Heuristic optimization is used to tune parameters in various scientific fields. Therefore, a successful optimization algorithm that must be able to evolve in a parsimonious manner in many situations is necessary. Often, heuristic optimization algorithms are inspired by nature but imperialist competitive algorithm, inspired by the laws and policies governing human society, was presented in recent decade. Imperialist competitive algorithm was applied in various fields. This algorithm had a very good performance compared to other optimization algorithms. For this reason, this study tries to modify imperialist competitive algorithm for improve the accuracy and performance of the algorithm. Assimilation operator of the imperialist competitive algorithm is modified. The main motivation of this work is to introduce a powerful heuristic optimization algorithm. A comparison between the proposed imperialist competitive algorithm framework and several versions imperialist competitive algorithm on 6 standard numerical benchmarks and four famous optimization algorithm indicate that the proposed algorithm has a good performance on a wide variety of problems.
HollandJ.H., Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, U Michigan Press, 1975.
2.
KennedyJ. and EberhartR., Particle swarm optimization, in: Proceedings of IEEE International Conference on Neural Networks4(2) (November 1995), 1942-1948.
3.
DorigoM. and GambardellaL.M., Ant colony system: A cooperative learning approach to the traveling salesman problem, Evolutionary Computation, IEEE Transactions on1(1) (1997), 53-66.
4.
StornR. and PriceK., Differential Evolution-A Simple and Efficient Adaptive Scheme for Global Optimization Over Continuous Spaces, Berkeley: ICSI, 1995.
5.
WebsterB. and BernhardP.J., A local search optimization algorithm based on natural principles of gravitation, 2003, http: //hdl.handle.net/11141/117.
6.
YangX.S., Firefly algorithms for multimodal optimization, in: Stochastic Algorithms: Foundations and Applications, Springer Berlin Heidelberg, (2009), 169-178, doi: 10.1007/978-3-642-04944-6_14.
7.
Atashpaz-GargariE. and LucasC., Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition, in: Evolutionary Computation, 2007, CEC 2007, IEEE Congress on, IEEE, (September 2007), 4661-4667. doi: 10.1109/CEC.2007.4425083.
8.
KavehA., Advances in Metaheuristic Algorithms for Optimal Design of Structures, ch 11, Springer International Pu, 2014, pp. 349-355. doi: 10.1007/978-3-319-05549-7.
9.
EsmaeilzadehM., A modified colonial competitive algorithm for optimizing convex functions, International Journal of Intelligent Computing and Cybernetics6(4) (2013), 370-385. doi: 10.1108/IJICC-02-2013-0006.
10.
Atashpaz-GargariE., HashemzadehF. and LucasC., Designing MIMO PID controller using colonial competitive algorithm: Applied to distillation column process, in: Evolutionary Computation, 2008, CEC 2008, (IEEE World Congress on Computational Intelligence), IEEE Congress on, IEEE, (June 2008), 1929-1934. doi: 10.1109/CEC.2008.4631052.
11.
NematiK., ShamsuddinS.M. and DarusM., An optimization technique based on imperialist competition algorithm to measurement of error for solving initial and boundary value problems, Measurement48 (2014), 96-108. doi: 10.1016/j.measurement2013.10.043.
12.
ZarandiM.H., ZarinbalM., GhanbariN. and TurksenI.B., A new fuzzy functions model tuned by hybridizing imperialist competitive algorithm and simulated annealing, Application: Stock Price Prediction Information Sciences222 (2013), 213-228. doi: 10.1016/j.ins.2012.08.002..
13.
NiknamT., Taherian FardE., PourjafarianN. and RoustaA., An efficient hybrid algorithm based on modified imperialist competitive algorithm and k-means for data clustering, Engineering Applications of Artificial Intelligence24(2) (2011), 306-317. doi: 10.1016/j.engappai.2010.10.001.
14.
KayvanfarV. and ZandiehM., The economic lot scheduling problem with deteriorating items and shortage: An imperialist competitive algorithm, The International Journal of Advanced Manufacturing Technology (2012), 759-773. doi: 10.1007/s00170-011-3820-6.
15.
KarimiN., ZandiehM. and NajafiA.A., Group scheduling in flexible flow shops: A hybridised approach of imperialist competitive algorithm and electromagnetic-like mechanism, International Journal of Production Research49(16) (2011), 4965-4977. doi: 10.1080/00207543.2010.481644.
16.
Mohammadi-ivatlooB., RabieeA., SoroudiA. and EhsanM., Imperialist competitive algorithm for solving non-convex dynamic economic power dispatch, Energy44(1) (2012), 228-240. doi: 10.1016/j.energy.2012.06.034.
17.
AhmadiM.A., EbadiM., ShokrollahiA. and MajidiS.M.J., Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir, Applied Soft Computing13(2) (2013), 1085-1098. doi: 10.1016/jasoc.2012.10.009..
18.
MousaviS.M., Tavakkoli-MoghaddamR., VahdaniB., HashemiH. and SanjariM.J., A new support vector model-based imperialist competitive algorithm for time estimation in new product development projects, Robotics and Computer-Integrated Manufacturing29(1) (2013), 157-168. doi: 10.1016/j.rcim.2012.04.006.
19.
CoelhoL.D.S., AfonsoL.D. and AlottoP., A modified imperialist competitive algorithm for optimization in electromagnetics, Magnetics IEEE Transactions on48(2) (2012), 579-582. doi: 10.1109/TMAG.2011.2172400.
20.
LucasC., Nasiri-GheidariZ. and TootoonchianF., Application of an imperialist competitive algorithm to the design of a linear induction motor, Energy Conversion and Management51(7) (2010), 1407-1411. doi: 10.1016/j.enconman.2010.01.014.
21.
KavehA. and TalatahariS., Optimum design of skeletal structures using imperialist competitive algorithm, Computers & Structures88(21) (2010), 1220-1229.
22.
XuJ. and ManH., Dictionary learning based on laplacian score in sparse coding, in: Machine Learning and Data Mining in Pattern Recognition, Springer Berlin Heidelberg, (2011), 253-264.
23.
LiuJ.Y.C., SuC. and ChiuC.T., On the convergence of imperialist competitive algorithm, in: Modelling Symposium (AMS), 2013 7th Asia, IEEE (July 2013), 16-20. doi: 10.1109/AMS.2013.9.
24.
XingB. and GaoW.J., Imperialist competitive algorithm, in: Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms, Springer International Publishing (2014), 203-209. doi: 10.1007/978-3-319-03404-1_15.
25.
AfonsoL.D., MarianiV.C. and Dos Santos CoelhoL., Modified imperialist competitive algorithm based on attraction and repulsion concepts for reliability-redundancy optimization, Expert Systems with Applications40(9) (2013), 3794-3802. doi: 10.1016/j.eswa.2012.12.093.
26.
SadighA.N., MozafariM. and KarimiB., Manufacturer-retailer supply chain coordination: A bi-level programming approach, Advances in Engineering Software45(1) (2012), 144-152. doi: 10.1016/j.advengsoft.2011.09.008.
27.
DuanH., XuC., LiuS. and ShaoS., Template matching using chaotic imperialist competitive algorithm, Pattern Recognition Letters31(13) (2010), 1868-1875. doi: 10.1016/j.patrec.200912.005.
28.
BehnamianJ. and ZandiehM., A discrete colonial competitive algorithm for hybrid flowshop scheduling to minimize earliness and quadratic tardiness penalties, Expert Systems with Applications38(12) (2011), 14490-14498. doi: 10.1016/jeswa.2011.04.241.
29.
RamezaniF., LotfiS. and Soltani-SarvestaniM.A., A hybrid evolutionary imperialist competitive algorithm (HEICA), in: Intelligent Information and Database Systems, Springer Berlin Heidelberg, (2012), 359-368. doi: 10.1007/978-3-642-28487-8_37.
30.
LepagnotJ., IdoumgharL. and FodoreanD., Hybrid imperialist competitive algorithm with simplex approach: Application to electric motor design, in: Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on, IEEE (October 2013), 2454-2459. doi: 10.1109/SMC.2013.419.
31.
GoldansazS.M., JolaiF. and Zahedi AnarakiA.H., A hybrid imperialist competitive algorithm for minimizing makespan in a multi-processor open shop, Applied Mathematical Modelling37(23) (2013), 9603-9616. doi: 10.1016/j.apm.2013.05002.
32.
JulaA., OthmanZ. and SundararajanE., A hybrid imperialist competitive-gravitational attraction search algorithm to optimize cloud service composition, in: Memetic Computing (MC), 2013 IEEE Workshop on, IEEE (April 2013), 37-43. doi: 10.1109/MC.2013.6608205.
33.
YaoX., LiuY. and LinG., Evolutionary programming made faster, Evolutionary Computation IEEE Transactions on3(2) (1999), 82-102.
34.
KunduR., DasS., MukherjeeR. and DebchoudhuryS., an improved particle swarm optimizer with difference mean based perturbation, Neuro Computing129 (2014), 315-333. doi: 10.1016/jneucom.2013.09.026.
35.
WilcoxonF., Individual comparisons by ranking methods, Biometrics Bulletin, 1945, pp. 80-83.
36.
ZhangJ. and SandersonA.C., JADE: Adaptive differential evolution with optional external archive, Evolutionary Computation IEEE Transactions on13(5) (2009), 945-958. doi: 10.1109/TEVC.2009.2014613.
37.
GaoW. and LiuS., Improved artificial bee colony algorithm for global optimization, Information Processing Letters111(17) (2011), 871-882. doi: 10.1016/j.ipl.2011.06.002.
38.
XuJ., HeH. and ManH., DCPE co-training for classification, Neurocomputing86 (2012), 75-85.
39.
GordonV.S. and WhitleyD., Serial and parallel genetic algorithms as function optimizers, Fifth International Conference on Genetic Algorithms, Morgan Kaufmann (1993), 177-183.
40.
KarabogaD. and BasturkB., A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm, Journal of Global Optimization39(3) (2007), 459-471.
