[2] B. Tutuko, “Fuzzy Logic-Ant Colony Optimization for Explorer-Follower Robot with Global Optimal Path Planning,” Comput. Eng. Appl. J. 7, vol. 7, 2018.
[3] N. L. Husni, “Swarm Robot Implementation in Gas Searching Using Particle Swarm Optimization Algorithm,” Comput. Eng. Appl. J., vol. 6, no. 3, 2017.
[4] M. Neshat, G. Sepidnam, M. Sargolzaei, and A. N. Toosi, “Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications,” Artif. Intell. Rev., vol. 42, no. 4, pp. 965–997, 2014.
[5] S. Nurmaini and S. Z. M. Hashim, “Swarm Robots Control System based,” no. August, pp. 20–21, 2014.
[6] I. Navarro and F. Matía, “An Introduction to Swarm Robotics,” ISRN Robot., vol. 2013, pp. 1–10, 2013.
[7] S. Mirjalili, S. M. Mirjalili, A. Lewis, G. W. Optimizer, S. Mirjalili, S. M. Mirjalili, A. Lewis, C. Technology, and S. Beheshti, “Grey Wolf Optimizer 1 1,” vol. 69, pp. 46–61, 2014.
[8] D. Karaboga, B. Gorkemli, C. Ozturk, and N. Karaboga, “A comprehensive survey: Artificial bee colony (ABC) algorithm and applications,” Artif. Intell. Rev., vol. 42, no. 1, pp. 21–57, 2014.
[9] S. Zhang, C. K. M. Lee, H. K. Chan, K. L. Choy, and Z. Wu, “Swarm intelligence applied in green logistics: A literature review,” Eng. Appl. Artif. Intell., vol. 37, pp. 154–169, 2015.
[10] B. G. Galef and L. A. Giraldeau, “Social influences on foraging in vertebrates: Causal mechanisms and adaptive functions,” Anim. Behav., vol. 61, no. 1, pp. 3–15, 2001.
[11] A. Deshpande, M. Kumar, and S. Ramakrishnan, “Robot Swarm for Efficient Area Coverage Inspired By Ant Foraging - The Case of Adaptive Switching Between Brownian motion and Levy Flight,” Proc. ASME 2017 Dyn. Syst. Control Conf., pp. 1–8, 2017.
[12] H. Verlekar and K. Joshi, “Ant & bee inspired foraging swarm robots using computer vision,” Int. Conf. Electr. Electron. Commun. Comput. Technol. Optim. Tech. ICEECCOT 2017, vol. 2018–January, pp. 191–195, 2018.
[13] A. Sáez, C. L. Morales, L. A. Garibaldi, and M. A. Aizen, “Invasive bumble bees reduce nectar availability for honey bees by robbing raspberry flower buds,” Basic Appl. Ecol., vol. 19, pp. 26–35, 2017.
[14] M. Dorigo, M. Birattari, and T. Stutzle, “Ant colony optimization Artificial Ants as a Computational Intelligence Technique,” IEEE Comput. Intell. Mag., vol. 1, no. 4, pp. 28–39, 2006.
[15] R. Jeanson, C. Rivault, J. L. Deneubourg, S. Blanco, R. Fournier, C. Jost, and G. Theraulaz, “Self-organized aggregation in cockroaches,” Anim. Behav., vol. 69, no. 1, pp. 169–180, 2005.
[16] A. Mogilner, L. Edelstein-Keshet, L. Bent, and A. Spiros, Mutual interactions, potentials, and individual distance in a social aggregation, vol. 47, no. 4. 2003.
[17] J. Kennedy and R. Eberhart, “Particle swarm optimization,” Neural Networks, 1995. Proceedings., IEEE Int. Conf., vol. 4, pp. 1942–1948 vol.4, 1995.
[18] L. Xiao-lei, L. Fei, T. Guo-hui, and Q. Ji-xin, “Applications of artificial fish school algorithm in combinatorial optimization problems.pdf,” J. Shandong Univ. (Engineering Sci., vol. 05, 2005.
[19] E. Rashedi and H. N. Saeid, “BGSA : binary gravitational search algorithm,” no. December 2009, pp. 727–745, 2010.
[20] E. Rashedi, H. Nezamabadi-pour, and S. Saryazdi, “GSA : A Gravitational Search Algorithm,” Inf. Sci. (Ny)., vol. 179, no. 13, pp. 2232–2248, 2009.
[21] X. Yang, “Stochastic Algorithms: Foundations and Applications,” vol. 5792, no. May, 2009.
[22] X. Yang, “Nature Inspired Cooperative Strategies for Optimization (NICSO 2010),” vol. 284, no. March 2014, 2010.
[23] X. Yang, Firefly Algorithm. 2010.
[24] K. N. Krishnanand and D. Ghose, “Detection of multiple source locations using a glowworm metaphor with applications to collective robotics,” Proc. - 2005 IEEE Swarm Intell. Symp. SIS 2005, vol. 2005, pp. 87–94, 2005.
[25] E. Emary, H. M. Zawbaa, and A. E. Hassanien, “Binary grey wolf optimization approaches for feature selection,” Neurocomputing, vol. 172, pp. 371–381, 2016.
[26] E. K. V. Kalko, H. U. Schnitzler, I. Kaipf, and A. D. Grinnell, “Echolocation and foraging behavior of the lesser bulldog bat, Noctilio albiventris: Preadaptations for piscivory?,” Behav. Ecol. Sociobiol., vol. 42, no. 5, pp. 305–319, 1998.
[27] B. M. Siemers, E. K. V. Kalko, and H.-U. Schnitzler., “Echolocation and foraging behavior of the lesser bulldog bat, Noctilio albiventris: Preadaptations for piscivory?,” Behav. Ecol. Sociobiol., p. 50: 317-328, 2001.
[28] X. Yang and A. Hossein Gandomi, “Bat algorithm: a novel approach for global engineering optimization,” Eng. Comput., vol. 29, no. 5, pp. 464–483, 2012.
[29] X. Yang, “A New Metaheuristic Bat-Inspired Algorithm,” vol. 284, no. April 2010, 2010.
[30] X. Yang, “Bat Algorithm : Literature Review and Applications,” pp. 1–10, 2013.
[31] K. M. Passino, “Biomimicry of Bacterial Foraging for Distributed Optimization and Control,” Control Syst. IEEE, vol. 22, no. 3, pp. 52–67, 2002.
[32] R. L. Buchanan, R. C. Whiting, and W. C. Damert, “When is simple good enough : a comparison of the Gompertz , Baranyi , and three-phase linear models for fitting bacterial growth curves 1,” pp. 313–326, 1997.
[33] A. Askarzadeh, “Commun Nonlinear Sci Numer Simulat Bird mating optimizer : An optimization algorithm inspired by bird mating strategies,” Commun. Nonlinear Sci. Numer. Simul., vol. 19, no. 4, pp. 1213–1228, 2014.
[34] S. Yang, J. Jiang, and G. Yan, “A dolphin partner optimization,” vol. 1, pp. 124–128, 2009.
[35] A. R. Kammerdiner, A. Mucherino, and P. M. Pardalos., “Application of Monkey Search Meta-heuristic to Solving Instances of the Multidimensional Assignment Problem,” Optim. Coop. Ctrl. Strateg. LNCIS 381, pp. 385–397, 1988.
[36] X. Yang and S. Deb, “Engineering Optimisation by Cuckoo Search,” pp. 1–17, 2009.
[37] A. H. Gandomi and A. H. Alavi, “Krill herd: A new bio-inspired optimization algorithm,” Commun. Nonlinear Sci. Numer. Simul., vol. 17, no. 12, pp. 4831–4845, 2012.
[38] A. H. Gandomi and A. H. Alavi, “Lagrangian model of the krill herding Commun Nonlinear Sci Numer Simulat,” vol. 17, p. 4831–4845 4833, 2012.
[39] F. J. . Garcia and J. A. . Perez, “Jumping Frogs Optimization: A New Swarm Method for discrete optimization,” Doc. Trab. del DEIOC, vol. 3, p. 10, 2008.
[40] M. Dorigo, V. Maniezzo, and A. Colorni, “Positive Feedback as a Search Strategy,” Citeceer, 1991.
[41] J. Kennedy and R. Eberhart, “Particle swarm optimization,” Proc. ICNN’95 - Int. Conf. Neural Networks, vol. 4, pp. 1942–1948, 1995.
[42] S. Larabi Marie-Sainte, “A survey of Particle Swarm Optimization techniques for solving university Examination Timetabling Problem,” Artif. Intell. Rev., vol. 44, no. 4, pp. 537–546, 2015.
[43] A. Banks, J. Vincent, and C. Anyakoha, “A review of particle swarm optimization. Part II: Hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications,” Nat. Comput., vol. 7, no. 1, pp. 109–124, 2008.
[44] W. Jatmiko, F. Jovan, R. Y. S. Dhiemas, a M. Sakti, F. M. Ivan, a Febrian, T. Fukuda, and K. Sekiyama, “Robots implementation for odor source localization using PSO algorithm,” WSEAS Trans. Circuits Syst., vol. 10, no. 4, pp. 115–125, 2011.
[45] M. Reed, A. Riannakou, and R. Evering, “An ant colony algorithm for the multi-compartment vehicle routing problem,” Appl. Soft Comput., vol. 15, no. February, pp. 169–176, 2014.
[46] S. R. Balseiro, I. Loiseau, and J. Ramonet, “An Ant Colony algorithm hybridized with insertion heuristics for the Time Dependent Vehicle Routing Problem with Time Windows,” Comput. Oper. Res., vol. 38, no. 6, June, pp. 954–966, 2011.
[47] S. Ghafurian and N. Javadian, “An ant colony algorithm for solving fixed destination multi-depot multiple traveling salesmen problems,” vol. 11, pp. 1256–1262, 2011.
[48] Y. Eroglu and S. U. Seckiner, “Design of wind farm layout using ant colony algorithm,” Renew. Energy, vol. 44, no. August, pp. 53–62, 2012.
[49] M. S. Kiran, E. Ozceylan, M. Gunduz, and T. Paksoy, “A novel hybrid approach based on Particle Swarm Optimization and Ant Colony Algorithm to forecast energy demand of Turkey,” Energy Convers. Manag., vol. 53, no. 1, January, pp. 75–83, 2012.
[50] K. Ishaque and Z. Salam, “Maximum Power Point Tracker for Photovoltaic System,” Ind. Electron. IEEE Trans., vol. 60, no. 8, pp. 3195–3206, 2012.
[51] M. H. Moradi and M. Abedini, “A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems,” Int. J. Electr. Power Energy Syst., vol. 34, no. 1, January, pp. 66–74, 2012.
[52] G. Moslehi and M. Mahnam, “A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search,” Int. J. Prod. Econ., vol. 129, no. 1, January, pp. 14–22, 2011.
[53] Y. Zhang, D. W. Gong, and Z. Ding, “A bare-bones multi-objective particle swarm optimization algorithm for environmental/economic dispatch,” Inf. Sci. (Ny)., vol. 192, no. 1 June, pp. 213–227, 2012.
[54] A. Askarzadeha and L. Dos Santos Coelhob, “Using two improved particle swarm optimization variants for optimization of daily electrical power consumption in multi-chiller systems,” Appl. Therm. Eng., vol. 89, no. 5 October, pp. 640–646, 2015.
[55] W. P. Xiaifeng Lv, Ling Ma, Jing Sun, “Test Point Selection Method Research Based on Genetic Algorithm and Binary Particle Swarm Optimization Algorithm,” Proc. Second Int. Conf. Mechatronics Autom. Control, vol. 334, no. of the series Lecture Notes in Electrical Engineering, pp. 577–585, 2015.
[56] J. Agrawal and S. Agrawal, “Acceleration based Particle Swarm Optimization (APSO) for RNA Secondary Structure Prediction,” Prog. Syst. Eng., vol. 330, no. of the series Advances in Intellegent System and Computing, pp. 741–746, 2014.
[57] E. Hancera, B. Xue, D. Karaboga, and M. Zhang, “A binary ABC algorithm based on advanced similarity scheme for feature selection,” Appl. Soft Comput., vol. 36, no. November, pp. 334–348, 2015.
[58] A. Moayedikia, R. Jensen, U. K. Wiil, and R. Forsati, “Weighted bee colony algorithm for discrete optimization problems with application to feature selection,” Eng. Appl. Artif. Intell., vol. 44, no. September, pp. 153–167, 2015.
[59] C. Ozturk, E. Hancer, and D. Karaboga, “Dynamic clustering with improved binary artificial bee colony algorithm,” Appl. Soft Comput., vol. 28, no. March, pp. 69–80, 2015.
[60] R. Roy and H. T. Jadhav, “Optimal power flow solution of power system incorporating stochastic wind power using Gbest guided artificial bee colony algorithm,” Int. J. Electr. Power Energy Syst., vol. 64, no. January, pp. 562–578, 2015.
[61] I. Aydotdu, A. Akin, and M. P. Saka, “Design optimization of real world steel space frames using artificial bee colony algorithm with Levy flight distribution,” Adv. Eng. Softw., vol. 92, no. February, pp. 1–14, 2016.
[62] L. Wang, Y. Shi, and S. Liu, “An improved fruit fly optimization algorithm and its application to joint replenishment problems,” Expert Syst. Appl., vol. 42, no. 9, 1 June, pp. 4310–4323, 2015.
[63] S. M. Mousavi, N. Alikar, S. T. A. Niaki, and A. Bahreininejad, “Optimizing a location allocation-inventory problem in a two-echelon supply chain network: A modified fruit fly optimization algorithm,” Comput. Ind. Eng., vol. 87, no. September, pp. 543–560, 2015.
[64] J. Niu, W. Zhong, Y. Liang, N. Luo, and F. Qian, “Fruit fly optimization algorithm based on differential evolution and its application on gasification process operation optimization,” Knowledge-Based Syst., vol. 88, no. November, pp. 253–263, 2015.
[65] X. L. Zheng, L. Wang, and S. Y. Wang, “A novel fruit fly optimization algorithm for the semiconductor final testing scheduling problem,” Knowledge-Based Syst., vol. 57, no. February, pp. 95–103, 2014.
[66] S. Duman, U. Güvenç, Y. Sönmez, and N. Yörükeren, “Optimal power flow using gravitational search algorithm,” Energy Convers. Manag., vol. 59, no. July, pp. 86–95, 2012.
[67] B. González, F. Valdez, P. Melin, and G. Prado-Arechiga, “Fuzzy logic in the gravitational search algorithm for the optimization of modular neural networks in pattern recognition,” Expert Syst. Appl., vol. 42, no. 14, 15 August, pp. 5839–5847, 2015.
[68] N. Gouthamkumar, V. Sharma, and R. Naresh, “Disruption based gravitational search algorithm for short term hydrothermal scheduling,” Expert Syst. Appl., vol. 42, no. 20, November, pp. 7000–7011, 2015.
[69] M. Rezaei and H. Nezamabadi-pour, “Using gravitational search algorithm in prototype generation for nearest neighbor classification Neurocomputing,” Neurocomputing, vol. 157, no. 1 June, pp. 256–263, 2015.
[70] N. Saxena and S. Ganguli, “Solar and Wind Power Estimation and Economic Load Dispatch Using Firefly Algorithm,” Procedia Comput. Sci., vol. 70, pp. 688–700, 2015.
[71] A. Baykasoglu and F. B. Ozsoydan, “Adaptive firefly algorithm with chaos for mechanical design optimization problems,” Appl. Soft Comput., vol. 36, no. November, pp. 152–164, 2015.
[72] G. T. Chandra Sekhar, R. K. Sahu, A. K. Baliarsingh, and S. Panda, “Load frequency control of power system under deregulated environment using optimal firefly algorithm,” Int. J. Electr. Power Energy Syst., vol. 74, no. January, pp. 195–211, 2016.
[73] X. Lei, F. Wang, F. X. Wu, A. Zhang, and W. Pedrycz, “Protein complex identification through Markov clustering with firefly algorithm on dynamic protein-protein interaction networks,” Inf. Sci. (Ny)., vol. 329, no. 1 February, pp. 303–316, 2016.
[74] M. Marinaki and Y. Marinakis, “A Glowworm Swarm Optimization algorithm for the Vehicle Routing Problem with Stochastic Demands,” Expert Syst. Appl., vol. 46, no. 15 March, pp. 145–163, 2016.
[75] V. Yepes, J. V. Marti, and T. Garcia-Segura, “Cost and CO2 emission optimization of precast-prestressed concrete U-beam road bridges by a hybrid glowworm swarm algorithm,” Autom. Constr., vol. 49, no. Part A, January, pp. 123–134, 2015.
[76] J. Yan, J. Zhang, Y. Liu, S. Han, L. Li, and C. Gu, “Unit commitment in wind farms based on a glowworm metaphor algorithm,” Electr. Power Syst. Res., vol. 129, no. December, pp. 94–104, 2015.
[77] X. Song, L. Tang, S. Zhao, X. Zhang, L. Li, J. Huang, and W. Cai, “Grey Wolf Optimizer for parameter estimation in surface waves,” Soil Dyn. Earthq. Eng., vol. 75, pp. 147–157, 2015.
[78] S. A. Medjahed, T. Ait Saadi, A. Benyettou, and M. Ouali, “Gray Wolf Optimizer for hyperspectral band selection,” Appl. Soft Comput., vol. 40, no. 28 November, pp. 178–186, 2015.
[79] M. H. Sulaiman, Z. Mustaffa, M. R. Mohamed, and O. Aliman, “Using the gray wolf optimizer for solving optimal reactive power dispatch problem,” Appl. Soft Comput., vol. 32, no. July, pp. 286–292, 2015.
[80] S. M. Abd-Elazim and E. S. Ali, “Load frequency controller design via BAT algorithm for nonlinear interconnected power system,” Int. J. Electr. Power Energy Syst., vol. 77, no. May, pp. 166–177, 2016.
[81] I. Fister, S. Rauterb, X. S. Yang, K. Ljubicd, and I. Fister Jr, “Planning the sports training sessions with the bat algorithm,” Neurocomputing, vol. 149, no. Part B, 3 February, pp. 993–1002, 2015.
[82] K. Premkumar and B. V. Manikandan, “Speed control of Brushless DC motor using bat algorithm optimized Adaptive Neuro-Fuzzy Inference System,” Appl. Soft Comput., vol. 32, no. July, pp. 403–419, 2015.
[83] A. Panda and M. Tripathy, “Security constrained optimal power flow solution of wind-thermal generation system using modified bacteria foraging algorithm,” Energy, vol. 93, no. Part 1, 15 December, pp. 816–827, 2015.
[84] K. R. Devabalaji, K. Ravi, and D. P. Kothari, “Optimal location and sizing of capacitor placement in radial distribution system using Bacterial Foraging Optimization Algorithm,” Int. J. Electr. Power Energy Syst., vol. 71, no. October, pp. 383–390, 2015.
[85] A. N. K. Nasir, M. O. Tokhi, and N. M. A. Ghani, “Novel adaptive bacterial foraging algorithms for global optimisation with application to modelling of a TRS,” Expert Syst. Appl., vol. 42, no. 3, 15 February, pp. 1513–1530, 2015.
[86] Y. Chen, Q. Zhu, and H. Xu, “Finding rough set reducts with fish swarm algorithm,” Knowledge-Based Syst., vol. 81, no. June, pp. 22–29, 2015.
[87] Q. He, X. Hu, H. Ren, and H. Zhang, “A novel artificial fish swarm algorithm for solving large-scale reliability-redundancy application problem,” ISA Trans., vol. 59, pp. 105–113, 2015.
[88] A. O. Helmy, S. Ahmed, and A. E. Hassenian, “Artificial Fish Swarm Algorithm for Energy-Efficient Routing Technique,” Intell. Syst., vol. 322, no. of the series advances in Intelligent System and Computing, pp. 509–519, 2015.
[89] N. K. S. Behera, A. R. Routray, J. Nayak, H. S. Behera, and Á. Á. Multilayer, “Bird Mating Optimization Based Multilayer Perceptron for Diseases Classification,” Comput. Intell. Data Min. - Vol. 3, vol. 33, no. Of the series Smart Innovation, Systems and Technologies, pp. 305–315, 2014.
[90] A. Askarzadeh and S. Coelho, “Determination of photovoltaic modules parameters at different operating conditions using a novel bird mating optimizer approach,” ENERGY Convers. Manag., vol. 89, pp. 608–614, 2015.
[91] S. J. Huang, T. Y. Tai, X. Z. Liu, W. F. Su, and P. H. Gu, “Application of Bird-Mating Optimization to Phase Adjustment of Open-Wye/Open-Delta Transformers in a Power Grid,” Ind. Technol. (ICIT), 2015 IEEE Int. Conf., no. 17–19 March, pp. 1275–1279, 2015.
[92] A. Kaveh and N. Farhoudi, “A new optimization method: Dolphin echolocation,” Adv. Eng. Softw., vol. 59, no. May, pp. 53–70, 2013.
[93] F. G. Duque, L. W. De Oliveira, E. J. De Oliveira, A. L. M. Marcato, and I. C. Silva, “Allocation of capacitor banks in distribution systems through a modified monkey search optimization technique,” Int. J. Electr. Power Energy Syst., vol. 73, pp. 420–432, 2015.
[94] Y. Zhou, X. Chen, and G. Zhou, “An improved monkey algorithm for a 0-1 knapsack problem,” Appl. Soft Comput. J., vol. 38, pp. 817–830, 2016.
[95] H. C. Tsai, “Roach infestation optimization with friendship centers,” Eng. Appl. Artif. Intell., vol. 39, pp. 109–119, 2015.
[96] A. Y. Abdelaziz and E. S. Ali, “Cuckoo Search algorithm based load frequency controller design for nonlinear interconnected power system,” Int. J. Electr. Power Energy Syst., vol. 73, pp. 632–643, 2015.
[97] X. Sun, W. Sun, J. Wang, Y. Zhang, and Y. Gao, “Using a Grey-Markov model optimized by Cuckoo search algorithm to forecast the annual foreign tourist arrivals to China,” Tour. Manag., vol. 52, pp. 369–379, 2016.
[98] T. T. Nguyen and A. V. Truong, “Distribution network reconfiguration for power loss minimization and voltage profile improvement using cuckoo search algorithm,” Int. J. Electr. Power Energy Syst., vol. 68, pp. 233–242, 2015.
[99] “Oppositional krill herd algorithm for optimal location of capacitor with reconfiguration in radial distribution system.”
[100] S. Sultana and P. K. Roy, “Oppositional krill herd algorithm for optimal location of capacitor with reconfiguration in radial distribution system,” Int. J. Electr. Power Energy Syst., vol. 74, pp. 78–90, 2016.
[101] L. Guo, G. Wang, A. H. Gandomi, A. H. Alavi, and H. Duan, “Neurocomputing A new improved krill herd algorithm for global numerical optimization,” Neurocomputing, vol. 138, pp. 392–402, 2014.
[102] N. Afkar. and A. Babazadeh, “Hybridized Particle Swarm Optimization Algorithm: Frog Leaping Concept For Solving Transportation Network Design Problem,” pp. 647–652.
[103] M. A. Ahandani and H. Alavi-Rad, “Opposition-based learning in shuffled frog leaping: An application for parameter identification,” Inf. Sci. (Ny)., vol. 291, no. C, pp. 19–42, 2015.
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© Computer Engineering and Applications Journal, 2018
Affiliations
Nyayu Husni Latifah
Politeknik Negeri Sriwijaya
Ade Silvia
Politeknik Negeri Sriwijaya
Ekawati Prihatini
Politeknik Negeri Sriwijaya
Siti Nurmaini
Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya,
Irsyadi Yani
Mechanical Engineering Department, Faculty of Engineering, Universitas Sriwijaya