams, bridges and towers of minimum weight keeping the natural disaster consequences in mind subject to certain constraints e.g. reliability, cost and other speci- fications. Widespread applicability of optimization meth- ods makes them a hot spot for researchers. Nature inspired optimization algorithms usually attempt to find a good approximation to the solution of a complex optimization problem. In the last two decades, several researchers have been focused on optimization algorithms those seek to mimic natural systems. Genetic algorithms (GA’s) devel- oped by Professor Holland (1975, 1968) and his students in the early 1970’s imitate some of the processes observed in natural evolution and is one of the most popular algo- rithms. Many modern nature inspired algorithms have some strong similarities with the Genetic algorithms since the population of individuals is used to search for an optimal solution. In general, nature inspired algorithms are population based algorithms. In the initial stage of the search population of individuals tend to explore the search space while in later stages population is more tilted towards the exploitation. If the algorithm is more focused on & Mangey Ram drmrswami@yahoo.com 1 Department of Mathematics, University of Petroleum & Energy Studies, Dehradun 248007, India 2 Department of Mathematics, Graphic Era University, Dehradun 248002, India 123 Int J Syst Assur Eng Manag DOI 10.1007/s13198-017-0623-7