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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 13/2022

06.03.2022 | Original Article

FP-SMA: an adaptive, fluctuant population strategy for slime mould algorithm

verfasst von: Jassim Alfadhli, Ali Jaragh, Mohammad Gh. Alfailakawi, Imtiaz Ahmad

Erschienen in: Neural Computing and Applications | Ausgabe 13/2022

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Abstract

In this paper, an adaptive Fluctuant Population size Slime Mould Algorithm (FP-SMA) is proposed. Unlike the original SMA where population size is fixed in every epoch, FP-SMA will adaptively change population size in order to effectively balance exploitation and exploration characteristics of SMA’s different phases. Experimental results on 13 standard and 30 IEEE CEC2014 benchmark functions have shown that FP-SMA can achieve significant reduction in run time while maintaining good solution quality when compared to the original SMA. Typical saving in terms of function evaluations for all benchmarks was between 20 and 30% on average with a maximum being as high as 60% in some cases. Therefore, with its higher computation efficiency, FP-SMA is much more favorable choice as compared to SMA in time stringent applications.
Vorheriger Artikel Disturbance rejection and high dynamic quadrotor control based on reinforcement learning and supervised learning
Nächster Artikel Supplementary-architecture weight-optimization neural networks

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Literatur
1.
2.
Abdel-Basset M, Mohamed R, Chakrabortty RK, Ryan MJ, Mirjalili S (2021) An efficient binary slime mould algorithm integrated with a novel attacking-feeding strategy for feature selection. Comput Ind Eng 153:107078
3.
4.
Anter AM, Oliva D, Thakare A, Zhang Z (2021) Afcm-lsma: new intelligent model based on lévy slime mould algorithm and adaptive fuzzy c-means for identification of covid-19 infection from chest x-ray images. Adv Eng Inform 49:101317
5.
Arabas J, Michalewicz Z, Mulawka J (1994) Gavaps-a genetic algorithm with varying population size. In: Proceedings of the 1st IEEE conference on evolutionary computation. IEEE world congress on computational intelligence. IEEE, pp 73–78
6.
Brest J, Greiner S, Boškovič B, Mernik M, Žumer V (2006) Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans Evol Comput 10:646–657
7.
Chen D, Zhao C (2009) Particle swarm optimization with adaptive population size and its application. Appl Soft Comput 9(1):39–48
8.
Chen Z, Liu W (2020) An efficient parameter adaptive support vector regression using k-means clustering and chaotic slime mould algorithm. IEEE Access 8:156851–156862
9.
Cui L, Li G, Zhu Z, Lin Q, Wen Z, Lu N, Wong KC, Chen J (2017) A novel artificial bee colony algorithm with an adaptive population size for numerical function optimization. Inf Sci 414:53–67MathSciNetMATH
10.
Cui Z, Hou X, Zhou H, Lian W, Wu J (2020) Modified slime mould algorithm via levy flight. In: 2020 13th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI). IEEE, pp 1109–1113 (2020)
11.
İzci D ES (2021) Comparative performance analysis of slime mould algorithm for efficient design of proportional–integral–derivative controller. Electrica 21:151–159
12.
Dhal KG, Das A, Sahoo S, Das R, Das S (2019) Measuring the curse of population size over swarm intelligence based algorithms. Evol Syst 12:1–48
13.
Dokeroglu T, Sevinc E, Kucukyilmaz T, Cosar A (2019) A survey on new generation metaheuristic algorithms. Comput Ind Eng 137:106040
14.
El-Fergany AA (2021) Parameters identification of pv model using improved slime mould optimizer and lambert w-function. Energy Rep 7:875–887
15.
Gao Z, Zhao J, Li S (2020) The hybridized slime mould and particle swarm optimization algorithms. In: 2020 IEEE 3rd international conference on automation, electronics and electrical engineering (AUTEEE). IEEE, pp 304–308 (2020)
16.
17.
Goldberg DE, Holland JH (1988) Genetic algorithms and machine learning. Mach Learn 3:95–99
18.
Hassan MH, Kamel S, Abualigah L, Eid A (2021) Development and application of slime mould algorithm for optimal economic emission dispatch. Expert Syst Appl 182:115205
19.
20.
Houssein EH, Mahdy MA, Blondin MJ, Shebl D, Mohamed WM (2021) Hybrid slime mould algorithm with adaptive guided differential evolution algorithm for combinatorial and global optimization problems. Expert Syst Appl 174:114689
21.
Houssein EH, Mahdy MA, Shebl D, Manzoor A, Sarkar R, Mohamed WM (2022) An efficient slime mould algorithm for solving multi-objective optimization problems. Expert Syst Appl 187:115870
22.
Hussain K, Salleh MNM, Cheng S, Shi Y (2019) Metaheuristic research: a comprehensive survey. Artif Intell Rev 52(4):2191–2233
23.
Ibrahim RA, Yousri D, Abd Elaziz M, Alshathri S, Attiya I (2021) Fractional calculus-based slime mould algorithm for feature selection using rough set. IEEE Access 9:131625–131636
24.
Khunkitti S, Siritaratiwat A, Premrudeepreechacharn S (2021) Multi-objective optimal power flow problems based on slime mould algorithm. Sustainability 13(13):7448
25.
Koumousis VK, Katsaras CP (2006) A saw-tooth genetic algorithm combining the effects of variable population size and reinitialization to enhance performance. IEEE Trans Evol Comput 10(1):19–28
26.
Li L, Pan TS, Sun XX, Chu SC, Pan JS (2021) A novel binary slime mould algorithm with au strategy for cognitive radio spectrum allocation. Int J Comput Intell Syst 14(1):1–18
27.
28.
Liang JJ, Qu BY, Suganthan PN (2013) Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization, vol 635. Technical report Zhengzhou, China
29.
Liu L, Zhao D, Yu F, Heidari AA, Ru J, Chen H, Mafarja M, Turabieh H, Pan Z (2021) Performance optimization of differential evolution with slime mould algorithm for multilevel breast cancer image segmentation. Comput Biol Med 138:104910
30.
Liu M, Li Y, Huo Q, Li A, Zhu M, Qu N, Chen L, Xia M (2020) A two-way parallel slime mold algorithm by flow and distance for the travelling salesman problem. Appl Sci 10(18):6180
31.
Liu Y, Heidari AA, Ye X, Liang G, Chen H, He C (2021) Boosting slime mould algorithm for parameter identification of photovoltaic models. Energy 234:121164
32.
33.
Mostafa M, Rezk H, Aly M, Ahmed EM (2020) A new strategy based on slime mould algorithm to extract the optimal model parameters of solar pv panel. Sustain Energy Technol Assess 42:100849
34.
35.
Naik MK, Panda R, Abraham A (2021) Adaptive opposition slime mould algorithm. Soft Comput 25(22):14297–14313
36.
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. Appl Soft Comput 113:107955
37.
38.
Nguyen TT, Wang HJ, Dao TK, Pan JS, Liu JH, Weng S (2020) An improved slime mold algorithm and its application for optimal operation of cascade hydropower stations. IEEE Access 8:226754–226772
39.
Poláková R, Tvrdík J, Bujok P (2019) Differential evolution with adaptive mechanism of population size according to current population diversity. Swarm Evolut Comput 50:100519
40.
Piotrowski A (2017) Review of differential evolution population size. Swarm Evol Comput 32:1–24
41.
Piotrowski AP, Napiorkowski JJ, Piotrowska AE (2020) Population size in particle swarm optimization. Swarm Evol Comput 58:100718
42.
Polakova R, Tvrdik J, Bujok P (2014) Controlled restart in differential evolution applied to CEC2014 benchmark functions. In: IEEE congress on evolutionary computation, pp 2230–2236
43.
Precup RE, David RC, Roman RC, Petriu EM, Szedlak-Stinean AI (2021) Slime mould algorithm-based tuning of cost-effective fuzzy controllers for servo systems. Int J Comput Intell Syst 14(1):1042–1052
44.
Premkumar M, Jangir P, Sowmya R, Alhelou HH, Heidari AA, Chen H (2021) Mosma: multi-objective slime mould algorithm based on elitist non-dominated sorting. IEEE Access 9:3229–3248
45.
Rizk-Allah RM, Hassanien AE, Song D (2021) 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
46.
Shi B, Ye H, Zheng J, Zhu Y, Heidari AA, Zheng L, Chen H, Wang L, Wu P (2021) Early recognition and discrimination of covid-19 severity using slime mould support vector machine for medical decision-making. IEEE Access 9:121996–122015
47.
Storn R, Price K (1997) Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11:341–359MathSciNetMATH
48.
49.
Back T (1996) Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms. Oxford University Press on Demand
50.
Teo J (2006) Exploring dynamic self-adaptive populations in differential evolution. Softw Comput. 10:673–686
51.
Vashishtha G, Chauhan S, Singh M, Kumar R (2021) Bearing defect identification by swarm decomposition considering permutation entropy measure and opposition-based slime mould algorithm. Measurement 178:109389
52.
53.
54.
Wazery YM, Saber E, Houssein EH, Ali AA, Amer E (2021) An efficient slime mould algorithm combined with k-nearest neighbor for medical classification tasks. IEEE Access 9:113666–113682
55.
Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82
56.
Yang M, Cai Z, Li C, Guan J (2013) An improved adaptive differential evolution algorithm with population adaptation. In: GECCO ’13 proceedings of the 15th annual conference on genetic and evolutionary computation, pp 145–152
57.
Yang M, Li C, Cai Z, Guan J (2014) Differential evolution with auto-enhanced population diversity. IEEE Trans Cybern 45:302–315
58.
Yang XS, Deb S (2014) Cuckoo search: recent advances and applications. Neural Comput Appl 24(1):169–174
59.
Yu C, Heidari AA, Xue X, Zhang L, Chen H, Chen W (2021) Boosting quantum rotation gate embedded slime mould algorithm. Expert Syst Appl 181:115082
60.
Zhao J, Gao ZM (2020) The hybridized Harris hawk optimization and slime mould algorithm. In: Journal of physics: conference series, vol 1682. IOP Publishing, pp 012029
61.
Zhao S, Wang P, Heidari AA, Chen H, Turabieh H, Mafarja M, Li C (2021) Multilevel threshold image segmentation with diffusion association slime mould algorithm and Renyi’s entropy for chronic obstructive pulmonary disease. Comput Biol Med 134:104427
62.
Zheng R, Jia H, Abualigah L, Liu Q, Wang S (2021) Deep ensemble of slime mold algorithm and arithmetic optimization algorithm for global optimization. Processes 9(10):1774
63.
Zubaidi SL, Abdulkareem IH, Hashim KS, Al-Bugharbee H, Ridha HM, Gharghan SK, Al-Qaim FF, Muradov M, Kot P, Al-Khaddar R (2020) Hybridised artificial neural network model with slime mould algorithm: a novel methodology for prediction of urban stochastic water demand. Water 12(10):2692
Metadaten
Titel
FP-SMA: an adaptive, fluctuant population strategy for slime mould algorithm
verfasst von
Jassim Alfadhli
Ali Jaragh
Mohammad Gh. Alfailakawi
Imtiaz Ahmad
Publikationsdatum
06.03.2022
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 13/2022
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-022-07034-6

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