How to cite this paper
Naik, A & Chokkalingam, P. (2011). Binary social group optimization algorithm for solving 0-1 knapsack problem.Decision Science Letters , 11(1), 55-72.
Refrences
Abdel-Basset, M., El-Shahat, D., & El-Henawy, I. (2019). Solving 0–1 knapsack problem by binary flower pollination algorithm. Neural Computing and Applications, 31(9), 5477-5495.
Abdel-Basset, M., Luo, Q., Miao, F., & Zhou, Y. (2017). Solving 0–1 knapsack problems by binary dragonfly algorithm. In International conference on intelligent computing (pp. 491-502). Springer, Cham.
Abdel-Basset, M., & Zhou, Y. (2018). An elite opposition-flower pollination algorithm for a 0-1 knapsack problem. International Journal of Bio-Inspired Computation, 11(1), 46-53.
Bansal, J. C., & Deep, K. (2012). A modified binary particle swarm optimization for knapsack problems. Applied Mathematics and Computation, 218(22), 11042-11061.
Bhattacharjee, K. K., & Sarmah, S. P. (2014). Shuffled frog leaping algorithm and its application to 0/1 knapsack problem. Applied Soft Computing, 19, 252-263.
Catharin, A. R., Kumar, A. S., Rakshiga, M., Kumaresan, S., & Raja, N. S. M. (2018, March). Examination of Glioblastoma Images by Thresholding using Heuristic Approach. In 2018 Fourth International Conference on Biosignals, Images and Instrumentation (ICBSII) (pp. 206-212). IEEE.
Chakravarthy, V. S., Chowdary, P. S. R., Satpathy, S. C., Terlapu, S. K., & Anguera, J. (2018). Antenna array synthesis using social group optimization. In Microelectronics, Electromagnetics and Telecommunications (pp. 895-905). Springer, Singapore.
Changdar, C., Mahapatra, G. S., & Pal, R. K. (2013). An ant colony optimization approach for binary knapsack problem under fuzziness. Applied Mathematics and Computation, 223, 243-253.
Cobos, C., Dulcey, H., Ortega, J., Mendoza, M., & Ordoñez, A. A Binary Fisherman Search Procedure for the 0/1 Knapsack Problem. Advances in Artificial Intelligence: Proceedings of the 17th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2016. O. Luaces et al.
Derrac, 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.
Dey, N., Rajinikanth, V., Ashour, A. S., & Tavares, J. M. R. (2018). Social group optimization supported segmentation and evaluation of skin melanoma images. Symmetry, 10(2), 51.
Du, D. P., & Zu, Y. R. (2015). Greedy strategy based self-adaption ant colony algorithm for 0/1 knapsack problem. In Ubiquitous Computing Application and Wireless Sensor (pp. 663-670). Springer, Dordrecht.
Fang, J., Zheng, H., Liu, J., Zhao, J., Zhang, Y., & Wang, K. (2018). A transformer fault diagnosis model using an optimal hybrid dissolved gas analysis features subset with improved social group optimization-support vector machine classifier. Energies, 11(8), 1922.
Feng, Y., Wang, G. G., Feng, Q., & Zhao, X. J. (2014). An effective hybrid cuckoo search algorithm with improved shuffled frog leaping algorithm for 0-1 knapsack problems. Computational Intelligence and Neuroscience, 2014.
Feng, Y., Wang, G. G., Dong, J., & Wang, L. (2018). Opposition-based learning monarch butterfly optimization with Gaussian perturbation for large-scale 0-1 knapsack problem. Computers & Electrical Engineering, 67, 454-468.
Feng, Y., Wang, G. G., Deb, S., Lu, M., & Zhao, X. J. (2017). Solving 0–1 knapsack problem by a novel binary monarch butterfly optimization. Neural Computing and Applications, 28(7), 1619-1634.
Gherboudj, A., Layeb, A., & Chikhi, S. (2012). Solving 0-1 knapsack problems by a discrete binary version of cuckoo search algorithm. International Journal of Bio-Inspired Computation, 4(4), 229-236.
Gong, Q. Q., Zhou, Y. Q., & Yang, Y. (2011). Artificial glowworm swarm optimization algorithm for solving 0-1 knapsack problem. In Advanced materials research (Vol. 143, pp. 166-171). Trans Tech Publications Ltd.
Kong, M., & Tian, P. (2006, June). Apply the particle swarm optimization to the multidimensional knapsack problem. In International conference on artificial intelligence and soft computing (pp. 1140-1149). Springer, Berlin, Heidelberg.
Kong, X., Gao, L., Ouyang, H., & Li, S. (2015). A simplified binary harmony search algorithm for large scale 0–1 knapsack problems. Expert Systems with Applications, 42(12), 5337-5355.
Kulkarni, A. J., Krishnasamy, G., & Abraham, A. (2017). Solution to 0–1 knapsack problem using cohort intelligence algorithm. In Cohort Intelligence: A Socio-inspired Optimization Method (pp. 55-74). Springer, Cham.
Layeb, A. (2011). A novel quantum inspired cuckoo search for knapsack problems. International Journal of Bio-inspired Computation, 3(5), 297-305.
Leena, J. G., Sundaravadivu, K., Monisha, R., & Rajinikanth, V. (2018, July). Design of Fractional-Order PI/PID Controller for SISO System Using Social-Group-Optimization. In 2018 IEEE International Conference on System, Computation, Automation and Networking (ICSCA) (pp. 1-5). IEEE.
Lim, T. Y., Al-Betar, M. A., & Khader, A. T. (2016). Taming the 0/1 knapsack problem with monogamous pairs genetic algorithm. Expert Systems with Applications, 54, 241-250.
Lin, C. J., Chern, M. S., & Chih, M. (2016). A binary particle swarm optimization based on the surrogate information with proportional acceleration coefficients for the 0-1 multidimensional knapsack problem. Journal of Industrial and Production Engineering, 33(2), 77-102.
Lv, J., Wang, X., Huang, M., Cheng, H., & Li, F. (2016). Solving 0-1 knapsack problem by greedy degree and expectation efficiency. Applied Soft Computing, 41, 94-103.
Ma, Y., & Wan, J. (2011, July). Improved hybrid adaptive genetic algorithm for solving knapsack problem. In 2011 2nd International Conference on Intelligent Control and Information Processing (Vol. 2, pp. 644-647). IEEE.
Madhavi, G., & Harika, V. (2018). Implementation of social group optimization to economic load dispatch problem. International Journal of Applied Engineering Research, 13(13), 11195-11200.
Mani, M. S., Manisha, S., Thanaraj, K. P., & Rajinikanth, V. (2017, July). Automated segmentation of Giemsa stained microscopic images based on entropy value. In 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT) (pp. 1124-1128). IEEE.
Martello, S., Pisinger, D., & Toth, P. (2000). New trends in exact algorithms for the 0–1 knapsack problem. European Journal of Operational Research, 123(2), 325-332.
Monisha, R., Mrinalini, R., Britto, M. N., Ramakrishnan, R., & Rajinikanth, V. (2019). Social group optimization and Shannon’s function-based RGB image multi-level thresholding. In Smart Intelligent Computing and Applications (pp. 123-132). Springer, Singapore.
Naik, A., & Satapathy, S. C. (2021). A comparative study of social group optimization with a few recent optimization algorithms. Complex & Intelligent Systems, 7(1), 249-295.
Naik, A., Satapathy, S. C., & Abraham, A. (2020). Modified Social Group Optimization—A meta-heuristic algorithm to solve short-term hydrothermal scheduling. Applied Soft Computing, 95, 106524.
Naik, A., Satapathy, S. C., Ashour, A. S., & Dey, N. (2018). Social group optimization for global optimization of multimodal functions and data clustering problems. Neural Computing and Applications, 30(1), 271-287.
Naik, A., Satapathy, S. C., & Jena, J. J. (2021). Non-dominated Sorting Social Group Optimization algorithm for multiobjective optimization. Journal of Scientific and Industrial Research (JSIR), 80(02), 129-136.
Nguyen, P. H., Wang, D., & Truong, T. K. (2017). A novel binary social spider algorithm for 0–1 knapsack problem. Internatioanl Journal of Innovative Computation Information Control, 13(6), 2039-2049.
Nguyen, P. H., Wang, D., & Truong, T. K. (2016). A new hybrid particle swarm optimization and greedy for 0-1 knapsack problem. Indonesian Journal of Electronic Engeering Compututation Science, 1(3), 411-418.
Parwekar, P. (2020). SGO a new approach for energy efficient clustering in WSN. In Sensor Technology: Concepts, Methodologies, Tools, and Applications (pp. 716-734). IGI Global.
Pavithr, R. S. (2016). Quantum Inspired Social Evolution (QSE) algorithm for 0-1 knapsack problem. Swarm and Evolutionary Computation, 29, 33-46.
Pham, A. H., Vu, T. V., & Tran, T. M. (2017, August). Optimal Volume Fraction of Functionally Graded Beams with Various Shear Deformation Theories Using Social Group Optimization. In International Conference on Advances in Computational Mechanics (pp. 395-408). Springer, Singapore.
Praveen, S. P., Rao, K. T., & Janakiramaiah, B. (2018). Effective allocation of resources and task scheduling in cloud environment using social group optimization. Arabian Journal for Science and Engineering, 43(8), 4265-4272.
Rajinikanth, V., & Satapathy, S. C. (2018). Segmentation of ischemic stroke lesion in brain MRI based on social group optimization and Fuzzy-Tsallis entropy. Arabian Journal for Science and Engineering, 43(8), 4365-4378.
Rani, S., & Suri, B. (2019, July). Adopting social group optimization algorithm using mutation testing for test suite generation: SGO-MT. In International Conference on Computational Science and Its Applications (pp. 520-528). Springer, Cham.
Razavi, S. F., & Sajedi, H. (2015). Cognitive discrete gravitational search algorithm for solving 0-1 knapsack problem. Journal of Intelligent & Fuzzy Systems, 29(5), 2247-2258.
Rizk-Allah, R. M., & Hassanien, A. E. (2018). New binary bat algorithm for solving 0–1 knapsack problem. Complex & Intelligent Systems, 4(1), 31-53.
Satapathy, S., & Naik, A. (2016). Social group optimization (SGO): a new population evolutionary optimization technique. Complex & Intelligent Systems, 2(3), 173-203.
Shi, H. (2006, August). Solution to 0/1 knapsack problem based on improved ant colony algorithm. In 2006 IEEE International Conference on Information Acquisition (pp. 1062-1066). IEEE.
Sonuc, E., Sen, B., & Bayir, S. (2016). A parallel approach for solving 0/1 knapsack problem using simulated annealing algorithm on CUDA platform. International Journal of Computer Science and Information Security, 14(12), 1096.
Tran, M. T., Pham, H. A., Nguyen, V. L., & Trinh, A. T. (2017). Optimisation of stiffeners for maximum fundamental frequency of cross-ply laminated cylindrical panels using social group optimisation and smeared stiffener method. Thin-Walled Structures, 120, 172-179.
Truong, T. K., Li, K., Xu, Y., Ouyang, A., & Nguyen, T. T. (2015). Solving 0-1 knapsack problem by artificial chemical reaction optimization algorithm with a greedy strategy. Journal of Intelligent & Fuzzy Systems, 28(5), 2179-2186.
Ulker, E., & Tongur, V. (2017). Migrating birds optimization (MBO) algorithm to solve knapsack problem. Procedia Computer Science, 111, 71-76.
Wang, L., Yang, R., Xu, Y., Niu, Q., Pardalos, P. M., & Fei, M. (2013). An improved adaptive binary harmony search algorithm. Information Sciences, 232, 58-87.
Yan, C., Gao, S., Luo, H., & Hu, Z. (2015, June). A hybrid algorithm based on tabu search and chemical reaction optimization for 0-1 knapsack problem. In International Conference in Swarm Intelligence (pp. 229-237). Springer, Cham.
Zhang, X., Huang, S., Hu, Y., Zhang, Y., Mahadevan, S., & Deng, Y. (2013). Solving 0-1 knapsack problems based on amoeboid organism algorithm. Applied Mathematics and Computation, 219(19), 9959-9970.
Zhou, G., Zhao, R., & Zhou, Y. (2018). Solving large-scale 0-1 knapsack problem by the social-spider optimisation algorithm. International Journal of Computing Science and Mathematics, 9(5), 433-441.
Zhou, Y., Bao, Z., Luo, Q., & Zhang, S. (2017). A complex-valued encoding wind driven optimization for the 0-1 knapsack problem. Applied Intelligence, 46(3), 684-702.
Zhou, Y., Chen, X., & Zhou, G. (2016). An improved monkey algorithm for a 0-1 knapsack problem. Applied Soft Computing, 38, 817-830.
Zhou, Y., Li, L., & Ma, M. (2016). A complex-valued encoding bat algorithm for solving 0–1 knapsack problem. Neural Processing Letters, 44(2), 407-430.
Zou, D., Gao, L., Li, S., & Wu, J. (2011). Solving 0–1 knapsack problem by a novel global harmony search algorithm. Applied Soft Computing, 11(2), 1556-1564.
Abdel-Basset, M., Luo, Q., Miao, F., & Zhou, Y. (2017). Solving 0–1 knapsack problems by binary dragonfly algorithm. In International conference on intelligent computing (pp. 491-502). Springer, Cham.
Abdel-Basset, M., & Zhou, Y. (2018). An elite opposition-flower pollination algorithm for a 0-1 knapsack problem. International Journal of Bio-Inspired Computation, 11(1), 46-53.
Bansal, J. C., & Deep, K. (2012). A modified binary particle swarm optimization for knapsack problems. Applied Mathematics and Computation, 218(22), 11042-11061.
Bhattacharjee, K. K., & Sarmah, S. P. (2014). Shuffled frog leaping algorithm and its application to 0/1 knapsack problem. Applied Soft Computing, 19, 252-263.
Catharin, A. R., Kumar, A. S., Rakshiga, M., Kumaresan, S., & Raja, N. S. M. (2018, March). Examination of Glioblastoma Images by Thresholding using Heuristic Approach. In 2018 Fourth International Conference on Biosignals, Images and Instrumentation (ICBSII) (pp. 206-212). IEEE.
Chakravarthy, V. S., Chowdary, P. S. R., Satpathy, S. C., Terlapu, S. K., & Anguera, J. (2018). Antenna array synthesis using social group optimization. In Microelectronics, Electromagnetics and Telecommunications (pp. 895-905). Springer, Singapore.
Changdar, C., Mahapatra, G. S., & Pal, R. K. (2013). An ant colony optimization approach for binary knapsack problem under fuzziness. Applied Mathematics and Computation, 223, 243-253.
Cobos, C., Dulcey, H., Ortega, J., Mendoza, M., & Ordoñez, A. A Binary Fisherman Search Procedure for the 0/1 Knapsack Problem. Advances in Artificial Intelligence: Proceedings of the 17th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2016. O. Luaces et al.
Derrac, 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.
Dey, N., Rajinikanth, V., Ashour, A. S., & Tavares, J. M. R. (2018). Social group optimization supported segmentation and evaluation of skin melanoma images. Symmetry, 10(2), 51.
Du, D. P., & Zu, Y. R. (2015). Greedy strategy based self-adaption ant colony algorithm for 0/1 knapsack problem. In Ubiquitous Computing Application and Wireless Sensor (pp. 663-670). Springer, Dordrecht.
Fang, J., Zheng, H., Liu, J., Zhao, J., Zhang, Y., & Wang, K. (2018). A transformer fault diagnosis model using an optimal hybrid dissolved gas analysis features subset with improved social group optimization-support vector machine classifier. Energies, 11(8), 1922.
Feng, Y., Wang, G. G., Feng, Q., & Zhao, X. J. (2014). An effective hybrid cuckoo search algorithm with improved shuffled frog leaping algorithm for 0-1 knapsack problems. Computational Intelligence and Neuroscience, 2014.
Feng, Y., Wang, G. G., Dong, J., & Wang, L. (2018). Opposition-based learning monarch butterfly optimization with Gaussian perturbation for large-scale 0-1 knapsack problem. Computers & Electrical Engineering, 67, 454-468.
Feng, Y., Wang, G. G., Deb, S., Lu, M., & Zhao, X. J. (2017). Solving 0–1 knapsack problem by a novel binary monarch butterfly optimization. Neural Computing and Applications, 28(7), 1619-1634.
Gherboudj, A., Layeb, A., & Chikhi, S. (2012). Solving 0-1 knapsack problems by a discrete binary version of cuckoo search algorithm. International Journal of Bio-Inspired Computation, 4(4), 229-236.
Gong, Q. Q., Zhou, Y. Q., & Yang, Y. (2011). Artificial glowworm swarm optimization algorithm for solving 0-1 knapsack problem. In Advanced materials research (Vol. 143, pp. 166-171). Trans Tech Publications Ltd.
Kong, M., & Tian, P. (2006, June). Apply the particle swarm optimization to the multidimensional knapsack problem. In International conference on artificial intelligence and soft computing (pp. 1140-1149). Springer, Berlin, Heidelberg.
Kong, X., Gao, L., Ouyang, H., & Li, S. (2015). A simplified binary harmony search algorithm for large scale 0–1 knapsack problems. Expert Systems with Applications, 42(12), 5337-5355.
Kulkarni, A. J., Krishnasamy, G., & Abraham, A. (2017). Solution to 0–1 knapsack problem using cohort intelligence algorithm. In Cohort Intelligence: A Socio-inspired Optimization Method (pp. 55-74). Springer, Cham.
Layeb, A. (2011). A novel quantum inspired cuckoo search for knapsack problems. International Journal of Bio-inspired Computation, 3(5), 297-305.
Leena, J. G., Sundaravadivu, K., Monisha, R., & Rajinikanth, V. (2018, July). Design of Fractional-Order PI/PID Controller for SISO System Using Social-Group-Optimization. In 2018 IEEE International Conference on System, Computation, Automation and Networking (ICSCA) (pp. 1-5). IEEE.
Lim, T. Y., Al-Betar, M. A., & Khader, A. T. (2016). Taming the 0/1 knapsack problem with monogamous pairs genetic algorithm. Expert Systems with Applications, 54, 241-250.
Lin, C. J., Chern, M. S., & Chih, M. (2016). A binary particle swarm optimization based on the surrogate information with proportional acceleration coefficients for the 0-1 multidimensional knapsack problem. Journal of Industrial and Production Engineering, 33(2), 77-102.
Lv, J., Wang, X., Huang, M., Cheng, H., & Li, F. (2016). Solving 0-1 knapsack problem by greedy degree and expectation efficiency. Applied Soft Computing, 41, 94-103.
Ma, Y., & Wan, J. (2011, July). Improved hybrid adaptive genetic algorithm for solving knapsack problem. In 2011 2nd International Conference on Intelligent Control and Information Processing (Vol. 2, pp. 644-647). IEEE.
Madhavi, G., & Harika, V. (2018). Implementation of social group optimization to economic load dispatch problem. International Journal of Applied Engineering Research, 13(13), 11195-11200.
Mani, M. S., Manisha, S., Thanaraj, K. P., & Rajinikanth, V. (2017, July). Automated segmentation of Giemsa stained microscopic images based on entropy value. In 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT) (pp. 1124-1128). IEEE.
Martello, S., Pisinger, D., & Toth, P. (2000). New trends in exact algorithms for the 0–1 knapsack problem. European Journal of Operational Research, 123(2), 325-332.
Monisha, R., Mrinalini, R., Britto, M. N., Ramakrishnan, R., & Rajinikanth, V. (2019). Social group optimization and Shannon’s function-based RGB image multi-level thresholding. In Smart Intelligent Computing and Applications (pp. 123-132). Springer, Singapore.
Naik, A., & Satapathy, S. C. (2021). A comparative study of social group optimization with a few recent optimization algorithms. Complex & Intelligent Systems, 7(1), 249-295.
Naik, A., Satapathy, S. C., & Abraham, A. (2020). Modified Social Group Optimization—A meta-heuristic algorithm to solve short-term hydrothermal scheduling. Applied Soft Computing, 95, 106524.
Naik, A., Satapathy, S. C., Ashour, A. S., & Dey, N. (2018). Social group optimization for global optimization of multimodal functions and data clustering problems. Neural Computing and Applications, 30(1), 271-287.
Naik, A., Satapathy, S. C., & Jena, J. J. (2021). Non-dominated Sorting Social Group Optimization algorithm for multiobjective optimization. Journal of Scientific and Industrial Research (JSIR), 80(02), 129-136.
Nguyen, P. H., Wang, D., & Truong, T. K. (2017). A novel binary social spider algorithm for 0–1 knapsack problem. Internatioanl Journal of Innovative Computation Information Control, 13(6), 2039-2049.
Nguyen, P. H., Wang, D., & Truong, T. K. (2016). A new hybrid particle swarm optimization and greedy for 0-1 knapsack problem. Indonesian Journal of Electronic Engeering Compututation Science, 1(3), 411-418.
Parwekar, P. (2020). SGO a new approach for energy efficient clustering in WSN. In Sensor Technology: Concepts, Methodologies, Tools, and Applications (pp. 716-734). IGI Global.
Pavithr, R. S. (2016). Quantum Inspired Social Evolution (QSE) algorithm for 0-1 knapsack problem. Swarm and Evolutionary Computation, 29, 33-46.
Pham, A. H., Vu, T. V., & Tran, T. M. (2017, August). Optimal Volume Fraction of Functionally Graded Beams with Various Shear Deformation Theories Using Social Group Optimization. In International Conference on Advances in Computational Mechanics (pp. 395-408). Springer, Singapore.
Praveen, S. P., Rao, K. T., & Janakiramaiah, B. (2018). Effective allocation of resources and task scheduling in cloud environment using social group optimization. Arabian Journal for Science and Engineering, 43(8), 4265-4272.
Rajinikanth, V., & Satapathy, S. C. (2018). Segmentation of ischemic stroke lesion in brain MRI based on social group optimization and Fuzzy-Tsallis entropy. Arabian Journal for Science and Engineering, 43(8), 4365-4378.
Rani, S., & Suri, B. (2019, July). Adopting social group optimization algorithm using mutation testing for test suite generation: SGO-MT. In International Conference on Computational Science and Its Applications (pp. 520-528). Springer, Cham.
Razavi, S. F., & Sajedi, H. (2015). Cognitive discrete gravitational search algorithm for solving 0-1 knapsack problem. Journal of Intelligent & Fuzzy Systems, 29(5), 2247-2258.
Rizk-Allah, R. M., & Hassanien, A. E. (2018). New binary bat algorithm for solving 0–1 knapsack problem. Complex & Intelligent Systems, 4(1), 31-53.
Satapathy, S., & Naik, A. (2016). Social group optimization (SGO): a new population evolutionary optimization technique. Complex & Intelligent Systems, 2(3), 173-203.
Shi, H. (2006, August). Solution to 0/1 knapsack problem based on improved ant colony algorithm. In 2006 IEEE International Conference on Information Acquisition (pp. 1062-1066). IEEE.
Sonuc, E., Sen, B., & Bayir, S. (2016). A parallel approach for solving 0/1 knapsack problem using simulated annealing algorithm on CUDA platform. International Journal of Computer Science and Information Security, 14(12), 1096.
Tran, M. T., Pham, H. A., Nguyen, V. L., & Trinh, A. T. (2017). Optimisation of stiffeners for maximum fundamental frequency of cross-ply laminated cylindrical panels using social group optimisation and smeared stiffener method. Thin-Walled Structures, 120, 172-179.
Truong, T. K., Li, K., Xu, Y., Ouyang, A., & Nguyen, T. T. (2015). Solving 0-1 knapsack problem by artificial chemical reaction optimization algorithm with a greedy strategy. Journal of Intelligent & Fuzzy Systems, 28(5), 2179-2186.
Ulker, E., & Tongur, V. (2017). Migrating birds optimization (MBO) algorithm to solve knapsack problem. Procedia Computer Science, 111, 71-76.
Wang, L., Yang, R., Xu, Y., Niu, Q., Pardalos, P. M., & Fei, M. (2013). An improved adaptive binary harmony search algorithm. Information Sciences, 232, 58-87.
Yan, C., Gao, S., Luo, H., & Hu, Z. (2015, June). A hybrid algorithm based on tabu search and chemical reaction optimization for 0-1 knapsack problem. In International Conference in Swarm Intelligence (pp. 229-237). Springer, Cham.
Zhang, X., Huang, S., Hu, Y., Zhang, Y., Mahadevan, S., & Deng, Y. (2013). Solving 0-1 knapsack problems based on amoeboid organism algorithm. Applied Mathematics and Computation, 219(19), 9959-9970.
Zhou, G., Zhao, R., & Zhou, Y. (2018). Solving large-scale 0-1 knapsack problem by the social-spider optimisation algorithm. International Journal of Computing Science and Mathematics, 9(5), 433-441.
Zhou, Y., Bao, Z., Luo, Q., & Zhang, S. (2017). A complex-valued encoding wind driven optimization for the 0-1 knapsack problem. Applied Intelligence, 46(3), 684-702.
Zhou, Y., Chen, X., & Zhou, G. (2016). An improved monkey algorithm for a 0-1 knapsack problem. Applied Soft Computing, 38, 817-830.
Zhou, Y., Li, L., & Ma, M. (2016). A complex-valued encoding bat algorithm for solving 0–1 knapsack problem. Neural Processing Letters, 44(2), 407-430.
Zou, D., Gao, L., Li, S., & Wu, J. (2011). Solving 0–1 knapsack problem by a novel global harmony search algorithm. Applied Soft Computing, 11(2), 1556-1564.