Abstract
Computation is concerned with the validation of algorithm, estimation of complexity and optimization. This requires large dataset analysis for the purpose of finding the unknown optimal solution. Aside the intricacies in completing tasks, this process is expensive and time inefficient; attaining solutions with conventional mathematical approaches are unrealizable. Search algorithms were advanced to improve solution approaches for optimization problems by finding the possible sets of solution to a particular problem as contained in search space. However, metaheuristic algorithms suggest three solutions to optimization problems on the basis of the application areas in real-life situations including: near optima, the optimal or the best solution. This paper analyses the decision-making processes of two nature-inspired search algorithms namely: Backpropagation search algorithm and ant colony optimization (ACO). The results revealed that, backpropagation search algorithm without ACO training trailed those trained with ACO for MSE, RMSE, RAE and MAPE. Again, forecasts errors estimated in the neural network set-up were smaller due to directional search mechanism of the ACO as against the approach provided in neuro-fuzzy rules set tuning by Rajab and Sharma (Soft Comput 23:921–936, 2017) [1]. There is need to consider metaheuristic algorithms approaches to obtain better solutions or nearest optimal values to the optimization problems in neural networks.
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References
Rajab, S., Sharma, V.: An interpretable neuro-fuzzy approach to stock price forecasting. Soft. Comput. 23(3), 921–936 (2017)
Baghel, M., Agrawal, S., Silakari, S.: Survey of metaheuristic algorithms. For combinatorial optimization. Int. J. Comput. Appl. 58(19), 21–31 (2012)
Khan, Z.H., Alin, T.S., Hussain, A.: Price prediction of share market using ANN. Comput. Expert Syst. Appl. 38(1), 9196–9206 (2011)
Turanoglu, E., Ozceylan, E., Kiran, M.S.: Particle swarm optimization and artificial bee colony approaches to optimize of single input-output fuzzy membership functions. In: Proceedings of the 41st International Conference on Computers & Industrial Engineering, pp. 542–548 (2012)
Pal, A., Chakraborty, D.: Prediction of stock exchange share price using ANN and PSO. Int. J. Eng. Sci. 80(1), 62–70 (2014)
Vyas, S., Sanadhya, S.: A survey of ant colony optimization with social network. Int. J. Comput. Appl. 107(9), 17–21 (2014)
Said, G.A., Mahmoud, A.M., El-Horbaty, E.M.: A comparative study of meta-heuristic algorithms for solving quadratuc assignment problem. Int. J. Adv. Comput. Sci. Appl. 5(1), 1–6 (2014)
Okewu, E., Misra, S.: Applying metaheuristic algorithm to the admission problem as a combinatorial optimisation problem. Front. Artif. Intell. Appl. Adv. Digit. Technol. 282, 53–64 (2016)
Crawford, B., Soto, R., Johnson, F., Vargas, M., Misra, S., Paredes, F.: A scheduling problem for software project solved with ABC metaheuristic. ICCSA 4, 628–639 (2015)
Crawford, B., Soto, R., Peña, C., Riquelme-Leiva, M., Torres-Rojas, C., Misra, S., Johnson, F., Paredes, F.: A comparison of three recent nature-inspired metaheuristics for the set covering problem. ICCSA 4, 431–443 (2015)
Crawford, B., Soto, R., Johnson, F., Vargas, M., Misra, S., Paredes, F.: The use of metaheuristics to software project scheduling problem. ICCSA, Part V, LNCS 8583, 215–226 (2014)
Alazzam, A., Lewis, H.W.: A new optimization algorithm for combinatorial problems. Int. J. Adv. Res. Artif. Intell. 2(5), 63–68 (2013)
Darquennes, D.: Implementation and applications of ant colony algorithms. A master thesis, Department of Information Technology, University of Namur, Belgium, pp. 1–101 (2005)
Patel, M.K., Kabat, M.R., Tripathy, C.R.: A hybrid ACO/PSO based algorithm for QoS multicast routing problem. Ain Shams Eng. J. 2(1), 113–120 (2014)
Moustafa, A.A.: Performance evaluation of artificial neural networks for spatial data analysis. Contemp. Eng. Sci. 4(4), 149–163 (2011)
Chakraborty, R.: Fundamentals of Neural Networks: Soft Computing Course Lecture Notes. Department of Computer Science, Indian Institute of Technology, Madras, pp. 7–14 (2010)
Su, Y.: An investigation of continuous learning in incomplete environments. A Ph.D. thesis. University of Nottingham, UK, pp. 1–180 (2005)
Gholizadeh, S., Fattahi, F.: Serial integration of particle swarm and ant colony algorithms for structural optimization. Asian J. Civ. Eng. (Build. Hous.) 13(1), 127–146 (2012)
Surekha, P.: Solving fuzzy based job shop scheduling problems using GA and ACO. J. Emerg. Trends Comput. Inf. Sci. 1, 95–102 (2010)
Hlaing, S.Z.S., Khine, M.A.: An ant colony optimization algorithm for solving traveling salesman problem. In: International Conference on Information Communication and Management, vol. 16, pp. 54–59 (2011)
Bin, A.Y., Zhong-Zhen, Y., Baozhen, Y.: An improved ant colony optimization for vehicle routing problem. Eur. J. Oper. Res. 196, 171–176 (2009)
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We would like to acknowledge the sponsorship and support provided by Covenant University through the Centre for Research, Innovation, and Discovery (CUCRID).
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Alfa, A.A., Misra, S., Ahmed, K.B., Arogundade, O., Ahuja, R. (2020). Metaheuristic-Based Intelligent Solutions Searching Algorithms of Ant Colony Optimization and Backpropagation in Neural Networks. In: Singh, P., Pawłowski, W., Tanwar, S., Kumar, N., Rodrigues, J., Obaidat, M. (eds) Proceedings of First International Conference on Computing, Communications, and Cyber-Security (IC4S 2019). Lecture Notes in Networks and Systems, vol 121. Springer, Singapore. https://doi.org/10.1007/978-981-15-3369-3_8
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