Skip to main content
SpringerLink
Log in

Boosting Kernel Search Optimizer with Slime Mould Foraging Behavior for Combined Economic Emission Dispatch Problems

  • Research Article
  • Published:
Journal of Bionic Engineering Aims and scope Submit manuscript

Abstract

Reducing pollutant emissions from electricity production in the power system positively impacts the control of greenhouse gas emissions. Boosting kernel search optimizer (BKSO) is introduced in this research to solve the combined economic emission dispatch (CEED) problem. Inspired by the foraging behavior in the slime mould algorithm (SMA), the kernel matrix of the kernel search optimizer (KSO) is intensified. The proposed BKSO is superior to the standard KSO in terms of exploitation ability, robustness, and convergence rate. The CEC2013 test function is used to assess the improved KSO's performance and compared to 11 well-known optimization algorithms. BKSO performs better in statistical results and convergence curves. At the same time, BKSO achieves better fuel costs and fewer pollution emissions by testing with four real CEED cases, and the Pareto solution obtained is also better than other MAs. Based on the experimental results, BKSO has better performance than other comparable MAs and can provide more economical, robust, and cleaner solutions to CEED problems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data Availability Statement

The data involved in this study are all public data, which can be downloaded through public channels.

References

  1. Cai, T. T., Dong, M. Y., Chen, K., & Gong, T. R. (2022). Methods of participating power spot market bidding and settlement for renewable energy systems. Energy Reports, 8, 7764–7772.

    Article  Google Scholar 

  2. Chanda, C. K., Maity, D., & Banerjee, S. (2017). Solution of economic load dispatch problem using biogeography based optimization technique considering valve point loading effect. International Journal of Electrical Energy, 5(1), 58–64.

    Article  Google Scholar 

  3. Duan, Y., Zhao, Y., & Hu, J. (2023). An initialization-free distributed algorithm for dynamic economic dispatch problems in microgrid: Modeling, optimization and analysis. Sustainable Energy, Grids and Networks, 34, 101004. https://doi.org/10.1016/j.segan.2023.101004.

    Article  Google Scholar 

  4. Li, P., Hu, J. P., Qiu, L., Zhao, Y. Y., & Ghosh, B. K. (2021). A distributed economic dispatch strategy for power-water networks. IEEE Transactions on Control of Network Systems, 9(1), 356–366.

    Article  MathSciNet  Google Scholar 

  5. Hagh, M. T., Kalajahi, S., & Ghorbani, N. (2019). Solution to economic emission dispatch problem including wind farms using exchange market algorithm method. Applied Soft Computing, 88, 106044.

    Article  Google Scholar 

  6. Guo, B. N., Wang, Y., Zhou, H. Y., & Hu, F. (2022). Can environmental tax reform promote carbon abatement of resource-based cities? Evidence from a quasinatural experiment in China. Environmental Science and Pollution Research, 29, 1–13. https://doi.org/10.1007/s11356-022-23669-3.

    Article  Google Scholar 

  7. Shang, Y. F., Lian, Y., Chen, H., & Qian, F. B. (2023). The impacts of energy resource and tourism on green growth: Evidence from Asian economies. Resources Policy, 81, 103359. https://doi.org/10.1016/j.resourpol.2023.103359

    Article  Google Scholar 

  8. Jadhav, H. T., Raj, S., & Roy, R. (2013). Solution to economic emission load dispatch problem using modified artificial bee colony algorithm. In International conference on electric power & energy conversion systems, Istanbul, Turkey. (pp. 235–240).

  9. Liu, Z. F., Li, L. L., Liu, Y. W., Liu, J. Q., Li, H. Y., & Shen, Q. (2021). Dynamic economic emission dispatch considering renewable energy generation: A novel multi-objective optimization approach. Energy, 235, 121407.

    Article  Google Scholar 

  10. Tian, J., Hou, M., Bian, H., & Li, J. (2022). Variable surrogate model-based particle swarm optimization for high-dimensional expensive problems. Complex & Intelligent Systems, 9, 3887–3935. https://doi.org/10.1007/s40747-022-00910-7.

    Article  Google Scholar 

  11. Wang, G., Deb, S., & Cui, Z. (2019). Monarch butterfly optimization. Neural Computing and Applications, 31(7), 1995–2014. https://doi.org/10.1007/s00521-015-1923-y.

    Article  Google Scholar 

  12. Yang, Y., Chen, H., Heidari, A. A., & Gandomi, A. H. (2021). Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towardsperformance shifts. Expert Systems with Applications, 177, 114864. https://doi.org/10.1016/j.eswa.2021.114864.

    Article  Google Scholar 

  13. Ahmadianfar, I., Heidari, A. A., Gandomi, A. H., Chu, X., & Chen, H. (2021). RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method. Expert Systems with Applications, 181, 115079. https://doi.org/10.1016/j.eswa.2021.115079.

    Article  Google Scholar 

  14. Tu, J. Z., Chen, H. L., Wang, M. J., & Gandomi, A. H. (2021). The colony predation algorithm. Journal of Bionic Engineering, 18(3), 674–710. https://doi.org/10.1007/s42235-021-0050-y

    Article  Google Scholar 

  15. Ahmadianfar, I., Heidari, A. A., Noshadian, S., Chen, H., & Gandomi, A. H. (2022). INFO: An efficient optimization algorithm based on weighted mean of vectors. Expert Systems with Applications, 195, 116516. https://doi.org/10.1016/j.eswa.2022.116516.

    Article  Google Scholar 

  16. Su, H., Zhao, D., Heidari, A. A., Liu, L., Zhang, X. Q., Mafarja, M., & Chen, H. L. (2023). RIME: A physics-based optimization. Neurocomputing, 532, 183–214. https://doi.org/10.1016/j.neucom.2023.02.010

    Article  Google Scholar 

  17. Deb, S., Abdelminaam, D. S., Said, M., & Houssein, E. H. (2021). Recent methodology-based gradient-based optimizer for economic load dispatch problem. IEEE Access, 9, 44322–44338. https://doi.org/10.1109/access.2021.3066329

    Article  Google Scholar 

  18. Hussien, A. G., Oliva, D., Houssein, E. H., Juan, A. A., & Yu, X. (2020). Binary whale optimization algorithm for dimensionality reduction. Mathematics, 8(10), 1821. https://doi.org/10.3390/math8101821

    Article  Google Scholar 

  19. Houssein, E. H., Hussain, K., Abualigah, L., Elaziz, M. A., Alomoush, W., Dhiman, G., Djenouri, Y., & Cuevas, E. (2021). An improved opposition-based marine predators algorithm for global optimization and multilevel thresholding image segmentation. Knowledge-Based Systems, 229, 107348. https://doi.org/10.1016/j.knosys.2021.107348

    Article  Google Scholar 

  20. Houssein, E. H., Helmy, B.E.-D., Rezk, H., & Nassef, A. M. (2021). An enhanced Archimedes optimization algorithm based on Local escaping operator and Orthogonal learning for PEM fuel cell parameter identification. Engineering Applications of Artificial Intelligence, 103, 104309. https://doi.org/10.1016/j.engappai.2021.104309

    Article  Google Scholar 

  21. Houssein, E. H., Emam, M. M., & Ali, A. A. (2021). An efficient multilevel thresholding segmentation method for thermography breast cancer imaging based on improved chimp optimization algorithm. Expert Systems with Applications, 185, 115651. https://doi.org/10.1016/j.eswa.2021.115651

    Article  Google Scholar 

  22. Elminaam, D. S. A., Nabil, A., Ibraheem, S. A., & Houssein, E. H. (2021). An efficient marine predators algorithm for feature selection. IEEE Access, 9, 60136–60153. https://doi.org/10.1109/access.2021.3073261

    Article  Google Scholar 

  23. Ismaeel, A. A. K., Elshaarawy, I. A., Houssein, E. H., Ismail, F. H., & Hassanien, A. E. (2019). Enhanced Elephant Herding Optimization for Global Optimization. IEEE Access, 7, 34738–34752. https://doi.org/10.1109/ACCESS.2019.2904679.

    Article  Google Scholar 

  24. Hassanien, A. E., Kilany, M., Houssein, E. H., & AlQaheri, H. (2018). Intelligent human emotion recognition based on elephant herding optimization tuned support vector regression. Biomedical Signal Processing and Control, 45, 182–191. https://doi.org/10.1016/j.bspc.2018.05.039

    Article  Google Scholar 

  25. Hamad, A., Houssein, E. H., Hassanien, A. E., & Fahmy, A. A. (2018). Hybrid grasshopper optimization algorithm and support vector machines for automatic seizure detection in EEG signals. Advances in Intelligent Systems and Computing, 723, 82–91.

    Article  Google Scholar 

  26. Houssein, E. H., & Sayed, A. (2023). Dynamic candidate solution boosted beluga whale optimization algorithm for biomedical classification. Mathematics, 11(3), 707. https://doi.org/10.3390/math11030707.

    Article  Google Scholar 

  27. Hota, P. K., Barisal, A. K., & Chakrabarti, R. (2010). Economic emission load dispatch through fuzzy based bacterial foraging algorithm. International Journal of Electrical Power & Energy Systems, 32(7), 794–803. https://doi.org/10.1016/j.ijepes.2010.01.016

    Article  Google Scholar 

  28. Roy, P. K., & Bhui, S. (2013). Multi-objective quasi-oppositional teaching learning based optimization for economic emission load dispatch problem. International Journal of Electrical Power & Energy Systems, 53(4), 937–948. https://doi.org/10.1016/j.ijepes.2013.06.015

    Article  Google Scholar 

  29. Silva, M. A. C., Klein, C. E., Mariani, V. C., & dos Santos Coelho, L. (2013). Multiobjective scatter search approach with new combination scheme applied to solve environmental/economic dispatch problem. Energy, 53, 14–21. https://doi.org/10.1016/j.energy.2013.02.045

    Article  Google Scholar 

  30. Abdelaziz, A. Y., Ali, E. S., & Elazim, S. A. (2016). Flower pollination algorithm to solve combined economic and emission dispatch problems. Engineering Science and Technology, an International Journal, 19(2), 980–990.

    Article  Google Scholar 

  31. Singh, M., & Dhillon, J. S. (2016). Multiobjective thermal power dispatch using opposition-based greedy heuristic search. International Journal of Electrical Power & Energy Systems, 82, 339–353.

    Article  Google Scholar 

  32. Dosoglu, M. K., Guvenc, U., Duman, S., Sonmez, Y., & Kahraman, H. T. (2018). Symbiotic organisms search optimization algorithm for economic/emission dispatch problem in power systems. Neural Computing and Applications, 29(3), 721–737.

    Article  Google Scholar 

  33. Hosny, M., Kamel, S., Salih, S., Lone, T., & Ebeed, M. (2021). Developing chaotic artificial ecosystem-based optimization algorithm for combined economic emission dispatch. IEEE Access, 9, 51146–51165. https://doi.org/10.1109/ACCESS.2021.3066914.

    Article  Google Scholar 

  34. Hassan, M. H., Houssein, E. H., Mahdy, M. A., & Kamel, S. (2021). An improved Manta ray foraging optimizer for cost-effective emission dispatch problems. Engineering Applications of Artificial Intelligence, 100, 104155. https://doi.org/10.1016/j.engappai.2021.104155

    Article  Google Scholar 

  35. Srivastava, A., & Das, D. (2020). A new aggrandized class topper optimization algorithm to solve economic load dispatch problem in a power system. IEEE Transactions on Cybernetics, 52(6), 4187–4197. https://doi.org/10.1109/TCYB.2020.3024607.

    Article  Google Scholar 

  36. Singh, D., & Dhillon, J. S. (2018). Ameliorated grey wolf optimization for economic load dispatch problem. Energy, 169, 398–419. https://doi.org/10.1016/j.energy.2018.11.034.

    Article  Google Scholar 

  37. Kaboli, H. R., & Alqallaf, A. (2019). Solving non-convex economic load dispatch problem via artificial cooperative search algorithm. Expert Systems with Applications, 128, 14–27. https://doi.org/10.1016/j.eswa.2019.02.002.

    Article  Google Scholar 

  38. Ahmed, M., El-Rifaie, A., Tolba, M., Houssein, E., & Deb, S. (2021). An efficient chameleon swarm algorithm for economic load dispatch problem. Mathematics, 9, 2770. https://doi.org/10.3390/math9212770

    Article  Google Scholar 

  39. Yamina Ahlem, G., Fatiha, L., Hamid, B., & Gherbi, F. Z. (2019). Hybridization of two metaheuristics for solving the combined economic and emission dispatch problem. Neural Computing and Applications, 31, 547–8559. https://doi.org/10.1007/s00521-019-04151-7.

    Article  Google Scholar 

  40. Al-Betar, M., Awadallah, M., & Krishan, M. (2020). A non-convex economic load dispatch problem with valve loading effect using a hybrid grey wolf optimizer. Neural Computing and Applications, 32, 12127–12154. https://doi.org/10.1007/s00521-019-04284-9.

    Article  Google Scholar 

  41. Fayyaz, S., Kashif, M., Waseem, M., Ashraf, M. U., Ahmad, A., Hussain, A., et al. (2021). Solution of combined economic emission dispatch problem using improved and chaotic population based polar bear optimization algorithm. IEEE Access, 9, 56152–56167. https://doi.org/10.1109/ACCESS.2021.3072012.

    Article  Google Scholar 

  42. Benyekhlef, L., El Islam, A. A. N., Ayad, I., Alharbi, H., Houari, B., Tayeb, A., et al. (2022). Investigation on new metaheuristic algorithms for solving dynamic combined economic environmental dispatch problems. Sustainability, 14(9), 5554. https://doi.org/10.3390/su14095554.

    Article  Google Scholar 

  43. Dong, R. Y., & Wang, S. S. (2020). New optimization algorithm inspired by kernel tricks for the economic emission dispatch problem with valve point. IEEE Access, 8, 16584–16594. https://doi.org/10.1109/ACCESS.2020.2965725.

    Article  Google Scholar 

  44. Zhang, Z. Y., Altalbawy, F. M., Al-Bahrani, M., & Riadi, Y. (2023). Regret-based multi-objective optimization of carbon capture facility in CHP-based microgrid with carbon dioxide cycling. Journal of Cleaner Production, 384, 135632.

    Article  Google Scholar 

  45. Yu, D. M., Wan, X. M., & Gu, B. (2023). Bi-objective optimization of biomass solid waste energy system with a solid oxide fuel cell. Chemosphere, 323, 138182.

    Article  Google Scholar 

  46. Kumar, R., Sadu, A., Kumar, R., & Panda, S. K. (2012). A novel multi-objective directed bee colony optimization algorithm for multi-objective emission constrained economic power dispatch. International Journal of Electrical Power & Energy Systems, 43(1), 1241–1250.

    Article  Google Scholar 

  47. Rajesh, K., & Visali, N. (2020). Hybrid method for achieving Pareto front on economic emission dispatch. International Journal of Electrical and Computer Engineering (IJECE), 10(4), 3358. https://doi.org/10.11591/ijece.v10i4.pp3358-3366

    Article  Google Scholar 

  48. Xia, A. M., Wu, X. D., & Bai, Y. J. (2021). Hybrid MHHO-DE algorithm for economic emission dispatch with valve-point effect. Arabian Journal for Science and Engineering, 46(10), 9399–9411. https://doi.org/10.1007/s13369-020-05308-6

    Article  Google Scholar 

  49. Xu, X. L., Hu, Z. B., Su, Q. H., Xiong, Z. G., & Liu, M. F. (2020). Multi-objective learning backtracking search algorithm for economic emission dispatch problem. Soft Computing, 25(3), 2433–2452. https://doi.org/10.1007/s00500-020-05312-w

    Article  Google Scholar 

  50. Wang, G. B., Zha, Y. X., Wu, T., Qiu, J., Peng, J. C., & Xu, G. (2019). Cross entropy optimization based on decomposition for multi-objective economic emission dispatch considering renewable energy generation uncertainties. Energy, 193, 116790. https://doi.org/10.1016/j.energy.2019.116790

    Article  Google Scholar 

  51. Ponnuvel, S., Murugesan, S., & Duraisamy, S. (2020). Multi-objective squirrel search algorithm to solve economic environmental power dispatch problems. International Transactions on Electrical Energy Systems, 30(12), 1–31. https://doi.org/10.1002/2050-7038.12635.

    Article  Google Scholar 

  52. Dhiman, G. (2020). MOSHEPO: a hybrid multi-objective approach to solve economic load dispatch and micro grid problems. Applied Intelligence, 50, 119–137. https://doi.org/10.1007/s10489-019-01522-4.

    Article  Google Scholar 

  53. Zou, D. X., Li, S., Li, Z. Y., & Kong, X. Y. (2017). A new global particle swarm optimization for the economic emission dispatch with or without transmission losses. Energy Conversion and Management, 139, 45–70. https://doi.org/10.1016/j.enconman.2017.02.035

    Article  Google Scholar 

  54. Li, S. M., Chen, H. L., Wang, M. J., Heidari, A. A., & Mirjalili, S. (2020). Slime mould algorithm: A new method for stochastic optimization. Future Generation Computer Systems-the International Journal of Escience, 111, 300–323. https://doi.org/10.1016/j.future.2020.03.055

    Article  Google Scholar 

  55. Hassan, M. H., Kamel, S., Abualigah, L., & Eid, A. (2021). Development and application of slime mould algorithm for optimal economic emission dispatch. Expert Systems with Applications, 182, 115205. https://doi.org/10.1016/j.eswa.2021.115205

    Article  Google Scholar 

  56. Tribhuvan, S. (2022). Chaotic slime mould algorithm for economic load dispatch problems. Applied Intelligence, 52(13), 15325–15344.

    Article  Google Scholar 

  57. Yaşar, C., & Özyön, S. (2012). Solution to scalarized environmental economic power dispatch problem by using genetic algorithm. International Journal of Electrical Power & Energy Systems, 38, 54–62. https://doi.org/10.1016/j.ijepes.2011.12.020

    Article  Google Scholar 

  58. Zhan, J. P., Wu, Q. H., Guo, C. X., & Zhou, X. X. (2014). Fast -iteration method for economic dispatch with prohibited operating zones. IEEE Transactions on Power Systems, 29, 990–991. https://doi.org/10.1109/TPWRS.2013.2287995

    Article  Google Scholar 

  59. Dong, R. Y., Ma, L., Chen, H. L., Heidari, A. A., & Liang, G. X. (2023). Hybrid kernel search and particle swarm optimization with Cauchy perturbation for economic emission load dispatch with valve point effect. Frontiers in Energy Research, 10, 1061408.

    Article  Google Scholar 

  60. Cao, B., Gu, Y., Lv, Z. H., Yang, S., Zhao, J. W., & Li, Y. J. (2020). RFID reader anticollision based on distributed parallel particle swarm optimization. IEEE Internet of Things Journal, 8(5), 3099–3107.

    Article  Google Scholar 

  61. Gandomi, A., Yang, X.-S., & Alavi, A. (2013). Cuckoo search algorithm: A metaheuristic approach to solve structural optimization problems. Engineering with Computers, 29, 1–19. https://doi.org/10.1007/s00366-011-0241-y

    Article  Google Scholar 

  62. Alcala-Fdez, J., Sanchez, L., Garcia, S., del Jesus, M. J., Ventura, S., Garrell, J. M., Otero, J., Romero, C., Bacardit, J., Rivas, V. M., Fernandez, J. C., & Herrera, F. (2009). KEEL: A software tool to assess evolutionary algorithms for data mining problems. Soft Computing, 13(3), 307–318. https://doi.org/10.1007/s00500-008-0323-y

    Article  Google Scholar 

  63. Li, X. T., & Sun, Y. (2021). Application of RBF neural network optimal segmentation algorithm in credit rating. Neural Computing and Applications, 33, 8227–8235.

    Article  Google Scholar 

  64. Zhan, C., Dai, Z., Yang, Z., Zhang, X., Ma, Z., Thanh, H. V., et al. (2023). Subsurface sedimentary structure identification using deep learning: A review. Earth-Science Reviews, 239(13), 104370. https://doi.org/10.1016/j.earscirev.2023.104370 .

    Article  Google Scholar 

  65. Liu, H., Yue, Y. P., Liu, C., Spencer, B., Jr., & Cui, J. (2023). Automatic recognition and localization of underground pipelines in GPR B-scans using a deep learning model. Tunnelling and Underground Space Technology, 134, 104861.

    Article  Google Scholar 

  66. Liang, J. J., Qu, B., Suganthan, P. N., & Hernández-Díaz, A. G. (2013). Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Nanyang Technological University, Singapore, Technical Report, 201212(34), 281–295.

    Google Scholar 

  67. Chen, W. N., Zhang, J., Lin, Y., Chen, N., Zhan, Z. H., Chung, H. S. H., Li, Y., & Shi, Y. H. (2013). Particle swarm optimization with an aging leader and challengers. IEEE Transactions on Evolutionary Computation, 17(2), 241–258. https://doi.org/10.1109/tevc.2011.2173577

    Article  Google Scholar 

  68. Jia, D. L., Zheng, G. X., Qu, B. Y., & Khan, M. K. (2011). A hybrid particle swarm optimization algorithm for high-dimensional problems. Computers & Industrial Engineering, 61(4), 1117–1122.

    Article  Google Scholar 

  69. Chen, H. L., Yang, C. J., Heidari, A. A., & Zhao, X. H. (2020). An efficient double adaptive random spare reinforced whale optimization algorithm. Expert Systems with Applications, 154, 113018. https://doi.org/10.1016/j.eswa.2019.113018

    Article  Google Scholar 

  70. Heidari, A. A., Abbaspour, R. A., & Chen, H. (2019). Efficient boosted grey wolf optimizers for global search and kernel extreme learning machine training. Applied Soft Computing, 81, 105521.

    Article  Google Scholar 

  71. Nenavath, H., & Jatoth, R. K. (2018). Hybridizing sine cosine algorithm with differential evolution for global optimization and object tracking. Applied Soft Computing, 62, 1019–1043. https://doi.org/10.1016/j.asoc.2017.09.039

    Article  Google Scholar 

  72. Sayed, G. I., Khoriba, G., & Haggag, M. H. (2018). A novel chaotic salp swarm algorithm for global optimization and feature selection. Applied Intelligence, 48(10), 3462–3481. https://doi.org/10.1007/s10489-018-1158-6

    Article  Google Scholar 

  73. Dong, R. Y., Chen, H. L., Heidari, A. A., Turabieh, H., Mafarja, M., & Wang, S. S. (2021). Boosted kernel search: Framework, analysis and case studies on the economic emission dispatch problem. Knowledge-Based Systems, 233, 107529. https://doi.org/10.1016/j.knosys.2021.107529

    Article  Google Scholar 

  74. Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 89, 228–249. https://doi.org/10.1016/j.knosys.2015.07.006

    Article  Google Scholar 

  75. Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris Hawks optimization: Algorithm and applications. Future Generation Computer Systems-the International Journal of Escience, 97, 849–872. https://doi.org/10.1016/j.future.2019.02.028

    Article  Google Scholar 

  76. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN’95 - International Conference on Neural Networks, Perth, Australia.

  77. Xue, X., Yu, X. N., Zhou, D. Y., Wang, X., Zhou, Z. B., & Wang, F. Y. (2022). Computational experiments: Past, present and future. https://arxiv.org/abs/2202.13690.

  78. Xue, X., Yu, X., Zhou, D., Peng, C., Wang, X., Liu, D., et al. (2023). Computational experiments for complex social systems—Part III: The docking of domain models. IEEE Transactions on Computational Social Systems, 99, 1–15. https://doi.org/10.1109/TCSS.2023.3243894.

    Article  Google Scholar 

  79. Chen, Y., Gan, H. M., Chen, H. L., Zeng, Y. G., Xu, L., Heidari, A. A., Zhu, X. D., & Liu, Y. N. (2023). Accurate iris segmentation and recognition using an end-to-end unified framework based on MADNet and DSANet. Neurocomputing, 517, 264–278. https://doi.org/10.1016/j.neucom.2022.10.064

    Article  Google Scholar 

  80. Xue, X., Li, G. D., Zhou, D. Y., Zhang, Y. P., Zhang, L., Zhao, Y., Feng, Z. Y., Cui, L. Z., Zhou, Z. B., & Sun, X. (2022). Research roadmap of service ecosystems: A crowd intelligence perspective. International Journal of Crowd Science, 6(4), 195–222.

    Article  Google Scholar 

  81. Zhao, C., Wang, H., Chen, H., Shi, W., & Feng, Y. (2022). JAMSNet: A remote pulse extraction network based on joint attention and multi-scale fusion. IEEE Transactions on Circuits and Systems for Video Technology, 33(6), 2783–2797. https://doi.org/10.1109/TCSVT.2022.3227348.

    Article  Google Scholar 

  82. Yu, M., Han, M., Li, X. W., Wei, X., Jiang, H., Chen, H. L., & Yu, R. G. (2022). Adaptive soft erasure with edge self-attention for weakly supervised semantic segmentation: Thyroid ultrasound image case study. Computers in Biology and Medicine, 144, 105347. https://doi.org/10.1016/j.compbiomed.2022.105347

    Article  Google Scholar 

  83. Abido, M. A. (2006). Multiobjective evolutionary algorithms for electric power dispatch problem. IEEE Transactions on Evolutionary Computation, 10(3), 315–329.

    Article  Google Scholar 

  84. Qu, B. Y., Liang, J., Zhu, Y. S., Wang, Z. Y., & Suganthan, P. (2016). Economic emission dispatch problems with stochastic wind power using summation based multi-objective evolutionary algorithm. Information Sciences, 351, 48–66. https://doi.org/10.1016/j.ins.2016.01.081.

    Article  Google Scholar 

  85. Hazra, J., & Sinha, A. K. (2011). A multi-objective optimal power flow using particle swarm optimization. European Transactions on Electrical Power, 21(1), 1028–1045. https://doi.org/10.1002/etep.494.

    Article  Google Scholar 

  86. Horn, J., Nafpliotis, N., & Goldberg, D. E. (1994). A niched Pareto genetic algorithm for multiobjective optimization. In Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence, Orlando, USA. https://doi.org/10.1109/ICEC.1994.350037.

  87. Panigrahi, B. K., Ravikumar Pandi, V., Das, S., & Das, S. (2010). Multiobjective fuzzy dominance based bacterial foraging algorithm to solve economic emission dispatch problem. Energy, 35(12), 4761–4770. https://doi.org/10.1016/j.energy.2010.09.014

    Article  Google Scholar 

  88. Dong, R. Y., & Wang, S. S. (2018). New optimization algorithm inspired by fluid mechanics for combined economic and emission dispatch problem. Turkish Journal of Electrical Engineering & Computer Sciences, 26(6), 3306–3319. https://doi.org/10.3906/elk-1803-88

    Article  Google Scholar 

  89. Jevtić, M., Jovanovic, N., Radosavljević, J., & Klimenta, D. (2017). Moth swarm algorithm for solving combined economic and emission dispatch problem. Elektronika ir Elektrotechnika, 23(5), 21–28. https://doi.org/10.5755/j01.eie.23.5.19267.

    Article  Google Scholar 

  90. Özyön, S., Yaşar, C., Durmus, B., & Temurtas, H. (2015). Opposition-based gravitational search algorithm applied to economic power dispatch problems consisting of thermal units with emission constraints. Turkish Journal of Electrical Engineering and Computer Sciences, 23, 2278–2288. https://doi.org/10.3906/elk-1305-258

    Article  Google Scholar 

  91. Abou El-Ela, A., Abido, M., & Spea, S. (2010). Differential evolution algorithm for emission constrained economic power dispatch problem. Electric Power Systems Research, 80, 1286–1292. https://doi.org/10.1016/j.epsr.2010.04.011

    Article  Google Scholar 

  92. Afzalan, E., & Joorabian, M. (2013). Emission, reserve and economic load dispatch problem with non-smooth and non-convex cost functions using epsilon-multi-objective genetic algorithm variable. International Journal of Electrical Power & Energy Systems, 52, 55–67. https://doi.org/10.1016/j.ijepes.2013.03.017

    Article  Google Scholar 

  93. Özyön, S., Temurtas, H., Durmuş, B., & Kuvat, G. (2012). Charged system search algorithm for emission constrained economic power dispatch problem. Energy, 46, 420–430. https://doi.org/10.1016/j.energy.2012.08.008

    Article  Google Scholar 

  94. Zhang, Y., Gong, D. W., & Ding, Z. H. (2012). A bare-bones multi-objective particle swarm optimization algorithm for environmental/economic dispatch. Information Sciences, 192, 213–227. https://doi.org/10.1016/j.ins.2011.06.004

    Article  Google Scholar 

  95. Sinha, N., Chakrabarti, R., & Chattopadhyay, P. K. (2003). Evolutionary programming techniques for economic load dispatch. IEEE Transactions on Evolutionary Computation, 7, 83–94. https://doi.org/10.1109/TEVC.2002.806788

    Article  Google Scholar 

  96. Coelho, L. (2010). Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems. Expert Systems with Applications, 37, 1676–1683. https://doi.org/10.1016/j.eswa.2009.06.044

    Article  Google Scholar 

  97. Taherkhani, M., & Safabakhsh, R. (2016). A novel stability-based adaptive inertia weight for particle swarm optimization. Applied Soft Computing, 38, 281–295. https://doi.org/10.1016/j.asoc.2015.10.004

    Article  Google Scholar 

  98. Abdelaziz, A., Ali, E., & Abd-Elazim, S. (2016). Combined economic and emission dispatch solution using flower pollination algorithm. International Journal of Electrical Power and Energy Systems, 80, 264–274. https://doi.org/10.1016/j.ijepes.2015.11.093

    Article  Google Scholar 

  99. Basu, M. (2011). Economic environmental dispatch using multi-objective differential evolution. Applied Soft Computing, 11, 2845–2853. https://doi.org/10.1016/j.asoc.2010.11.014

    Article  Google Scholar 

  100. Modiri-Delshad, M., & Rahim, N. A. (2016). Multi-objective backtracking search algorithm for economic emission dispatch problem. Applied Soft Computing, 40, 479–494. https://doi.org/10.1016/j.asoc.2015.11.020

    Article  Google Scholar 

  101. Guvenc, U. (2010). Combined economic emission dispatch solution using genetic algorithm based on similarity crossover. Scientific Research and Essays, 5, 2451–2456.

    Google Scholar 

  102. Balamurugan, R., & Subramanian, S. (2008). A simplified recursive approach to combined economic emission dispatch. Electric Power Components and Systems, 36, 17–27. https://doi.org/10.1080/15325000701473742

    Article  Google Scholar 

  103. Jadoun, V. K., Gupta, N., Niazi, K. R., & Swarnkar, A. (2015). Modulated particle swarm optimization for economic emission dispatch. International Journal of Electrical Power & Energy Systems, 73, 80–88. https://doi.org/10.1016/j.ijepes.2015.04.004

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by the Science & Technology Development Project of Jilin Province, China (YDZJ202201ZYTS555), the Science & Technology Research Project of the Education Department of Jilin Province, China (JJKH20220244KJ).

Author information

Authors and Affiliations

  1. College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin, 132000, China

    Ruyi Dong, Lixun Sun & Long Ma

  2. School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, 1417935840, Iran

    Ali Asghar Heidari

  3. Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou, 325000, China

    Xinsen Zhou & Huiling Chen

Authors
  1. Ruyi Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lixun Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Long Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ali Asghar Heidari
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xinsen Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Huiling Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huiling Chen.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix A

Appendix A

See Tables 23, 24 and 25.

Table 23 Comparison results of BKSO and some excellent peers
Full size table
Table 24 Analysis result by using the Wilcoxon signed-rank test
Full size table
Table 25 p values obtained by conducting the Wilcoxon signed-rank test
Full size table

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dong, R., Sun, L., Ma, L. et al. Boosting Kernel Search Optimizer with Slime Mould Foraging Behavior for Combined Economic Emission Dispatch Problems. J Bionic Eng 20, 2863–2895 (2023). https://doi.org/10.1007/s42235-023-00408-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42235-023-00408-z

Keywords

Search

Navigation