Abstract
The fruit fly optimization algorithm (FOA) has been developed by inspiring osphresis and vision behaviors of the fruit flies to solve continuous optimization problems. As many researchers know that FOA has some shortcomings, this study presents an improved version of FOA to remove with these shortcomings in order to improve its optimization performance. According to the basic version of FOA, the candidate solutions could not take values those are negative as well as stated in many studies in the literature. In this study, two sign parameters are added into the original FOA to consider not only the positive side of the search space, but also the whole. To experimentally validate the proposed approach, namely signed FOA, SFOA for short, 21 well-known benchmark problems are considered. In order to demonstrate the effectiveness and success of the proposed method, the results of the proposed approach are compared with the results of the original FOA, results of the two different state-of-art versions of particle swarm optimization algorithm, results of the cuckoo search optimization algorithm and results of the firefly optimization algorithm. By analyzing experimental results, it can be said that the proposed approach achieves more successful results on many benchmark problems than the compared methods, and SFOA is presented as more equal and fairer in terms of screening the solution space.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Abdelaziz AY, Ali ES (2015) Cuckoo search algorithm based load frequency controller design for nonlinear interconnected power system. Int J Electric Power Energy Syst 73:632–643. doi:10.1016/j.ijepes.2015.05.050
Andziulis A, Dzemydiene D, Steponavicius R, Jakovlev S (2011) Comparison of two heuristic approaches for solving the production scheduling problem. Inf Technol Control 40(2):118–122
Babaoglu I (2015) Artificial bee colony algorithm with distribution-based update rule. Appl Soft Comput 34:851–861. doi:10.1016/j.asoc.2015.05.041
Bhandari AK, Singh VK, Kumar A, Singh GK (2014) Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy. Expert Syst Appl 41(7):3538–3560. doi:10.1016/j.eswa.2013.10.059
Chandrasekaran K, Simon SP (2012) Network and reliability constrained unit commitment problem using binary real coded firefly algorithm. Int J Electric Power Energy Syst 43(1):921–932. doi:10.1016/j.ijepes.2012.06.004
Dai HD, Zhao GR, Lu JH, Dai SW (2014) Comment and improvement on “a new fruit fly optimization algorithm: taking the financial distress model as an example”. Knowl-Based Syst 59:159–160. doi:10.1016/j.knosys.2014.01.010
Das R (2016) Estimation of feasible materials and thermal conditions in a trapezoidal fin using genetic algorithm. Proc Inst Mech Eng Part G J Aerosp Eng 230(13):2356–2368. doi:10.1177/0954410015623975
Das R, Ooi KT (2013) Application of simulated annealing in a rectangular fin with variable heat transfer coefficient. Inverse Probl Sci Eng 21(8):1352–1367. doi:10.1080/17415977.2013.764294
Das R, Singh K, Akay B, Gogoi TK (2016) Application of artificial bee colony algorithm for maximizing heat transfer in a perforated fin. Proc Inst Mech Eng Part E J Process Mech Eng. doi:10.1177/0954408916682985
Dorigo M, Maniezzo V, Colorni A (1996) Ant system: optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B Cybern 26(1):29–41. doi:10.1109/3477.484436
Fateen SEK, Bonilla-Petriciolet A (2014) Unconstrained Gibbs free energy minimization for phase equilibrium calculations in nonreactive systems, using an improved cuckoo search algorithm. Ind Eng Chem Res 53(26):10826–10834. doi:10.1021/ie5016574
Hakli H, Uguz H (2014) A novel particle swarm optimization algorithm with Levy flight. Appl Soft Comput 23:333–345. doi:10.1016/j.asoc.2014.06.034
Jiang B, Wang N (2014) Cooperative bare-bone particle swarm optimization for data clustering. Soft Comput 18(6):1079–1091. doi:10.1007/s00500-013-1128-1
Karaboğa D (2005) An idea based on honey bee swarm for numerical optimization. Technical Report-TR06. http://mf.erciyes.edu.tr/abc/pub/tr06_2005.pdf. Accessed 10 Jul 2016
Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132. doi:10.1016/j.amc.2009.03.090
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: 1995 IEEE international conference on neural networks proceedings, vol 1–6, pp 1942–1948. doi:10.1109/Icnn.1995.488968
Kiran MS (2015) TSA: tree-seed algorithm for continuous optimization. Expert Syst Appl 42(19):6686–6698. doi:10.1016/j.eswa.2015.04.055
Kiran MS, Ozceylan E, Gunduz M, Paksoy T (2012) A novel hybrid approach based on particle swarm optimization and ant colony algorithm to forecast energy demand of Turkey. Energy Convers Manag 53(1):75–83. doi:10.1016/j.enconman.2011.08.004
Kiran MS, Hakli H, Gunduz M, Uguz H (2015) Artificial bee colony algorithm with variable search strategy for continuous optimization. Inf Sci 300:140–157. doi:10.1016/j.ins.2014.12.043
Lei W, Xiao WS, Liang Z, Qi L, Wang JL (2016) An improved fruit fly optimization algorithm based on selecting evolutionary direction intelligently. Int J Comput Intell Syst 9(1):80–90. doi:10.1080/18756891.2016.1144155
Li HZ, Guo S, Li CJ, Sun JQ (2013) A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm. Knowl-Based Syst 37:378–387. doi:10.1016/j.knosys.2012.08.015
Lim WCE, Kanagaraj G, Ponnambalam SG (2014) PCB drill path optimization by combinatorial cuckoo search algorithm. Sci World J. doi:10.1155/2014/264518
Lin SM (2013) Analysis of service satisfaction in web auction logistics service using a combination of fruit fly optimization algorithm and general regression neural network. Neural Comput Appl 22(3–4):783–791. doi:10.1007/s00521-011-0769-1
Lopez-Ibanez M, Blum C (2010) Beam-ACO for the travelling salesman problem with time windows. Comput Oper Res 37(9):1570–1583. doi:10.1016/j.cor.2009.11.015
Marinakis Y, Marinaki M, Migdalas A (2016) A hybrid discrete artificial bee colony algorithm for the multicast routing problem. In: Squillero G, Burelli P (eds) Applications of evolutionary computation: 19th European conference, EvoApplications 2016, Porto, Portugal, March 30–April 1, 2016, Proceedings, Part I. Springer, Cham, pp 203–218
Niu JW, Zhong WM, Liang Y, Luo N, Qian F (2015) Fruit fly optimization algorithm based on differential evolution and its application on gasification process operation optimization. Knowl-Based Syst 88:253–263. doi:10.1016/j.knosys.2015.07.027
Omran M (2007) SPSO 2007 Matlab. Retrieved from http://www.particleswarm.info/Programs.html
Palit S, Sinha SN, Molla MA, Khanra A, Kule M (2011). A cryptanalytic attack on the knapsack cryptosystem using binary Firefly algorithm. Paper presented at the 2011 2nd international conference on computer and communication technology (ICCCT-2011)
Pan WT (2012) A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl-Based Syst 26:69–74. doi:10.1016/j.knosys.2011.07.001
Pan QK, Sang HY, Duan JH, Gao L (2014) An improved fruit fly optimization algorithm for continuous function optimization problems. Knowl-Based Syst 62:69–83. doi:10.1016/j.knosys.2014.02.021
Panda S, Sahu BK, Mohanty PK (2012) Design and performance analysis of PID controller for an automatic voltage regulator system using simplified particle swarm optimization. J Frankl Inst Eng Appl Math 349(8):2609–2625. doi:10.1016/j.jfranklin.2012.06.008
Patwardhan AP, Patidar R, George NV (2014) On a cuckoo search optimization approach towards feedback system identification. Digit Signal Process 32:156–163. doi:10.1016/j.dsp.2014.05.008
Piechocki J, Ambroziak D, Palkowski A, Redlarski G (2014) Use of modified cuckoo search algorithm in the design process of integrated power systems for modern and energy self-sufficient farms. Appl Energy 114:901–908. doi:10.1016/j.apenergy.2013.07.057
Shan D, Cao GH, Dong HJ (2013) LGMS-FOA: an improved fruit fly optimization algorithm for solving optimization problems. Math Probl Eng. doi:10.1155/2013/108768
Vastrakar NK, Padhy PK (2013) Simplified PSO PI-PD controller for unstable processes. In: Fourth international conference on intelligent systems, modelling and simulation (ISMS 2013), pp 350-354. doi:10.1109/Isms.2013.133
Xing YF (2013) Design and optimization of key control characteristics based on improved fruit fly optimization algorithm. Kybernetes 42(3):466–481. doi:10.1108/03684921311323699
Xu BL, Chen QL, Zhu JH, Wang ZQ (2010) Ant estimator with application to target tracking. Signal Process 90(5):1496–1509. doi:10.1016/j.sigpro.2009.10.020
Yang XS (2010) Firefly algorithm, stochastic test functions and design optimisation. Int J Bio-Inspired Comput 2(2):78–84
Yang XS, Deb S (2009) Cuckoo search via Levey flights. In: 2009 World congress on nature and biologically inspired computing (NaBIC 2009), pp 210–214. doi:10.1109/Nabic.2009.5393690
Yang XS, Hosseini SSS, Gandomi AH (2012) Firefly algorithm for solving non-convex economic dispatch problems with valve loading effect. Appl Soft Comput 12(3):1180–1186. doi:10.1016/j.asoc.2011.09.017
Yavuz G, Aydin D (2016) Angle modulated artificial bee colony algorithms for feature selection. Appl Comput Intell Soft Comput. doi:10.1155/2016/9569161
You XM, Liu S, Wang YM (2010) Quantum dynamic mechanism-based parallel ant colony optimization algorithm. Int J Comput Intell Syst 3:101–113
Yuan XF, Dai XS, Zhao JY, He Q (2014) On a novel multi-swarm fruit fly optimization algorithm and its application. Appl Math Comput 233:260–271. doi:10.1016/j.amc.2014.02.005
Yuan XF, Liu YM, Xiang YZ, Yan XG (2015) Parameter identification of BIPT system using chaotic-enhanced fruit fly optimization algorithm. Appl Math Comput 268:1267–1281. doi:10.1016/j.amc.2015.07.030
Zhang Y, Gong DW, Ding ZH (2012) A bare-bones multi-objective particle swarm optimization algorithm for environmental/economic dispatch. Inf Sci 192:213–227. doi:10.1016/j.ins.2011.06.004
Zheng XL, Wang L, Wang SY (2014) A novel fruit fly optimization algorithm for the semiconductor final testing scheduling problem. Knowl-Based Syst 57:95–103. doi:10.1016/j.knosys.2013.12.011
Acknowledgements
The authors wish to thank the Scientific Project Coordinatorship at Selcuk University and the Scientific and Technological Research Council of Turkey for their institutional supports
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Informed consent
Informed consent was obtained from all authors included in the study. This manuscript does not contain any studies with human participants or animals performed by any of the authors.
Additional information
Communicated by V. Loia.
Rights and permissions
About this article
Cite this article
Babalık, A., İşcan, H., Babaoğlu, İ. et al. An improvement in fruit fly optimization algorithm by using sign parameters. Soft Comput 22, 7587–7603 (2018). https://doi.org/10.1007/s00500-017-2733-1
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-017-2733-1