Abstract
Moth Flame Optimization (MFO) is a nature-inspired optimization algorithm, based on the principle of navigation technique of moth toward moon. Due to less parameter and easy implementation, MFO is used in various field to solve optimization problems. Further, for the complex higher dimensional problems, MFO is unable to make a good trade-off between global and local search. To overcome these drawbacks of MFO, in this work, an enhanced MFO, namely WF-MFO, is introduced to solve higher dimensional optimization problems. For a more optimal balance between global and local search, the original MFO’s exploration ability is improved by an exploration operator, namely, Weibull flight distribution. In addition, the local optimal solutions have been avoided and the convergence speed has been increased using a Fibonacci search process-based technique that improves the quality of the solutions found. Twenty-nine benchmark functions of varying complexity with 1000 and 2000 dimensions have been utilized to verify the projected WF-MFO. Numerous popular algorithms and MFO versions have been compared to the achieved results. In addition, the robustness of the proposed WF-MFO method has been evaluated using the Friedman rank test, the Wilcoxon rank test, and convergence analysis. Compared to other methods, the proposed WF-MFO algorithm provides higher quality solutions and converges more quickly, as shown by the experiments. Furthermore, the proposed WF-MFO has been used to the solution of two engineering design issues, with striking success. The improved performance of the proposed WF-MFO algorithm for addressing larger dimensional optimization problems is guaranteed by analyses of numerical data, statistical tests, and convergence performance.
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References
Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 89, 228–249.
McCarthy, J. F. (1989). Block-conjugate-gradient method. Physical Review D, 40(6), 2149.
Wu, G. H., Pedrycz, W., Suganthan, P. N., & Mallipeddi, R. (2015). A variable reduction strategy for evolutionary algorithms handling equality constraints. Applied Soft Computing, 37, 774–786.
Nama, S., & Saha, A. (2019). A novel hybrid backtracking search optimization algorithm for continuous function optimization. Decision Science Letters, 8(2), 163–174.
Nama, S., & Saha, A. (2018). An ensemble symbiosis organisms search algorithm and its application to real world problems. Decision Science Letters, 7(2), 103–118.
Sharma, A., Sharma, A., Averbukh, M., Rajput, S., Jately, V., Choudhury, S., & Azzopardi, B. (2022). Improved moth flame optimization algorithm based on opposition-based learning and Lévy flight distribution for parameter estimation of solar module. Energy Reports, 8, 6576–6592.
Hou, G. L., Gong, L. J., Hu, B., Su, H. L., Huang, T., Huang, C. Z., Fan, W., & Zhao, Y. Z. (2022). Application of fast adaptive moth-flame optimization in flexible operation modeling for supercritical unit. Energy, 239, 121843.
Ma, M. X., Wu, J., Shi, Y., Yue, L. F., Yang, C., & Chen, X. (2022). Chaotic random opposition-based learning and cauchy mutation improved moth-flame optimization algorithm for intelligent route planning of multiple uavs. IEEE Access, 10, 49385–49397.
Khan, M. A., Arshad, H., Damaševičius, R., Alqahtani, A., Alsubai, S., Binbusayyis, A., Nam, Y. Y., & Kang, B.-G. (2022). Human gait analysis: a sequential framework of lightweight deep learning and improved moth-flame optimization algorithm. Computational Intelligence and Neuroscience, 2022, 1–13.
Ab. Rashid, M.F.F., Mohd Rose, A. N., & Nik Mohamed, N. M. Z., (2022). Hybrid flow shop scheduling with energy consumption in machine shop using moth flame optimization. In: Recent Trends in Mechatronics Towards Industry 4.0: Selected Articles from iM3F 2020, (pp. 77–86). Springer Singapore.
Ramachandran, R., Satheesh Kumar, J., Madasamy, B., & Veerasamy, V. (2021). A hybrid MFO-GHNN tuned self-adaptive FOPID controller for ALFC of renewable energy integrated hybrid power system. IET Renewable Power Generation, 15(7), 1582–1595.
Khalilpourazari, S., & Khalilpourazary, S. (2019). An efficient hybrid algorithm based on water cycle and moth-flame optimization algorithms for solving numerical and constrained engineering optimization problems. Soft Computing, 23(5), 1699–1722.
Xu, Y. T., Chen, H. L., Luo, J., Zhang, Q., Jiao, S., & Zhang, X. Q. (2019). Enhanced moth-flame optimizer with mutation strategy for global optimization. Information Sciences, 492, 181–203.
Nadimi-Shahraki, M. H., Fatahi, A., Zamani, H., Mirjalili, S., & Abualigah, L. (2021). An improved moth-flame optimization algorithm with adaptation mechanism to solve numerical and mechanical engineering problems. Entropy, 23(12), 1637.
Xu, Y. T., Chen, H. L., Heidari, A. A., Luo, J., Zhang, Q., Zhao, X. H., & Li, C. Y. (2019). An efficient chaotic mutative moth-flame-inspired optimizer for global optimization tasks. Expert Systems with Applications, 129, 135–155.
Kaur, K., Singh, U., & Salgotra, R. (2020). An enhanced moth flame optimization. Neural Computing and Applications, 32(7), 2315–2349.
Gu, W. L., & Xiang, G. L. (2021). Improved moth flame optimization with multioperator for solving real-world optimization problems. 2021 IEEE 5th advanced information technology, electronic and automation control conference (IAEAC) (vol. 5, pp. 2459–2462). https://doi.org/10.1109/IAEAC50856.2021.9390876
Nadimi-Shahraki, M. H., Fatahi, A., Zamani, H., Mirjalili, S., Abualigah, L., & Abd Elaziz, M. (2021). Migration-based moth-flame optimization algorithm. Processes, 9(12), 2276.
Li, Z. F., Zeng, J. H., Chen, Y. Q., Ma, G., & Liu, G. Y. (2021). Death mechanism-based moth–flame optimization with improved flame generation mechanism for global optimization tasks. Expert Systems with Applications, 183, 115436. https://doi.org/10.1016/j.eswa.2021.115436
Shan, W. F., Qiao, Z. L., Heidari, A. A., Chen, H. L., Turabieh, H., & Teng, Y. T. (2021). Double adaptive weights for stabilization of moth flame optimizer: balance analysis, engineering cases, and medical diagnosis. Knowledge-Based Systems, 214, 106728. https://doi.org/10.1016/j.knosys.2020.106728
Sahoo, S. K., Saha, A. K., Sharma, S., Mirjalili, S., & Chakraborty, S. (2022). An enhanced moth flame optimization with mutualism scheme for function optimization. Soft Computing, 26, 1–28.
Khan, B. S., Raja, M. A. Z., Qamar, A., & Chaudhary, N. I. (2021). Design of moth flame optimization heuristics for integrated power plant system containing stochastic wind. Applied Soft Computing, 104, 107193.
Pelusi, D., Mascella, R., Tallini, L., Nayak, J., Naik, B., & Deng, Y. (2020). An improved moth-flame optimization algorithm with hybrid search phase. Knowledge-Based Systems, 191, 105277.
Kigsirisin, S., & Miyauchi, H. (2021). Short-term operational scheduling of unit commitment using binary alternative moth-flame optimization. IEEE Access, 9, 12267–12281.
Sapre, S., & Mini, S. (2021). Emulous mechanism based multi-objective moth–flame optimization algorithm. Journal of Parallel and Distributed Computing, 150, 15–33.
Zhang, Z. D., Qin, H., Yao, L. Q., Liu, Y. Q., Jiang, Z. Q., Feng, Z. K., & Ouyang, S. (2020). Improved multi-objective moth-flame optimization algorithm based on R-domination for cascade reservoirs operation. Journal of Hydrology, 581, 124431.
Dabba, A., Tari, A., Meftali, S., & Mokhtari, R. (2021). Gene selection and classification of microarray data method based on mutual information and moth flame algorithm. Expert Systems with Applications, 166, 114012.
Kadry, S., Rajinikanth, V., Raja, N., Jude Hemanth, D., Hannon, N., & Raj, A. N. J. (2021). Evaluation of brain tumor using brain MRI with modified-moth-flame algorithm and Kapur’s thresholding: A study. Evolutionary Intelligence, 14(2), 1053–1063.
Sapre, S., & Mini, S. (2021). A differential moth flame optimization algorithm for mobile sink trajectory. Peer-to-Peer Networking and Applications, 14(1), 44–57.
Dash, S. P., Subhashini, K. R., & Satapathy, J. K. (2020). Optimal location and parametric settings of FACTS devices based on JAYA blended moth flame optimization for transmission loss minimization in power systems. Microsystem Technologies, 26(5), 1543–1552.
Sahoo, S. K., & Saha, A. K. (2022). A hybrid moth flame optimization algorithm for global optimization. Journal of Bionic Engineering, 19(5), 1522–1543. https://doi.org/10.1007/s42235-022-00207-y
Sahoo, S. K., Saha, A. K., Nama, S., & Masdari, M. (2022). An improved moth flame optimization algorithm based on modified dynamic opposite learning strategy. Artificial Intelligence Review. https://doi.org/10.1007/s10462-022-10218-0
Chakraborty, S., Saha, A. K., Sharma, S., Sahoo, S. K., & Pal, G. (2022). Comparative performance analysis of differential evolution variants on engineering design problems. Journal of Bionic Engineering, 19(4), 1140–1160. https://doi.org/10.1007/s42235-022-00190-4
Sahoo, S. K., Saha, A. K., Ezugwu, A. E., Agushaka, J. O., Abuhaija, B., Alsoud, A. R., & Abualigah, L. (2022). Moth flame optimization: theory, modifications, hybridizations, and applications. Archives of Computational Methods in Engineering. https://doi.org/10.1007/s11831-022-09801-z
Sahoo, S. K., & Saha, A. K. (2022). A modernized moth flame optimization algorithm for higher dimensional problems. In: ICSET: International Conference on Sustainable Engineering and Technology, (vol. 1(1), pp. 9–20).
Bigham, A., & Gholizadeh, S. (2020). Topology optimization of nonlinear single-layer domes by an improved electro-search algorithm and its performance analysis using statistical tests. Structural and Multidisciplinary Optimization, 62, 1821–1848.
Gholizadeh, S., Razavi, N., & Shojaei, E. (2019). Improved black hole and multiverse algorithms for discrete sizing optimization of planar structures. Engineering Optimization, 51(10), 1645–1667.
Gholizadeh, S., Davoudi, H., & Fattahi, F. (2017). Design of steel frames by an enhanced moth-flame optimization algorithm. Steel & Composite Structures, 24(1), 129–140.
Gholizadeh, S., & Aligholizadeh, V. (2019). Reliability-based optimum seismic design of RC frames by a metamodel and metaheuristics. The Structural Design of Tall and Special Buildings, 28(1), e1552. https://doi.org/10.1002/tal.1552
Wang, G. G., Deb, S., & Cui, Z. H. (2019). Monarch butterfly optimization. Neural Computing and Applications, 31(7), 1995–2014.
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, 111, 300–323.
Wang, G. G. (2018). Moth search algorithm: A bio-inspired metaheuristic algorithm for global optimization problems. Memetic Computing, 10(2), 151–164.
Yang, Y. T., Chen, H. L., Heidari, A. A., & Gandomi, A. H. (2021). Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts. Expert Systems with Applications, 177, 114864.
Ahmadianfar, I., Heidari, A. A., Gandomi, A. H., Chu, X. F., & Chen, H. L. (2021). RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method. Expert Systems with Applications, 181, 115079.
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.
Ahmadianfar, I., Heidari, A. A., Noshadian, S., Chen, H. L., & Gandomi, A. H. (2022). INFO: An efficient optimization algorithm based on weighted mean of vectors. Expert Systems with Applications, 195, 116516.
Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. L. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849–872.
Nadimi-Shahraki, M. H., Taghian, S., Mirjalili, S., Ewees, A. A., Abualigah, L., & Abd Elaziz, M. (2021). Mtv-mfo: Multi-trial vector-based moth-flame optimization algorithm. Symmetry, 13(12), 2388.
Hai, T., Zhou, J. C., Masdari, M., & Marhoon, H. A. (2023). A hybrid marine predator algorithm for thermal-aware routing scheme in wireless body area networks. Journal of Bionic Engineering, 20(1), 81–104.
Gharehchopogh, F. S., Nadimi-Shahraki, M. H., Barshandeh, S., Abdollahzadeh, B., & Zamani, H. (2023). Cqffa: A chaotic quasi-oppositional farmland fertility algorithm for solving engineering optimization problems. Journal of Bionic Engineering, 20(1), 158–183.
Sharma, S., Khodadadi, N., Saha, A. K., Gharehchopogh, F. S., & Mirjalili, S. (2022). Non-dominated sorting advanced butterfly optimization algorithm for multi-objective problems. Journal of Bionic Engineering. https://doi.org/10.1007/s42235-022-00288-9
Ezugwu, A. E., Agushaka, J. O., Abualigah, L., Mirjalili, S., & Gandomi, A. H. (2022). Prairie dog optimization algorithm. Neural Computing and Applications, 34(22), 20017–20065.
Houssein, E. H., Saad, M. R., Ali, A. A., & Shaban, H. (2023). An efficient multi-objective gorilla troops optimizer for minimizing energy consumption of large-scale wireless sensor networks. Expert Systems with Applications, 212, 118827.
Yu, C. Y., Heidari, A. A., & Chen, H. L. (2020). A quantum-behaved simulated annealing algorithm-based moth-flame optimization method. Applied Mathematical Modelling, 87, 1–19.
Long, W., Jiao, J. J., Liang, X. M., & Tang, M. Z. (2018). An exploration-enhanced grey wolf optimizer to solve high-dimensional numerical optimization. Engineering Applications of Artificial Intelligence, 68, 63–80.
Chakraborty, S., Nama, S., & Saha, A. K. (2022). An improved symbiotic organisms search algorithm for higher dimensional optimization problems. Knowledge-Based Systems, 236, 107779.
Li, Y., Yu, X. M., & Liu, J. S. (2022). Enhanced butterfly optimization algorithm for large-scale optimization problems. Journal of Bionic Engineering, 19, 1–17.
Chakraborty, S., Saha, A. K., Chakraborty, R., & Saha, M. (2021). An enhanced whale optimization algorithm for large scale optimization problems. Knowledge-Based Systems, 233, 107543.
Long, W., Wu, T. B., Liang, X. M., & Xu, S. J. (2019). Solving high-dimensional global optimization problems using an improved sine cosine algorithm. Expert Systems with Applications, 123, 108–126.
Wang, H., Liang, M. N., Sun, C. L., Zhang, G. C., & Xie, L. P. (2021). Multiple-strategy learning particle swarm optimization for large-scale optimization problems. Complex & Intelligent Systems, 7(1), 1–16.
Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67–82. https://doi.org/10.1109/4235.585893
Molina, D., LaTorre, A., & Herrera, F. (2018). SHADE with iterative local search for large-scale global optimization. In: 2018 IEEE congress on evolutionary computation (CEC) (pp. 1–8). IEEE.
Wang, M., Wang, J. S., Li, X. D., Zhang, M., & Hao, W. K. (2022). Harris hawk optimization algorithm based on cauchy distribution inverse cumulative function and tangent flight operator. Applied Intelligence, 52, 10999–11026.
Houssein, E. H., Saad, M. R., Hashim, F. A., Shaban, H., & Hassaballah, M. (2020). Lévy flight distribution: A new metaheuristic algorithm for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 94, 103731.
Krohling, R. A., & dos Santos Coelho, L. (2006). PSO-E: Particle swarm with exponential distribution. IEEE International Conference on Evolutionary Computation, 2006, 1428–1433.
Layeb, A. (2022). Tangent search algorithm for solving optimization problems. Neural Computing and Applications, 34(11), 8853–8884.
Waloddi, W. (1951). A statistical distribution function of wide applicability. Journal of Applied Mechanics, Transactions ASME, 18(3), 293–297.
Layeb, A. (2022). Differential evolution algorithms with novel mutations, adaptive parameters and weibull flight operator [Preprint]. In Review. https://doi.org/10.21203/rs.3.rs-1898342/v1
Salgotra, R., Singh, U., Saha, S., & Gandomi, A. H. (2021). Self adaptive cuckoo search: Analysis and experimentation. Swarm and Evolutionary Computation, 60, 100751. https://doi.org/10.1016/j.swevo.2020.100751
Yazıcı, İ, Yaylacı, E. K., Cevher, B., Yalçın, F., & Yüzkollar, C. (2021). A new MPPT method based on a modified Fibonacci search algorithm for wind energy conversion systems. Journal of Renewable and Sustainable Energy, 13(1), 013304.
Ramaprabha, R. (2012). Maximum power point tracking of partially shaded solar PV system using modified Fibonacci search method with fuzzy controller. International Journal of Electrical Power & Energy Systems, 43, 754–765.
Jamil, M., & Yang, X. S. (2013). A literature survey of benchmark functions for global optimisation problems. International Journal of Mathematical Modelling and Numerical Optimisation, 4(2), 150. https://doi.org/10.1504/IJMMNO.2013.055204
Storn, R., & Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341–359.
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In: Proceedings of ICNN’95—international conference on neural networks (vol. 4, pp. 1942–1948).
Yang, X. S. (2012). Flower pollination algorithm for global optimization. in: Unconventional Computation and Natural Computation, Lecture Notes in Computer Science, 7445, (pp. 240–249).
Arora, S., & Singh, S. (2015). Butterfly algorithm with levy flights for global optimization. 2015 International Conference on Signal Processing, Computing and Control (ISPCC), Waknaghat, India, (pp. 220–224).
Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp swarm algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163–191.
Mirjalili, S. (2016). SCA: A sine cosine algorithm for solving optimization problems. Knowledge-Based Systems, 96, 120–133.
Soliman, G. M. A., Khorshid, M. M. H., & Abou-El-Enien, T. H. M. (2016). Modified moth-flame optimization algorithms for terrorism prediction. International Journal of Application or Innovation in Engineering and Management, 5(7), 12.
Elsakaan, A. A., El-Sehiemy, R. A., Kaddah, S. S., & Elsaid, M. I. (2018). An enhanced moth-flame optimizer for solving non-smooth economic dispatch problems with emissions. Energy, 157, 1063–1078.
Chen, C. C., Wang, X. C., Yu, H. L., Wang, M. J., & Chen, H. L. (2021). Dealing with multi-modality using synthesis of Moth-flame optimizer with sine cosine mechanisms. Mathematics and Computers in Simulation, 188, 291–318.
Azizi, M., Talatahari, S., & Gandomi, A. H. (2022). Fire hawk optimizer: a novel metaheuristic algorithm. Artificial Intelligence Review, 56(1), 287–363.
Sharma, S., Chakraborty, S., Saha, A. K., Nama, S., & Sahoo, S. K. (2022). mLBOA: A modified butterfly optimization algorithm with lagrange interpolation for global optimization. Journal of Bionic Engineering. https://doi.org/10.1007/s42235-022-00175-3
Nama, S., Sharma, S., Saha, A. K., & Gandomi, A. H. (2022). A quantum mutation-based backtracking search algorithm. Artificial Intelligence Review, 55(4), 3019–3073.
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.
Nama, S. (2022). A novel improved SMA with quasi reflection operator: Performance analysis, application to the image segmentation problem of Covid-19 chest X-ray images. Applied Soft Computing, 118, 108483.
Tang, A., Zhou, H., Han, T., & Xie, L. (2021). A modified manta ray foraging optimization for global optimization problems. IEEE Access, 9, 128702–128721.
Sharma, S., & Saha, A. K. (2020). m-MBOA: A novel butterfly optimization algorithm enhanced with mutualism scheme. Soft Computing, 24(7), 4809–4827.
Gu, H. M., & Wang, X. (2016). Application of nsga-ii algorithm in the design of car body lateral crashworthiness. DEStech Transactions on Materials Science and Engineering, icmeat.
Yildiz, A. R., Abderazek, H., & Mirjalili, S. (2020). A comparative study of recent non-traditional methods for mechanical design optimization. Archives of Computational Methods in Engineering, 27(4), 1031–1048. https://doi.org/10.1007/s11831-019-09343-x
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Appendix 1: Formulation of 29 benchmark functions
Appendix 1: Formulation of 29 benchmark functions
Sl. no | Functions | Formulation of objective functions | d | F min | Search space |
---|---|---|---|---|---|
Unimodal benchmark functions | |||||
F1 | Sphere | \(f\left( x \right) = \sum\nolimits_{j = 1}^{d} {x_{j}^{2} }\) | 30 | 0 | [− 100, 100] |
F2 | Matyas | \(f\left( x \right) = 0.26\left( {x_{1}^{2} + x_{2}^{2} } \right) - 0.48x_{1} x_{2}\) | 2 | 0 | [− 10, 10] |
F3 | Sumsquare | \(f\left( x \right) = \sum\nolimits_{i = 1}^{D} {x_{i}^{2} \times i}\) | 30 | 0 | [− 10, 10] |
F4 | Zettl | \(f\left( x \right) = \left( {x - 1^{2} + x - 2^{2} - 2x_{1} } \right)^{2} + 0.25x_{1}\) | 2 | − 0.00379 | [− 1, 5] |
F5 | Zakhrov | \(f\left( x \right) = \sum\nolimits_{j = 1}^{d} {x_{j}^{2} } + \left( {0.5\sum\nolimits_{j = 1}^{d} {jx_{j} } } \right)^{2} + \left( {0.5\sum\nolimits_{j = 1}^{d} {jx_{j} } } \right)^{4}\) | 2 | 0 | [− 5, 10] |
F6 | High conditioned elliptic function | \(f\left( x \right) = \sum\nolimits_{j = 1}^{d} {\left( {1000000^{{\left( {\frac{j - 1}{{D - 1}}} \right)}} \times x_{j}^{2} } \right)}\) | 30 | 0 | [− 100, 100] |
F7 | Brown | \(f\left( x \right) = \sum\nolimits_{j = 1}^{n - 1} {\left( {x_{j}^{2} } \right)^{{(x_{j + 1}^{2} + 1)}} + \left( {\left( {x_{j + 1}^{2} } \right)^{{(x_{j}^{2} + 1)}} } \right)}\) | 30 | 0 | [− 1, 4] |
F8 | Cube | \(f\left( x \right) = 100\left( {x_{2} - x_{1}^{3} } \right)^{2} + \left( {1 - x_{1} } \right)^{2}\) | 2 | 0 | [− 10, 10] |
F9 | Rotated hyperellipsoid | \(f\left( x \right) = \sum\nolimits_{j = 1}^{d} {\left( {d + 1 - j} \right)x_{j}^{2} }\) | 30 | 0 | [− 100, 100] |
F10 | Schwefel 1.2 | \(f\left( x \right) = \sum\nolimits_{j = 1}^{d} {\sum\nolimits_{k = 1}^{j} {x_{j}^{2} } }\) | 30 | 0 | [− 100, 100] |
F11 | Schwefel 2.21 | \(f\left( x \right) = \max \left\{ {\left| {x_{j} } \right|, 1 \le j \le d} \right\}\) | 30 | 0 | [− 100, 100] |
F12 | Rosenbrock | \(f\left( x \right) = \sum\nolimits_{j = 1}^{d} {[100\left( {x_{j + 1} - x_{j}^{2} } \right)^{2} + \left( {x_{j} - 1} \right)^{2} ]}\) | 30 | 0 | [− 2.048, 2.048] |
F13 | Cigar | \(f\left( x \right) = 10^{6} \sum\nolimits_{j = 1}^{d} {x_{j}^{2} }\) | 30 | 0 | [− 100, 100] |
F14 | Step | \(f\left( x \right) = \mathop \sum \limits_{j = 1}^{d} \left( {x_{j} + 0.5} \right)^{2}\) | 30 | 0 | [− 100, 100] |
Multimodal benchmark functions | |||||
F15 | Bohachevsky | \(f\left( x \right) = x_{1}^{2} + 2x_{2}^{2} - 0.3\cos \left( {3\pi x_{1} } \right) - 0.3\) | 2 | 0 | [− 100, 100] |
F16 | Bohachevsky 3 | \(f\left( x \right) = x_{1}^{2} + 2x_{2}^{2} - 0.3\cos \left( {3\pi x_{1} } \right) - 0.3\) | 2 | 0 | [− 50, 50] |
F17 | Levy | \(f\left( x \right) = \sin^{2} \left( {\pi x_{1} } \right) + \sum\nolimits_{i = 1}^{D - 1} {\left( {x_{i} - 1} \right)^{2} \left[ {1 + 10\sin^{2} \left( {\pi x_{i} + 1} \right)} \right] + \left( {x_{D} - 1} \right)^{2} \left[ {1 + \sin^{2} \left( {2\pi x_{D} } \right)} \right]}\) where \(x_{i} = 1 + \frac{1}{4}(x_{i} - 1), i = 1,2, \ldots D\) | 30 | 0 | [− 10, 10] |
F18 | Alpine | \(f\left( x \right) = \sum\nolimits_{i = 1}^{D} {\left| {x_{i} \sin (x_{i} ) + 0.1x_{i} } \right|}\) | 30 | 0 | [− 10, 10] |
F19 | Schaffers | \(f\left( x \right) = 0.5 + \frac{{\sin^{2} \left( {x_{1}^{2} + x_{2}^{2} } \right) - 0.5}}{{\left[ {1 + 0.001\left( {x_{1}^{2} + x_{2}^{2} } \right)} \right]^{2} }}\) | 2 | 0 | [− 100, 100] |
F20 | Salomon | \(f\left( x \right) = 1 - \cos \left( {2\pi \sqrt {\sum\nolimits_{j = 1}^{d} {x_{j}^{2} } } } \right) + 0.1\sqrt {\sum\nolimits_{j = 1}^{d} {x_{j}^{2} } }\) | 30 | 0 | [− 100, 100] |
F21 | Penalized 2 | \(f\left( x \right) = 0.1\left\{ { 10\sin^{2} \left( {\pi x_{i} } \right) + \sum\nolimits_{i = 1}^{D - 1} {\left( {x_{i} - 1} \right)^{2} [1 + 10\sin^{2} \left( {3\pi x_{i + 1} } \right) + \left( {x_{D} - 1} \right)^{2} [1 + \sin^{2} \left( {2\pi x_{D} } \right)]]} } \right\} + \sum\nolimits_{i = 1}^{D} {u\left( {x_{i} ,5,100,4} \right)}\) | 30 | 0 | [− 50, 50] |
F22 | Inverted cosine mixture | \(f\left( x \right) = 0.1 \times 30 - \left[ {0.1 \times \sum\nolimits_{j = 1}^{d} {5\pi x_{j} } - \sum\nolimits_{j = 1}^{d} {x_{j}^{2} } } \right]\) | 30 | 0 | [− 1, 1] |
F23 | Modified sphere | \(f\left( x \right) = \sum\nolimits_{j = 1}^{6} {\frac{{\left( {x_{j}^{2} \times 2^{j} } \right) - 1745}}{899}}\) | 30 | 0 | [− 5.12, 5.12] |
F24 | Drop wave | \(f\left( x \right) = 1 - \frac{{1 + \cos \left( {12\sqrt {x_{1}^{2} + x_{2}^{2} } } \right)}}{{0.5\left( {x_{1}^{2} + x_{2}^{2} } \right) + 2}}\) | 2 | − 1 | [− 5.12, 5.12] |
F25 | Egg-holder | \(f\left( x \right) = \left[ { - \left( {47 + x_{i + 1} } \right)\sin \begin{array}{*{20}c} {\sqrt {\left| {x_{i + 1} + \frac{{x_{i} }}{2} + 47} \right|} } \\ { - x_{i} \sin \sqrt {x_{i} - \left( {x_{i + 1} + 47} \right)} } \\ \end{array} } \right]\) | 30 | − 959.6407 | [− 512, 512] |
F26 | Rastrigin | \(f\left( x \right) = 10D + \sum\nolimits_{j = 1}^{d} {[x_{j}^{2} - 10\cos (2\pi x_{j} )]}\) | 30 | 0 | [− 5.12, 5.12] |
F27 | Schwefel 2.26 | \(f\left( x \right) = - \sum\nolimits_{j = 1}^{d} {x_{j} \sin \left( {\sqrt {\left| {x_{j} } \right|} } \right)}\) | 30 | − 418.982 | [− 500, 500] |
F28 | Ackley | \(f\left( x \right) = 20 + e - 20e^{{\frac{1}{d}\left( {\sqrt {\left( {\frac{1}{d}\sum\nolimits_{j = 1}^{d} {x_{j}^{2} } } \right)} } \right)}} - e^{{\frac{1}{d}\left( {\sum \cos \left( {2\pi x_{j} } \right)} \right)}}\) | 30 | 0 | [− 32.768, 32.768] |
F29 | Griewank | \(f\left( x \right) = \sum\nolimits_{j = 1}^{d} {\frac{{x_{j}^{2} }}{4000}} - \prod\nolimits_{j = 1}^{d} {\cos \left( {\frac{{x_{j} }}{\sqrt j }} \right) - 1}\) | 30 | 0 | [− 600, 600] |
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Sahoo, S.K., Sharma, S. & Saha, A.K. A Novel Variant of Moth Flame Optimizer for Higher Dimensional Optimization Problems. J Bionic Eng 20, 2389–2415 (2023). https://doi.org/10.1007/s42235-023-00357-7
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DOI: https://doi.org/10.1007/s42235-023-00357-7