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A novel improved slime mould algorithm for engineering design

  • Optimization
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Abstract

Metaheuristic intelligent optimization algorithm is an effective method to settle high-dimensional nonlinear complicated optimization problems. Slime mould algorithm is a novel intelligent optimization algorithm proposed in 2020. However, the basic slime mould algorithm still has shortcomings, such as slow convergence rate, easy falling into local extremum, and imbalanced exploration and exploitation capabilities. To further enhance the optimization capability and expand the application scope of the slime mould algorithm, a slime mould algorithm based on the mechanism of multi-strategy information interaction and optimally oriented initialization (MSII-SMA) is proposed. Three improved mechanisms are introduced into the algorithm and the time complexity of MSII-SMA is analyzed. To verify the optimization effect, MSII-SMA and the other 5 typical comparison algorithms are applied to settle the CEC2015 test function set. The analysis of the optimization accuracy, convergence curve, Friedman test, boxplot and scalability test shows that the optimization ability, convergence rate, stability and scalability of MSII-SMA are evidently better than the comparison algorithm. Finally, MSII-SMA and other comparison algorithms are used to settle engineering design optimization problems with different complexity. The experimental results verify the universality, reliability and preponderance of MSII-SMA in dealing with engineering design constraint optimization problems.

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References

  • Abualigah L, Diabat A, Elaziz M A (2021) Improved slime mould algorithm by opposition-based learning and Levy flight distribution for global optimization and advances in real-world engineering problems. Journal of Ambient Intelligence and Humanized Computing, pp 1–40.

  • Asghari K, Masdari M, Gharehchopogh FS et al (2021) A chaotic and hybrid gray wolf-whale algorithm for solving continuous optimization problems. Prog Artificial Intell 10(3):349–374

    Article  Google Scholar 

  • Cao Y, Li Y, Zhang G et al (2019) Experimental modeling of PEM fuel cells using a new improved seagull optimization algorithm. Energy Rep 5:1616–1625

    Article  Google Scholar 

  • Chen Q, Liu B, Zhang Q, et al. (2014) Problem definitions and evaluation criteria for CEC 2015 special session on bound constrained single-objective computationally expensive numerical optimization. Technical Report, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Technical Report, Nanyang Technological University

  • Dooyum UD, Mallipeddi R, Pamulapati T, Park T, Kim J, Woo S, Ha Y (2018) Interactive livestock feed ration optimization using evolutionary algorithms. Comput Electron Agric 155:1–11

    Article  Google Scholar 

  • Dorigo M, Maniezzo V, Colorni A (1996) Ant system: Optimization by a colony of cooperating agents. IEEE Trans on Syst, Man, and Cybernet, Part B: Cybernet a Publicat IEEE Syst Man, Cybernet Soc 26(1):29–41

    Article  Google Scholar 

  • Ewees AA, Abualigah L, Yousri D et al (2022) Improved slime mould algorithm based on firefly algorithm for feature selection: a case study on QSAR model. Eng Comput 38(3):2407–2421

    Article  Google Scholar 

  • Faramarzi A, Heidarinejad M, Stephens B et al (2020) Equilibrium optimizer: a novel optimization algorithm. Knowl-Based Syst 191:105190

    Article  Google Scholar 

  • Frigge M, Hoaglin DC, Iglewicz B (1989) Some implementations of the boxplot. Am Stat 43(1):50–54

    Google Scholar 

  • Guo W, Liu T, Dai F et al (2020) An improved whale optimization algorithm for forecasting water resources demand. Appl Soft Comput 86:105925

    Article  Google Scholar 

  • Gupta S, Deep K, Mirjalili S (2020) An efficient equilibrium optimizer with mutation strategy for numerical optimization. Appl Soft Comput 96:106542

    Article  Google Scholar 

  • He X, Zhou Y (2018) Enhancing the performance of differential evolution with covariance matrix self-adaptation. Appl Soft Comput 64:227–243

    Article  Google Scholar 

  • Himmelblau DM (1972) Applied nonlinear programming, McGraw-Hill Companies

  • Houssein EH, Mahdy MA, Blondin MJ et al (2021) Hybrid slime mould algorithm with adaptive guided differential evolution algorithm for combinatorial and global optimization problems. Expert Syst Appl 174:114689

    Article  Google Scholar 

  • Kang F, Li J, Dai J (2019) Prediction of long-term temperature effect in structural health monitoring of concrete dams using support vector machines with Jaya optimizer and salp swarm algorithms. Adv Eng Softw 131:60–76

    Article  Google Scholar 

  • Kennedy J, Eberhart J (1995) Particle swarm Optimization. Proceedings of IEEE International Conference on Neural Networks, pp 1942–1948.

  • Kumar A, Wu G, Ali MZ et al (2020) A test-suite of non-convex constrained optimization problems from the real-world and some baseline results[J]. Swarm Evol Comput 56:100693

    Article  Google Scholar 

  • Li S, Chen H, Wang M et al (2020) Slime mould algorithm: a new method for stochastic optimization[J]. Futur Gener Comput Syst 111:300–323

    Article  Google Scholar 

  • Liu J, Li W, Li Y (2022) LWMEO: An efficient equilibrium optimizer for complex functions and engineering design problems. Expert Syst Appl 198:116828

    Article  Google Scholar 

  • Liu J, Mao Y, Liu X et al (2020) A dynamic adaptive firefly algorithm with globally orientation. Math Comput Simul 174:76–101

    Article  MathSciNet  MATH  Google Scholar 

  • Marichelvam MK, Prabaharan T, Yang XS (2014) Improved cuckoo search algorithm for hybrid flow shop scheduling problems to minimize makespan. Appl Soft Comput 19:93–101

    Article  Google Scholar 

  • Meena NK, Parashar S, Swarnkar A, Gupta N, Niazi KR (2018) Improved elephant herding optimization for multiobjective DER accommodation in distribution systems. IEEE Trans Industr Inf 14(3):1029–1039. https://doi.org/10.1109/TII.2017.2748220

    Article  Google Scholar 

  • Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Article  Google Scholar 

  • Naik MK, Panda R, Abraham A (2021) An entropy minimization based multilevel colour thresholding technique for analysis of breast thermograms using equilibrium slime mould algorithm[J]. Appl Soft Comput 113:107955

    Article  Google Scholar 

  • Naik M K, Panda R, Abraham A (2020) Normalized square difference based multilevel thresholding technique for multispectral images using leader slime mould algorithm. J King Saud Univ-Comput Inform Sci

  • Nguyen TT, Wang HJ, Dao TK et al (2020) An improved slime mold algorithm and its application for optimal operation of cascade hydropower stations. IEEE Access 8:226754–226772

    Article  Google Scholar 

  • Rao RV, Savsani VJ, Vakharia D (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43:303–315

    Article  Google Scholar 

  • Rizk-Allah RM, Hassanien AE, Song D (2022) Chaos-opposition-enhanced slime mould algorithm for minimizing the cost of energy for the wind turbines on high-altitude sites. ISA Trans 121:191–205

    Article  Google Scholar 

  • Ryoo HS, Sahinidis NV (1995) Global optimization of nonconvex nlps and minlps with applications in process design. Comput Chem Eng 19:551–566

    Article  Google Scholar 

  • Sigmund O (2001) A 99 line topology optimization code written in matlab. Struct Multidiscip Optim 21:120–127

    Article  Google Scholar 

  • Tang K, Li X, Suganthan PN et al (2009) Benchmark functions for the CEC’2010 special session and competition on large-scale global optimization. University of Science and Technology of China, Hefei

    Google Scholar 

  • Tizhoosh HR (2005) Opposition-based learning: a new scheme for machine intelligence[C]//International conference on computational intelligence for modelling, control and automation and international conference on intelligent agents, web technologies and internet commerce (CIMCA-IAWTIC’06). IEEE 1:695–701

    Google Scholar 

  • Wen L, Tie-Bin W, Ming-Zhu T et al (2020) Grey wolf optimizer algorithm based on lens imaging learning strategy[J]. Acta Automatica Sinica 46(10):2148–2164

    MATH  Google Scholar 

  • Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82

    Article  Google Scholar 

  • Yang XS, Deb S (2014) Cuckoo search: recent advances and applications. Neural Comput Appl 24(1):169–174

    Article  Google Scholar 

  • Yu K, Liu L, Chen Z (2021) An improved slime mould algorithm for demand estimation of urban water resources. Mathematics 9(12):1316

    Article  Google Scholar 

Download references

Acknowledgements

This work is jointly supported by the National Natural Science Foundation of China (grant number 72104069), the Key R&D and Promotion Projects of Henan Province, China (grant number 222102210065), the Major Science and Technology Project of Henan Province, China (grant number 201300210400), and the Action Plan for Postgraduate Training Innovation and Quality Improvement of Henan University (grant number SYLYC2022150). The authors would like to thank the anonymous reviewers and the editors for their helpful comments.

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Authors and Affiliations

  1. International Joint Laboratory of Intelligent Network Theory and Key Technology, Henan University, Kaifeng, China

    Jingsen Liu & Yiwen Fu

  2. College of Software, Henan University, Kaifeng, China

    Jingsen Liu & Yiwen Fu

  3. Institute of Management Science and Engineering, Henan University, Kaifeng, China

    Yu Li

  4. Business School, Henan University, Kaifeng, China

    Yu Li & Huan Zhou

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  1. Jingsen Liu
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  2. Yiwen Fu
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  4. Huan Zhou
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Correspondence to Yu Li.

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Liu, J., Fu, Y., Li, Y. et al. A novel improved slime mould algorithm for engineering design. Soft Comput 27, 12181–12210 (2023). https://doi.org/10.1007/s00500-023-08430-3

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