A New Optimization Algorithm Based on Search and Rescue Operations

[EN] In this paper, a new optimization algorithm called the search and rescue optimization algorithm (SAR) is proposed for solving single-objective continuous optimization problems. SAR is inspired by the explorations carried out by humans during search and rescue operations. The performance of SAR was evaluated on fifty-five optimization functions including a set of classic benchmark functions and a set of modern CEC 2013 benchmark functions from the literature. The obtained results were compared with twelve optimization algorithms including well-known optimization algorithms, recent variants of GA, DE, CMA-ES, and PSO, and recent metaheuristic algorithms. The Wilcoxon signed-rank test was used for some of the comparisons, and the convergence behavior of SAR was investigated. The statistical results indicated SAR is highly competitive with the compared algorithms. Also, in order to evaluate the application of SAR on real-world optimization problems, it was applied to three engineering design problems, and the results revealed that SAR is able to find more accurate solutions with fewer function evaluations in comparison with the other existing algorithms. Thus, the proposed algorithm can be considered an efficient optimization method for real-world optimization problems.This study was partially supported by the Spanish Research Project (nos. TIN2016-80856-R and TIN2015-65515-C4-1-R).Shabani, A.; Asgarian, B.; Gharebaghi, SA.; Salido Gregorio, MA.; Giret Boggino, AS. (2019). A New Optimization Algorithm Based on Search and Rescue Operations. Mathematical Problems in Engineering. 2019:1-23. https://doi.org/10.1155/2019/2482543S1232019Bianchi, L., Dorigo, M., Gambardella, L. M., & Gutjahr, W. J. (2008). A survey on metaheuristics for stochastic combinatorial optimization. Natural Computing, 8(2), 239-287. doi:10.1007/s11047-008-9098-4Holland, J. H. (1992). Genetic Algorithms. Scientific American, 267(1), 66-72. doi:10.1038/scientificamerican0792-66Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 26(1), 29-41. doi:10.1109/3477.484436Manjarres, D., Landa-Torres, I., Gil-Lopez, S., Del Ser, J., Bilbao, M. N., Salcedo-Sanz, S., & Geem, Z. W. (2013). A survey on applications of the harmony search algorithm. Engineering Applications of Artificial Intelligence, 26(8), 1818-1831. doi:10.1016/j.engappai.2013.05.008Karaboga, D., Gorkemli, B., Ozturk, C., & Karaboga, N. (2012). A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artificial Intelligence Review, 42(1), 21-57. doi:10.1007/s10462-012-9328-0Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3), 303-315. doi:10.1016/j.cad.2010.12.015Zhang, C., Lin, Q., Gao, L., & Li, X. (2015). Backtracking Search Algorithm with three constraint handling methods for constrained optimization problems. Expert Systems with Applications, 42(21), 7831-7845. doi:10.1016/j.eswa.2015.05.050Yang, X. S. (2010). Firefly algorithm, stochastic test functions and design optimisation. International Journal of Bio-Inspired Computation, 2(2), 78. doi:10.1504/ijbic.2010.032124Punnathanam, V., & Kotecha, P. (2016). Yin-Yang-pair Optimization: A novel lightweight optimization algorithm. Engineering Applications of Artificial Intelligence, 54, 62-79. doi:10.1016/j.engappai.2016.04.004Zhao, C., Wu, C., Chai, J., Wang, X., Yang, X., Lee, J.-M., & Kim, M. J. (2017). Decomposition-based multi-objective firefly algorithm for RFID network planning with uncertainty. Applied Soft Computing, 55, 549-564. doi:10.1016/j.asoc.2017.02.009Zhao, C., Wu, C., Wang, X., Ling, B. W.-K., Teo, K. L., Lee, J.-M., & Jung, K.-H. (2017). Maximizing lifetime of a wireless sensor network via joint optimizing sink placement and sensor-to-sink routing. Applied Mathematical Modelling, 49, 319-337. doi:10.1016/j.apm.2017.05.001Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67-82. doi:10.1109/4235.585893Simon, D. (2008). Biogeography-Based Optimization. IEEE Transactions on Evolutionary Computation, 12(6), 702-713. doi:10.1109/tevc.2008.919004Garg, H. (2015). An efficient biogeography based optimization algorithm for solving reliability optimization problems. Swarm and Evolutionary Computation, 24, 1-10. doi:10.1016/j.swevo.2015.05.001Storn, R., & Price, K. (1997). Journal of Global Optimization, 11(4), 341-359. doi:10.1023/a:1008202821328Das, S., Mullick, S. S., & Suganthan, P. N. (2016). Recent advances in differential evolution – An updated survey. Swarm and Evolutionary Computation, 27, 1-30. doi:10.1016/j.swevo.2016.01.004Couzin, I. D., Krause, J., Franks, N. R., & Levin, S. A. (2005). Effective leadership and decision-making in animal groups on the move. Nature, 433(7025), 513-516. doi:10.1038/nature03236Gandomi, A. H., & Alavi, A. H. (2012). Krill herd: A new bio-inspired optimization algorithm. Communications in Nonlinear Science and Numerical Simulation, 17(12), 4831-4845. doi:10.1016/j.cnsns.2012.05.010Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61. doi:10.1016/j.advengsoft.2013.12.007Erol, O. K., & Eksin, I. (2006). A new optimization method: Big Bang–Big Crunch. Advances in Engineering Software, 37(2), 106-111. doi:10.1016/j.advengsoft.2005.04.005Kaveh, A., & Mahdavi, V. R. (2014). Colliding bodies optimization: A novel meta-heuristic method. Computers & Structures, 139, 18-27. doi:10.1016/j.compstruc.2014.04.005Rashedi, E., Nezamabadi-pour, H., & Saryazdi, S. (2009). GSA: A Gravitational Search Algorithm. Information Sciences, 179(13), 2232-2248. doi:10.1016/j.ins.2009.03.004Zheng, Y.-J. (2015). Water wave optimization: A new nature-inspired metaheuristic. Computers & Operations Research, 55, 1-11. doi:10.1016/j.cor.2014.10.008Kaveh, A., & Khayatazad, M. (2012). A new meta-heuristic method: Ray Optimization. Computers & Structures, 112-113, 283-294. doi:10.1016/j.compstruc.2012.09.003Glover, F. (1989). Tabu Search—Part I. ORSA Journal on Computing, 1(3), 190-206. doi:10.1287/ijoc.1.3.190Chiang, H.-P., Chou, Y.-H., Chiu, C.-H., Kuo, S.-Y., & Huang, Y.-M. (2013). A quantum-inspired Tabu search algorithm for solving combinatorial optimization problems. Soft Computing, 18(9), 1771-1781. doi:10.1007/s00500-013-1203-7Mousavirad, S. J., & Ebrahimpour-Komleh, H. (2017). Human mental search: a new population-based metaheuristic optimization algorithm. Applied Intelligence, 47(3), 850-887. doi:10.1007/s10489-017-0903-6Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39(3), 459-471. doi:10.1007/s10898-007-9149-xRao, R. V., Savsani, V. J., & Vakharia, D. P. (2012). Teaching–Learning-Based Optimization: An optimization method for continuous non-linear large scale problems. Information Sciences, 183(1), 1-15. doi:10.1016/j.ins.2011.08.006Digalakis, J. G., & Margaritis, K. G. (2001). On benchmarking functions for genetic algorithms. International Journal of Computer Mathematics, 77(4), 481-506. doi:10.1080/00207160108805080Karaboga, D., & Akay, B. (2009). A comparative study of Artificial Bee Colony algorithm. Applied Mathematics and Computation, 214(1), 108-132. doi:10.1016/j.amc.2009.03.090Lim, T. Y., Al-Betar, M. A., & Khader, A. T. (2015). Adaptive pair bonds in genetic algorithm: An application to real-parameter optimization. Applied Mathematics and Computation, 252, 503-519. doi:10.1016/j.amc.2014.12.030Fleury, C., & Braibant, V. (1986). Structural optimization: A new dual method using mixed variables. International Journal for Numerical Methods in Engineering, 23(3), 409-428. doi:10.1002/nme.1620230307Derrac, J., García, S., Molina, D., & Herrera, F. (2011). A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation, 1(1), 3-18. doi:10.1016/j.swevo.2011.02.002Gandomi, A. H., Yang, X.-S., & Alavi, A. H. (2011). Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Engineering with Computers, 29(1), 17-35. doi:10.1007/s00366-011-0241-yWang, G. G. (2003). Adaptive Response Surface Method Using Inherited Latin Hypercube Design Points. Journal of Mechanical Design, 125(2), 210-220. doi:10.1115/1.1561044Cheng, M.-Y., & Prayogo, D. (2014). Symbiotic Organisms Search: A new metaheuristic optimization algorithm. Computers & Structures, 139, 98-112. doi:10.1016/j.compstruc.2014.03.007CHICKERMANE, H., & GEA, H. C. (1996). STRUCTURAL OPTIMIZATION USING A NEW LOCAL APPROXIMATION METHOD. International Journal for Numerical Methods in Engineering, 39(5), 829-846. doi:10.1002/(sici)1097-0207(19960315)39:53.0.co;2-uChou, J.-S., & Ngo, N.-T. (2016). Modified firefly algorithm for multidimensional optimization in structural design problems. Structural and Multidisciplinary Optimization, 55(6), 2013-2028. doi:10.1007/s00158-016-1624-xSonmez, M. (2011). Artificial Bee Colony algorithm for optimization of truss structures. Applied Soft Computing, 11(2), 2406-2418. doi:10.1016/j.asoc.2010.09.003Degertekin, S. O. (2012). Improved harmony search algorithms for sizing optimization of truss structures. Computers & Structures, 92-93, 229-241. doi:10.1016/j.compstruc.2011.10.022Degertekin, S. O., & Hayalioglu, M. S. (2013). Sizing truss structures using teaching-learning-based optimization. Computers & Structures, 119, 177-188. doi:10.1016/j.compstruc.2012.12.011Talatahari, S., Kheirollahi, M., Farahmandpour, C., & Gandomi, A. H. (2012). A multi-stage particle swarm for optimum design of truss structures. Neural Computing and Applications, 23(5), 1297-1309. doi:10.1007/s00521-012-1072-5Kaveh, A., Bakhshpoori, T., & Afshari, E. (2014). An efficient hybrid Particle Swarm and Swallow Swarm Optimization algorithm. Computers & Structures, 143, 40-59. doi:10.1016/j.compstruc.2014.07.012Kaveh, A., & Bakhshpoori, T. (2016). A new metaheuristic for continuous structural optimization: water evaporation optimization. Structural and Multidisciplinary Optimization, 54(1), 23-43. doi:10.1007/s00158-015-1396-8Jalili, S., & Hosseinzadeh, Y. (2015). A Cultural Algorithm for Optimal Design of Truss Structures. Latin American Journal of Solids and Structures, 12(9), 1721-1747. doi:10.1590/1679-7825154

Lévy mutation in artificial bee colony algorithm for gasoline price prediction

In this paper, a mutation strategy that is based on Lévy Probabily Distribution is introduced in Artificial Bee Colony algorithm. The purpose is to better exploit promising solutions found by the bees.Such an approach is used to improve the performance of the original ABC in optimizing Least Squares Support Vector Machine hyper parameters.From the conducted experiment, the proposed lvABC shows encouraging results in optimizing parameters of interest.The proposed.lvABC-LSSVM has outperformed existing prediction model, Backpropogation Neural Network (BPNN), in predicting gasoline price

Land scene classification from remote sensing images using improved artificial bee colony optimization algorithm

The images obtained from remote sensing consist of background complexities and similarities among the objects that act as challenge during the classification of land scenes. Land scenes are utilized in various fields such as agriculture, urbanization, and disaster management, to detect the condition of land surfaces and help to identify the suitability of the land surfaces for planting crops, and building construction. The existing methods help in the classification of land scenes through the images obtained from remote sensing technology, but the background complexities and presence of similar objects act as a barricade against providing better results. To overcome these issues, an improved artificial bee colony optimization algorithm with convolutional neural network (IABC-CNN) model is proposed to achieve better results in classifying the land scenes. The images are collected from aerial image dataset (AID), Northwestern Polytechnical University-Remote Sensing Image Scene 45 (NWPU-RESIS45), and University of California Merced (UCM) datasets. IABC effectively selects the best features from the extracted features using visual geometry group-16 (VGG-16). The selected features from the IABC are provided for the classification process using multiclass-support vector machine (MSVM). Results obtained from the proposed IABC-CNN achieves a better classification accuracy of 96.40% with an error rate 3.6%

Image Denoising Based on Artificial Bee Colony and BP Neural Network

Image is often subject to noise pollution during the process of collection, acquisition and transmission, noise is a major factor affecting the image quality, which has greatly impeded people from extracting information from the image. The purpose of image denoising is to restore the original image without noise from the noise image, and at the same time maintain the detailed information of the image as much as possible. This paper, by combining artificial bee colony algorithm and BP neural network, proposes the image denoising method based on artificial bee colony and BP neural network (ABC-BPNN), ABC-BPNN adopts the “double circulation” structure during the training process, after specifying the expected convergence speed and precision, it can adjust the rules according to the structure, automatically adjusts the number of neurons, while the weight of the neurons and relevant parameters are determined through bee colony optimization. The simulation result shows that the algorithm proposed in this paper can maintain the image edges and other important features while removing noise, so as to obtain better denoising effect

Application of Artificial Bee Colony Algorithm in Vehicle Routing Problem With Time Windows

In order to improve the accuracy of the artificial bee colony algorithm (ABC) on vehicle routing problem with time window (VRPTW),This paper makes the following improvements to the ABC :We introduce three kinds of neighborhood search methods,In the leader bee and follower bee search stage,we changing the single search mode into a three-way search method,which improves the optimization depth of the algorithm.Conducting multiple neighborhood searches of new food sources generated by the scouter bee and proceeding to the next iteration has enhanced the survival of new food sources and increased the diversity of populations. The global optimal solution is recorded by setting and updating the bulletin board. Simulation experiments show that the improved discrete ABC algorithm has obvious advantages in solving large-scale VRPTW. Therefore, the improved discrete ABC algorithm has great potential and application value in solving VRPTW