1 Introduction
2 Applications of differential evolution and particle swarm optimization against COVID-19
Topic | Paper | Problems/models | Dimensionality | Number of objectives | Algorithms used | Objective function | Number of runs | Number of function calls | Population size | Other control parameters | Comparison of performance | Comments |
---|---|---|---|---|---|---|---|---|---|---|---|---|
In host modeling of COVID-19 | Abuin et al. (2020) | Ordinary differential equations | 3 (?) | 1 | DE (Storn and Price, 1997) | RMSlogE | ? | ? | ? | ? | No | The same model as in Hernandez-Vargas and Velasco-Hernandez (2020). Dimensionality not clearly stated by the authors |
In host modeling of COVID-19 | Hernandez-Vargas and Velasco-Hernandez (2020) | Ordinary differential equations | 3 (?) | 1 | DE (Storn and Price, 1997) | RMSlogE | ? | ? | ? | ? | No | |
Epidemiology and management | Ames et al. (2020) | 1. SIR 2. SIHRD | 5 (SIR) 10 (SIHRD) | ? | 1. DE (?) 2. CMA-ES (?) 3. NSGA-II (?) | Specified | ? | 160.000 (CMA-ES for SIHRD) | 400 | ? | Only CMA-ES results are discussed, authors are satisfied | No references to algorithms used. No detailed information on the number of function calls for SIR or non-CMA-ES algorithms |
Epidemiology | Anand et al. (2020) | SIQR + testing | 2 | 1 | DE (Storn and Price, 1997) | MSE | ? | ? | ? | ? | No | |
Epidemiology | de Camino Beck (2020) | SEIRC | 2 | 1 | DE with gradient descend (?) | ? | ? | ? | ? | ? | No | No reference to the algorithm used |
Epidemiology | Comunian et al. (2020) | SIR | 5 | 1 | DE (Storn and Price, 1997) | Specified | 10 | 3.312 – 31.566 depending on the SIR variant | Default settings (no details) | Default settings (no details) | “Results obtained were very good” (Comunian et al. 2020) | Different SIR variants were tested. Very similar study to Giudici et al. (2020) |
Epidemiology | de Falco et al. (2020) | SIR + distancing | 3 | 1 | DE (Storn and Price, 1997) | RMSE | 1 | 50.000 | 50 | F = 0.7 CR = 0.9 rand/1/bin | No | |
Epidemiology | Fanelli and Piazza (2020) | SIRD | 4 or 6 | 1 | DE (Storn and Price, 1997) | ? | 30 | ? | ? | ? | No | Dimensionality depends on the application to the specific country |
Epidemiology | Freitas Reis et al. (2020) | SEIR | 10 | 1 | DE (Storn and Price, 1997) | Specified | ? | ? | ? | ? | No | |
Epidemiology | Giudici et al. (2020) | SIRD | 5 | 1 | DE (Storn and Price, 1997) | ? | 10 | ? | Default settings (no details) | Default settings (no details) | Algorithm “yielded good results” | Very similar study to Comunian et al. (2020) |
Epidemiology | Godreev et al. (2020) | SEIRD | 6 | 1 | DE (Storn and Price, 1997) | RMSE | ? | ? | ? | ? | No | |
Epidemiology | Krivorot’ko et al. (2020) | 1. SEIR-HZD 2. SEIR-D | 8 (SEIR-HZD) 9 (SEIR-D) | 1 | SEIRHZD: DE (?) SEIR-D: 1. DE (?) 2. SA (?) 3. GA (?) 4. PSO (?) | Specified | ? | ? | ? | ? | Although four algorithms were applied, their results were finally not compared | In the paper it is stated that the codes of all algorithms from Python library were used. However, the references to the specific scientific papers are lacking |
Epidemiology | Lobato et al. (2020) | SIRD | 4 | 1. SIRD 2. minimization of SIRD maximization of noise | 1 objective: 1. DE (Storn and Price 1997) 2. SFS (Salimi 2015) 3. GA (Holland, 1975) 4. FA (Yang 2008) 2 objectives: 1. MODE (Souza et al., (2015) 2. MOSFS (Lobato et al., 2020) 3. NSGA-II (Deb et al., 2002) 4. MOFA (Lobato and Steffen, 2013) | 1 objective: scaled MSE 2 objectives: scaled MSE and noise maximization | 20 | 6.250 | 25 | DE/MODE: F = 0.9 CR = 0.9 GA/NSGA-II: CR = 0.8 mutation = 0.01 FA/ MOFA: absorption = 0.9 attractivness = 0.9 SFS: authors refer to Salimi (2015) no information on other specific multi-objective parameters | Marginal differences between single objective algorithms: SFS and FA perform equally, DE worse by 0.001, GA worse by 0.002. No measure for comparison of bi-objective algorithms is given | |
Epidemiology | Quaranta et al. (2020) | SAIRD | 5 | 1 | DE (?) | Normalized MSE | ? | 1.500 | 30 | F = 0.9 CR = 0.5 current-to-best/1 | No | |
Epidemiology | Rica and Ruz (2020) | SIR | 5 | 1 | DE (Storn and Price, 1997) | MSE | ? | 15.000 | 15 | F—sampled for each generation from [0.5,1.0] CR = 0.7 | Comparison only with random search | Detailed discussion of the SIR parameters obtained |
Epidemiology | Ricardo and Hernandez-Vargas (2020) | SEIR | 3 | 1 | DE (Storn and Price, 1997) | RMSE | 3.000 | ? | ? | ? | No | |
Epidemiology and management | Libotte et al. (2020) | 1. SIR 2. vaccine management (VM) | 3 (SIR) 9 (VM) | 1 (SIR) 1–2 (VM) | SIR: DE (Storn and Price, (1997) VM: MODE (Lobato and Steffen, 2011) | SIR: scaled MSE VM: minimizing infected population and number of vaccines used | 20 | SIR and 1-obj. VM: 2500 2 objective VM: 5000 | SIR and 1-obj. VM: 25 2-obj. VM: 50 | DE and MODE: F = 0.8 CR = 0.8 rand/1/bin | No | There are 3 applications: 1. DE is used to optimize 3 SIR parameters; 2. DE is used to optimize vaccine use within 9 periods to minimize the number of infections; 3. MODE is used to optimize vaccine use within 9 periods to minimize the number of infections and the number of vaccines provided |
Epidemiology | Saif et al. (2021) | ANFIS for predicting the number of COVID-19 cases | ? | 1 | 1. DE (?) 2. PSO (?) 3. mutation BA (Saif et al., 2021) 4. GA (?) 5. FA (?) 6. HS (?) 7. TLBO (?) 8. BA (Pham et al., 2005) | RMSE | 10 | 5.000 | 25 (all algorithms) | DE: F = 0.9 CR = 0.2 PSO: c1 = 2 c2 = 2 w = 1 specified also for other algorithms | Results for India 1. mutation BA 2. PSO 3. BA 4. FA 5. TLBO 6. HS 7. DE 8. GA results for USA: 1. mutation BA 2. BA 3. PSO 4. FA 5. HS 6. TLBO 7. GA 8. DE | The specific variants of algorithms used are undefined, with exception of Bees Algorithm-based ones |
Epidemiology | Sanche et al. (2020) | Finding delays between infection and symptoms; modelling the spread of COVID-19 disease to various provinces of China | ? | 1 | DE (Storn and Price, 1995) | Maximization of likelihood | ? | ? | ? | ? | No | |
Epidemiology and management | Sainz-Pardo and Valero (2020) | Optimal allocation in space and time of COVID-19 infection tests based on SIR-kind of population epidemiology model | “Large number of parameters” | 1 | DE with directional information (Iorio and Li, 2006) | Specified | ? | 1.000 iterations (unclear number of function calls) | 5 | F generated randomly from [0,1] in each generation No crossover | No | Authors considered various numbers of COVID-19 tests, from 10.000 to 500.000. The number of saved infections by optimal allocation of tests is modeled with respect to the homogenous testing in time and space |
Human immunological response to COVID-19 | Xavier et al. (2020) | Model based on five ordinary differential equations | 11 | 1 | Specified | 1 (?) | ? | ? | ? | No | The details of constraint handling approach not specified | |
Molecular docking | Bhaliya and Shah (2020) | Molegro Virtual Docker | ? | 1 | Guided DE (Thomsen and Christensen, 2006) | ? | 10 | ? | ? | ? (also no information in Thomsen and Christiansen 2006) | No | DE is used to dock molecules with the virus within MVD program |
Molecular docking | de Castro et al. (2020) | Molegro Virtual Docker | ? | 1 | Guided DE (Thomsen and Christensen, 2006) | Specified | ? | ? | ? | ? (also no information in Thompsen and Christiansen 2006) | No | DE is used to dock molecules with the virus within MVD program |
Molecular docking | Sheybani et al. (2020) | Molegro Virtual Docker | ? | 1 | Guided DE (?) | ? | 10 | ? | ? | ? | No | No reference to Guided DE |
Molecular docking | Gonzalez-Paz et al. (2020) | Molegro Virtual Docker | ? | 1 | Guided DE (Thomsen and Christensen, 2006) | ? | 25 | ? | ? | ? | No | DE is used within MVD for drugs development |
x-ray image diagnostics | Abdel-Basset et al. (2020c) | x-ray image segmentation | Threshold levels 2–30 | 1 | 1. iL-SHADE (Brest et al., 2016) 2. HSMA-WOA (Abdel-Basset et al. (2020c) 3. FA (Erdmann et al., 2015) 4. WOA (Abd Elaziz et al., 2017) 5. SSA (Wang et al., 2020b) 6. HHA (Bao et al., 2019) 7. SMA (?) | Specified | 20 | 4.500 | 30 (for all algorithms) | No information on control parameters of algorithms other than HSMA-WOA and SMA | 1. HSMA-WOA 2. SMA 3. WOA 4. HHA 5. FA 6. SSA 7. iL-SHADE | The population size and the number of function calls highly inappropriate for iL-SHADE. It is unclear whether the linear population size reduction is used or not for iL-SHADE. It is also unclear how iL-SHADE was applied to topics like image segmentation |
x-ray image diagnostics | Abd Elaziz et al. (2020a) | Feature selection for x-ray chest images | 961 (?) | 1 combinatorial | 1. MRF-DE (Abd Elaziz et al., 2020a) 2. MRF (Zhao et al., 2020) 3. SCA (?) 4. GWO (?) 5. HGS (?) 6. WOA (?) 7. HHO (?) | Accuracy measure | ? (but more than 1) | ? (only evaluation time is given) | ? | ? | Averaged over 2 data sets: 1. MRF-DE 2. MRF 3. GWO 4. SCA 5. WOA 6. HGS 7. HHO | Original DE only hybridized with Manta Ray Foraging. Optimizers are used to choose features among those extracted from x-chest images by Fractional Multichannel Exponent Moments. These features are than used by classifier |
COVID-19 radiographs | Nowakova et al. (2020) | Column subset selection in matrixes | ? | 1 | DE (Storn and Price, 1997) | Specified | 51 | 40.000 | 20 | F = 0.9 CR = 0.9 | No | |
COVID-19 patient classification based on tommography chest images | Singh et al. (2020a) | Hyperparameters of CNN | 10 (mix of numerical and combinatorial variables) | 1 | DE (Storn and Price, 1997) | Specified | ? | 8.000 | 40 | F = 0.1 CR = 0.5 | No | DE is claimed to be multiobjective, but two objectives are de facto summed into a single objective problem |
COVID-19 patient classification based on tommography chest images | Singh et al. (2020b) | Hyperparameters of CNN | 10 (mix of numerical and combinatorial variables) | 2 | 1. MODE (Babu et al., 2005) 2. PSO (?) 3. GA (?) | Specified | ? | 1.500 (MODE) unclear for PSO and GA | 50 (MODE) unclear for PSO and GA | JADE-based mutation and adaptation of F and CR | Unclear | It is claimed that MODE (Babu et al., 2005) is used, but mutation and F, CR adaptation are different in this paper than in Babu et al. (2005). Variants of PSO and GA are not specified. Very different number of epochs is used by CNN trained by PSO, GA and MODE |
COVID-19 patient classification based on computer tomography | Punitha et al. (2020) | Feature selection and classification | ? | 1 | 1. DE (?) 2. PSO (?) 3. GA (?) 4. DRF (?) | Classification accuracy | 10 | ? | ? | ? | 1. GA 2. DE 3. PSO 4. DRF | In the paper GA is mainly used, other algorithms are just mentioned as competitive methods, without any details. The precise role of metaheuristics used is not given |
Impact of environmental factors on COVID-19 cases | Haghshenas et al. (2020) | MLP-ANN | ? | 1 | 1. DE (Storn and Price, 1997) 2. PSO (Eberhart and Kennedy, 1995) | MSE | ? (probably 1 per case) | 450 (used for DE and PSO, various values up to 2.000 are tested) | 15 (various values from 5 to 40 are tested) | PSO: c1 = 1.49 c2 = 1.49 w = ? DE: no details | PSO marginally better than DE | DE and PSO were used for MLP-ANN training. No detailed discussion on historical applications is given. No detailed results of various population sizes and numbers of function calls that are said to be tested |
Mask production real-time scheduling | Wu et al. (2020) | Large size scheduling instances | ? | 1 | 1. SCEA (Zhao et al., 2015) 2. algebraic DE (Santucci et al., 2016) 3.TLBO (Shao et al., 2017) 4. BBO (Du et al., 2018) 5. discrete WWO (Zheng et al., 2019) | Specified | 50 | 100.000 | ? | ? | Averaged from various cases: 1. algebraic DE 2. WWO 3. SCEA 4. TLBO 5. BBO all metaheuristics better than other optimization methods for ANN scheduling | No sufficient details on metaheuristics used. Scheduling problem |
COVID-19 prevention programs | Zheng et al. (2020a) | Resources allocation for prevention programs in various communities and resident clustering | ? (large) | 1 (resident clustering) 1 (with constraints for resources allocation) | For clustering: 1. DE (Storn and Price, 1997) 2. GA (Muhlenbein and Schlierkamp-Voosen, 1993) 3. CLPSO (Liang et al., 2006) 4. hybrid BBO (Ma et al., 2014) 5. EBO (Zheng et al., 2014b) For resources allocation: 1. DE-NM (Luchi and Krohlingb, 2015) 2. WWO (Zheng, 2015) 3. GA (Koziel and Michalewicz, 1999) 4. BBO (Ma and Simon, 2011) 5. improved CS (Abdel-Basset et al., 2018) 6. integer-encoding GWO (Xing et al., 2019) | Specified | 30 (for both problems) | ? | ? | ? | For clustering: 1. EBO 2. DE 3. CLPSO 4. hybrid BBO 5. GA For resources allocation: 1. WWO 2. DE-NM 3. CS 4. GWO 5. BBO 6. GA | No sufficient details on compared metaheuristics and allowed number of function calls. A modified version of this study appeared as Zheng et al. (2020c) |
COVID-19 resource allocations and costs | Zheng et al. (2020b) | Balancing disease prevention and epidemic control | Main 2-objective problem: 10,000 ~ 40,000 Transformed 2-objective problem: 200 ~ 600 | 2 | Main 2-objective problem 1. MOEA/D (Zhang and Li, 2007) 2. NSGA-II (Deb et al., 2002) 3. CMOEA (Woldesenbet et al., 2009) 4. DECMOSA (Zamuda et al., 2009) 5. D2MOPSO (Al Moubayed et al., 2014) Transformed 2-objective problem: 1. NSGA-II (Deb et al., 2002) 2. MOEA/D 3. DEMOwSA (Zamuda et al., 2007) 4. MOPSO (Zheng et al., 2014a) | Specified | 30 | ? (discussed only for Tabu Search on single-objective sub-problems) | ? | ? | Comparisons for 14 hospitals are shown; results for transformed problems are much better; depending on the hospital, the best performance is obtained by MOPSO, DEMOwSA or MOEA/D | Discussed algorithms are used to find solutions of 2-objective problems; these are then divided into 1-objective low-dimensional sub-problems that are solved by Tabu search |
Goods management during COVID-19 pandemic | Zou et al. (2020) | Goods assignment for supermarkets to address residents needs during pandemics | 1000 supermarkets and 6758 communities | 2 | 1. PSO-DE (Zou et al., 2020) 2. ACO (Mouhoub and Wang, 2006) 3. SA (Peng et al., 1996) 4. GA (Ahuja et al., 2000) | Specified; minimization of infection risk and maximization of goods coverage for residents | 10 | Termination criteria related to pareto front, not the number of function calls | PSO-DE 30 (tested also 10 and 50) | c1 = c2 = 0.005 (also tested 0.1, 0.01, 0.001) w = 0.1 other parameters of PSO-DE hybrid also specified; unspecified for competitors | PSO-DE is considered as the best, as it significantly reduces infection risk, even though its goods coverage efficiency is marginally lower than in the case of other metaheuristics | The references to competing algorithms were not linked to the specific method in the paper; the details of control parameters of competing methods were not specified. However, the sensitivity study for the control parameters of the proposed PSO-DE hybrid is given |
Topic | Paper | Problems/models | Dimensionality | Number of objectives | Algorithms used | Objective function | Number of runs | Number of function calls | Population size | Other control parameters | Comparison of performance | Comments |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Epidemiology | Bertuzzo et al. (2020) | SEPIA | 7 | 1 | DREAMZS (Ter Braak and Vrugt, 2008) | ? | ? | ? | ? | Partly provided | No | |
Epidemiology | Davies et al. (2020) | Deterministic compartmental model | ? | 1 | DE-MCMC (Ter Braak, 2006) | Specified | ? | ? | ? | ? | No | |
Epidemiology | Gatto et al. (2020) | SEPIA + HQRD | 12 | 1 | DREAMZS (Vrugt et al., 2009) | Specified | ? | ? | ? | Partly provided | No | |
Epidemiology | Rahmandad et al. (2020) | Multi-country SEIR | 20 (?) | 1 | DREAMZS (Vrugt et al., 2009) | Specified | ? | 1.000.000 | ? | ? | No | |
Epidemiology | Wong et al. (2020) | Age of infection model | 22 (?) | 1 | Ensemble of MCMC-DE variants | Specified | ? | ? | ? | Partly provided | No | The specific version of the ensemble is undefined |
Topic | Paper | Problems/models | Dimensionality | Number of objectives | Algorithms used | Objective function | Number of runs | Number of function calls | Population size | Other control parameters | Comparison of performance | Comments |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Eoidemiology | Al-Hussein and Tahir (2020) | SEIR | 6 | 1 | PSO (?) | Scaled RMSE | ? | ? | ? | ? | No | |
Epidemiology | Al-qaness et al. (2020a) | ANFIS for infection prediction | ? | 1 | 1. MPA (Faramarzi et al. 2020) 2. PSO (?) 3. ABC (?) 4. GA (?) 5. FPA-SSA (Al-qaness et al. 2020b) 6. SCA (?) | MSE | 30 | 2.500 | 25 | PSO: c1 = 2 c2 = 2 wmax = 0.9 wmin = 0.2 also specified for other algorithms | For USA: 1. MPA 2. PSO 3. GA 4. ABC 5. SCA 6. FPA-SSA for Iran: 1. GA 2. MPA 3. PSO 4. FPA-SSA 5. ABC 6. SCA for Italy: 1. MPA 2. GA 3. PSO 4. SCA 5. FPA-SSA 6. ABC for S. Korea 1. MPA 2. GA 3. FPA-SSA 4. PSO 5. ABC 6. SCA | There are very big differences in root mean square errors between a group of better algorithms (GA, MPA and PSO) and a group of worse algorithms (FPASS, ABC and SCA). It is written that MSE is used as objective function, but results are given for RMSE and other measures |
Epidemiology | Al-qaness et al. (2020b) | ANFIS for infection prediction | ? | 1 | 1. FPA-SSA (Al-qaness et al. 2020b) 2. PSO (?) 3. GA (?) 4. ABC (?) 5. FPA (Yang 2012) | MSE | 30 | 2.500 | 25 | PSO: c1 = 2 c2 = 2 wmax = 0.9 wmin = 0.2 also specified for other algorithms | For China: 1. FPA-SSA 2. FPA 3. PSO 4. GA 5. ABC | It is written that MSE is used as objective function, but results are given for RMSE and other measures. Unclear why FPA-SSA perform so poor in Al-qaness et al. (2020a) study |
Epidemiology | Al-qaness et al. (2021a) | ANFIS for infection prediction | ? | 1 | 1. chaotic MPA (Al-qaness et al. 2021a) 2. PSO (?) 3. MPA (Faramarzi et al. 2020) | RMSE | ? | ? | ? | ? | 1. chaotic MPA 2. MPA 3. PSO | The model was used for Brazil and Russia, ranking of algorithms is the same in both cases |
Epidemiology | Ardabili et al. (2020) | 8 simple regression models | 1–4 | 1 | 1. PSO (?) 2. GA (Whitley et al. 1990) 3. GWO (Mirjalili et al. 2014) | MSE | 1 (?) | 500.000 (PSO and GWO) 150.000 (GA) | 500 (GA and PSO) 1000 (GWO) | ? | 1. GWO 2. PSO 3. GA | Metaheuristics are used even to fit linear regression model. Different numbers of function calls are used for different methods. Population sizes are very big |
Epidemiology | Bowman et al. (2020) | Regression coefficients in 1. Ensemble Model Output Statistics 2. Quantile Regression Averaging | ? | 1 | PSO (Kennedy and Eberhart 1995) | ? | ? | ? | ? | ? | No | The role of PSO is unclear |
Epidemiology | Cordelli et al. (2020) | SIRQ | 3 | 1 | PSO (Poli et al. 2007) | Scaled MSE | ? | ? | ? | ? | No | |
Epidemiology | Dutra et al. (2020) | SIR + unreported symptomatic | 3 | 1 | PSO (Kennedy and Eberhart 1995) | Specified | 50 | ? | 100 | c1 = 2.0 c2 = 2.0 w = 0.9 | No | PSO used to select initial solutions for MCMC-particle filter (Liu and West 2001) |
Epidemiology | Godio et al. (2020) | SEIR | 6 | 1 | Scaled RMSE | 50 | 30.000 | 150 | ? | No | In each run the algorithm converge to almost identical values of 2 SEIR parameters, but very different values for 4 others | |
Epidemiology | He et al. (2020a) | SEIR | 3 | 1 | PSO (Kennedy and Eberhart 1995) | ? | ? | ? | ? | ? | No | |
Epidemiology | He et al. (2020b) | SEIR | 2 | 1 | PSO (Kennedy and Eberhart 1995) | Unspecified “error” | 1 | 4.000 | 40 | c1 = 2 c2 = 2 w = after Peng et al. (2019) | No | |
Epidemiology | Hoffman (2020) | SEIR | 9 | 1 | PSO (Kennedy and Eberhart 1995) | ? | ? | ? | ? | ? | No | |
Epidemiology | Kergassner et al. (2020) | Memory-based spatial infection model | ? | 1 | PSO (Clerc and Kennedy 2002) | Specified | ? | ? | 300 | c1 = 1.496172 c2 = 1.496172 w = 0.72984 local topology | No | |
Epidemiology | Li et al. (2020a) | ? | ? | ? | ? | ? | ? | ? | ? | ? | ? | It is unclear what and how is optimized with PSO |
Epidemiology | Makade et al. (2020) | Linear regression (?) | ? | ? | ? | ? | ? | ? | ? | ? | ? | It seems that PSO is used to fit linear regression coefficients |
Epidemiology | Naraigh and Byrne (2020) | SEIR | 13 | 1 | 1. SA (?) 2. PSO (?) | Specified | ? | ? | ? | ? | “Results are the same” | No reference to SA or PSO |
Epidemiology | Ngie et al. (2020) | Unclear; probably parameter tuning or features selection | ? | ? | PSO (Kennedy and Eberhart 1995) | ? | ? | ? | ? | ? | ? | |
Epidemiology | Niazi et al. (2020) | SNDUR | 6 | 1 | PSO (Kennedy and Eberhart 1995) | Specified | ? | ? | ? | ? | No | The name of the model has been slightly modified—N is used instead of I, as I already have a different meaning in SIR models discussed in this Table |
Epidemiology | Oliveira et al. (2021) | SEIIHURD model | 6 | 1 | PSO (Miranda 2018) | Unclear | ? | 300.000 | 300 | c1 = 0.1 c2 = 0.3 w = 0.9 | No | |
Epidemiology | Paggi (2020a) | SIRAUL | 5 or 7 | 1 | PSO (Kennedy and Eberhart 1995) | Variant of absolute error | ? | 100.000 | 100 | c1 = 0.5 c2 = 0.5 wmax = 0.9 wmin = 0.5 | No | Dimensionality vary depending on the specific case |
Epidemiology | Paggi (2020b) | SIRAD | 5 | 1 | PSO (Kennedy and Eberhart 1995) | Variant of absolute error | ? | 1.000.000 | 1000 | c1 = 0.5 c2 = 0.5 wmax = 0.9 wmin = 0.5 | No | |
Epidemiology | Sazvar et al. (2020) | MLP ANN | ? | 1 | 1. PSO (Kennedy and Eberhart 1995) 2. GA (Muhlenbein and Mahnig 1999) 3. ICA (Atashpaz-Gargari and Lucas 2007) | MAPE | 1 best out of 20 | ? | ? | ? | 1. GA 2. ICA 3. PSO | ICA and PSO perform very poorly |
Epidemiology | Unlu et al. (2020) | SEIR | 9 | 1 | PSO (Kennedy and Eberhart 1995) | 1-R2 | 1 | ? | ? | ? | No | |
Epidemiology | Van Tinh (2020a) | Fuzzy logic model | ? | 1 | PSO (Kennedy and Eberhart 1995) | MSE | ? | 7.500 | 50 | c1 = 2 c2 = 2 wmax = 0.9 wmin = 0.4 | No | Almost the same study as Van Tinh (2020b) |
Epidemiology | Van Tinh (2020b) | Fuzzy logic model | ? | 1 | PSO (Kennedy and Eberhart 1995) | MSE | ? | 3.000 | 30 | c1 = 2 c2 = 2 wmax = 0.9 wmin = 0.4 | No | Almost the same study as Van Tinh (2020a) |
Epidemiology | Wang et al. (2020a) | SIR | 2 | 1 | PSO (Kennedy and Eberhart 1995) | ? | ? | ? | ? | ? | No | |
Epidemiology | Zhan et al. (2020) | SEIRM | 5003 (?) | 1 | 1. PSO (?) 2. GA (?) 3. Pattern Search (?) 4. pseudoevolutionary SA (Zhan et al. 2020) | Specified | ? | ? | ? | ? | “These methods cannot provide a satisfied result or cannot even converge in an acceptable computation time (such as one day), while the proposed method can converge to the global optima in two hours” | The paper criticizes the performance of metaheuristics for the particular problem |
Epidemiology | Zreiq et al. (2020) | SIR, generalized growth model, classical logistic growth model, generalized logistic model, generalized Richards model | 2–4 | 1 | MSE | 1 (?) | 10.000 | 50 | c1 = 2 c2 = 2 wmax = 0.9 wmin = 0.4 | No | Authors refer to Boubaker (2017), but from the text one may infer that they use classical PSO with inertia weight | |
Epidemiology | Too and Mirjalili (2020) | Selecting features and predicting the fate of a patient | 15 features | 1 | 1. binary PSO (Kennedy and Eberhart 1997) 2. HLBDA (Too and Mirjalili 2020) 3. binary DRF (Mirjalili 2016a) 4. binary MVO (Al-Madi et al. 2019) | Specified | 20 | 1.000 | 10 | c1 = 2 c2 = 2 wmax = 0.9 wmin = 0.4 | Very similar accuracy is obtained by all methods, results are only given graphically and it is hard to see any differences; authors claim that HLBDA performed best | The paper aimed mainly at introduction of new metaheuristic (HLBDA) to find an optimal subset of features for classification problems. Tests with COVID-19 disease are added at the end of the paper, after 21 other datasets, and are not discussed in details |
Epidemiology and impact of the government interventions on spread of SARS-COV-2 in Brazil | Jorge et al. (2020) | Selected parameters of SEIR model | ? | 1 | PSO (Miranda 2018) | ? | ? | 75.000 | 150 | c1 = 0.1 c2 = 0.3 w = 0.9 | No | |
Fast Infection Detection | Asghari et al. (2020) | Minimization of bending loss of waveguide | 3 | ? | PSO (?) | ? | ? | ? | ? | ? | No | In the paper it is just mentioned that for calibration PSO was used |
Virus infection detection | Bhonde et al. (2020) | Random forest algorithm for feature detection | ? | 1 | PSO (?) | ? (clearly stated for final model only) | ? | ? | ? | c1 = 0.5 c2 = 0.5 w = 0.9 seems to use local topology | No | Role, variant and usage of PSO unclear |
Blood test based diagnostics | de Freitas Barbosa et al. (2021) | Feature selection for blood tests | ? | 1 | 1. PSO-fs (Wang et al. 2007) 2. Evolutionary search (Kim et al. 2000) | Specified | ? | 10.000 | 20 | ? | Equal performance | |
x-ray-based diagnostics | Canayaz (2020) | Feature selection | ? | 1 | 1. binary PSO (Too et al. 2019) 2. binary GWO (Too et al. 2018) | Specified | ? | 2.000 | 20 (both algorithms) | binary PSO: c1 = 2 c2 = 2 vmax = 0.9 wmax = 0.9 wmin = 0.4 binary GWO unspecified | Binary PSO marginally better than binary GWO | |
Computed tomography-based diagnostics | El-Kenawy et al. (2020) | Features selection and classification for CNN (2 distinct problems) | ? | 1 | Feature selection: 1. SFS–Guided WOA (SFS-GWOA, El Kenawy et al. 2020) 2. WOA (Mirjalili and Lewis 2016) 3. GWO (Al-Tashi et al. 2019) 4. GA (Kabir et al. 2011) 5. two-step PSO (Bello et al. 2007) 6. PSO-GWO (Senel et al. 2019) 7. GA-GWO hybrid (?) 8. BA (Karakonstantis and Vlachos 2020) 12. FA (Fister et al. 2012) Classification: 1. PSO-GWOA (EL Kenawy et al. 2020) 2. PSO (?) 3. GWO (?) 4. GA (?) 5. WOA (?) | Specified | 20 | Feature selection: only given for SFS-GWOA = 800 classification: only given for PSO-GWO = 400 | Feature selection: only given for SFS-GWOA = 10 classification: 20 for all metaheuristics | Both problems: two-step PSO: c1 = 2 c2 = 2 wmax = 0.9 wmin = 0.6 also specified for other metaheuristics | Feature selection: 1. SFS-GWOA 2. GWO 3. BBO 4. MVO 5. GA 6. GA-GWO 7. FA 8. WOA 9. two –step PSO 10. PSO-GWO 11. SBO 12. BO classification: 1. PSO-GWOA 2. PSO 3. GWO 4. GA 5. WOA | The number of function calls for non SFS-GWOA algorithms is unclear It is unclear whether the metaheuristics used for classification are the same as for feature selection or not |
x-ray chest image based classification | Goel et al. (2020) | CNN hyperparameters optimization | 4 | 1 | 1. PSO (Kennedy and Eberhart 1995) 2. GWO (Mirjalili et al. 2014) 3. GA (Holland 1992) 4. Pattern Search (PS, Hooke and Jervis 1961) 5. Simmulated Annealing (SA, van Laarhoven and Aarts 1987) 6. WSO (Mirjalili and Levis 2016) | Specified | ? | 900 (discussed for GWO only) | 30 (discussed for GWO only) | ? | 1. GWO 2. WOA 3. GA 4. SA 5. PSO 6. PS | No comparison rules are given, number of function calls and population size is specified for GWO only |
x-ray chest image based classification | Mohammed et al. (2020) | Threshold in x-ray segmentation | 1 | 1 | PSO (Eberhart and Kennedy 1995) | Specified | ? | ? | ? | ? | No | |
x-ray chest image based classification | Sahlol et al. (2020) | CNN-based feature selection | 459 and 462 features | 1 | 1. Fractional-order MPA (FO-MPA, Sahlol et al. 2020) 2. BPSO (?) 3. WOA (Mirjalili and Lewis 2016) 4. HGS (Hashim et al. 2019) 5. SCA (?) 6. SMA (Li et al. 2020b) 7. GWO (Mirjalili et al. 2014) 8. HHO (Heidari et al. 2019) 9. GA (?) 10. MPA (Faramarzi et al. 2020) | Specified | 25 | 300 | 15 | No details on Control parameters | According to Table 4 (performance): Dataset 1: 1. FO-MPA 2. SCA 3. GA 4. BPSO 5. WOA 5. MPA 7. GWO 8. SMA 9. HHO 10. HGS Dataset 2: 1. FO-MPA 2. BPSO 3. GA 4. MPA 5. GWO 6. SCA 7. WOA 8. SMA 8. HGS 10. HHO however, these results seems to disagree with Table 3 (results of the feature selection phase based on fitness function) and discussion in the manuscript; the reason is unclear | No references to GA, SCA and BPSO |
x-ray image based classification | Asghar et al. (2020) | CNN-based feature selection | 1000 features | 1 | PSO-fs (Indu et al. 2018) | Specified | ? | ? | ? | ? | No | The version of PSO proposed for features selection by Indu et al. (2018) was used |
Computer tomography based-diagnostics | Abd Elaziz et al. (2020b) | Multilevel thresholding of computer tomography images | Threshold levels 6–19 | 1 | 1. MPA-MFA (Abd Elaziz et al. (2020b) 2. PSO (Kennedy and Eberhart 1995) 3. MPA (Faramarzi et al. 2020) 4. HHO (Heidari et al. 2019) 5. CS (Yang and Deb 2009) 6. GWO (Mirjalili et al. 2014) 7. GO (Mirjalili et al. 2018) 8. SSO (Zhao et al. 2019) 9. MFA (Mirjalili 2015) | Specified | 30 | 2.000 | 20 (all algorithms) | PSO: c1 = 2 c2 = 2 wmax = 0.9 wmin = 0.2 | 2 experiments with 2 ways of comparison: overall: 1. MPA-MFO 2. HHO 3. CS 4. SSO 5. PSO 6. MPA 7. GWO 8. MFA 9. GO | |
COVID-19 genome sequence | Issa and Abd Elaziz (2020) | Finding the longest common consecutive subsequence via Fragmented Local Aligner Technique | ? | 1 | 1. IMA-PSO (Issa and Abd Elaziz 2020) 2. ASCA-PSO (Issa et al. 2018) 3. IMA (Javidy et al. 2015) 4. SCA (Mirjalili, 2016b) 5. greedy IMA (GIMA, Yang et al. 2018) 6. diversity enhanced IMA (DIMA, Pan et al. 2019) | Specified | 20 | Only number of iterations is given (larger for IMO-PSO than other algorithms) | From 40 to 700, depending on the consecutive subsequence case | IMO-PSO and ASCA-PSO: c1 = 0.5 c2 = 0.5 w = 0.2; ASCA-PSO: a = 2; also specified for SCA, but not for others | 1. IMA-PSO 2. ASCA-PSO 3. GIMA 4. SCA 5. DIMA 6. IMA | It seems that IMA-PSO is allowed to perform more function calls than other methods, but it is not clear from the paper |
Remote care for COVID-19 patients by means of moving robotic arms with PID controller | Therib et al. (2020) | PID controller optimization | ? | ? | PSO (Kennedy and Eberhart 1995) | ? | ? | ? | ? | ? | No | No details on the role of PSO in the system is provided, apart from a general flowchart |
Power consumption under COVID-19 pandemic in China | Huang et al. (2021) | Calibration of specific parameters used by Rolling IMSGM(1,1) model | 2 (?) | 1 | PSO (Kennedy and Eberhart 1995) | Specified | ? | ? | ? | ? | No | PSO and ACO are applied for calibration of different kind of parameters during Rolling IMSGM(1,1) model implementation. Although the general role of PSO is specified, the details are unclear |
Daily electricity demand during COVID-19 pandemic | Lu et al. (2021) | Support Vector Machine calibration | ? | 2 | 1. PSO (Kennedy and Eberhart 1995) 2. multi-objective GWO (Mirjalili et al. 2016b) 3. NSGA-II (Deb et al. 2002) 4. WOA (Mirjalili and Lewis 2016) | Specified | ? | ? | ? | ? | 1. multi-objective GWO 2. WOA 3. PSO 4. NSGA-II | It is not specified how the basic PSO or WOA were implemented for 2-objective problem |
User opinion on mobile applications developed for monitoring the spread of COVID-19 among population | Mustopa et al. (2020) | Support Vector Machine calibration for classification of opinions | 1364 opinions from users for classification | 1 | PSO (?) | ? | ? | ? | ? | ? | No | The exact role of PSO is unspecified |
Internet of Things for students distancing | Alrashidi (2020) | Optimizing the student seats allocation in a classroom | 10–250 students in 2-dimensional room | 1 | 1. PSO (?) 2. ACO (?) 3. GA (?) | Distance | 20 | ? | ? | PSO: c1 = 0.4 c2 = 0.6 w = 0.8 (?) specified also for ACO, but not for GA | 10–20 students: 1. ACO 2. PSO 3. GA 40–250 students: 1. PSO 2. ACO 3. GA | There is an error in inertia weight naming, but it seems that it is set to 0.8 |
Big Data Application for modelling COVID-19 medical compound | Cholissodin et al. (2020) | Unclear | ? | ? | PSO (?) | ? | ? | ? | ? | ? | No | The role of PSO and the variant used are unclear |
Mobility of US population during pandemics | Kang et al. (2020) | Minimizing difference between estimated and direct mobile phone-based flow of people | 2 | 1 | PSO (Kennedy and Eberhart 1995) | RMSE | ? | ? | ? | ? | No | |
Impact of lockdown on air quality | Al-qaness et al. (2021b) | ANFIS for air quality estimation: fine particulate matter (PM2.5), carbon dioxine (CO2), sulfur dioxine (SO2) and nitrogen dioxine (NO2) | 14 (?) | 1 | 1. PSO (Eberhart and Kennedy 1995) 2. SMA (Li et al. 2020b) 3. PSOSMA (Al-qaness et al. 2021b) 4. GA (?) 5. SCA (?) 6. SSA (?) | MSE | 30 | 3.000 | 30 | c1 = 2 c2 = 2 wmax = 0.9 wmin = 0.2 specified also for other algorithms | PM2.5: 1. PSOSMA 2. SMA 3. PSO 4. GA 5. SSA 7. SCA CO2: 1. PSOSMA 2. PSO 3. SMA 4. GA 5. SSA 6. SCA SO2: 1. PSOSMA 2. SMA 3. GA 4. PSO 5. SSA 6. SCA NO2: 1. PSOSMA 2. PSO 3. SMA 4. GA 5. SSA 6. SCA | The number of ANFIS parameters is not specified. It was estimated based on the figure provided in the paper, but it is unclear if the number of nodes used is the same as given in the figure. The differences in the final comparison between PSOSMA, PSO, SMA and to some extent GA are small, SSA and SCA perform much poorer |
Forecasting currency exchange during COVID-19 pandemics | Hakimah and Kurniawan (2020) | Calibration of double exponential smoothing damped trend model | 3 | 1 | 1. PSO (Kenedy and Eberhart 1995) 2. GA (?) | Mean absolute percentage error | 10 | ? | ? | ? | 1. PSO 2. GA (marginal difference) | The reference to PSO variant is unclear, but from the text one may infer that the original PSO without inertia weight is used. The variant of GA is unspecified |
Relationship between words used in COVID-19 research | Fister et al. (2020a) | Association rule text mining in COVID-19 abstracts | ? | 1 | PSO-ARTM (Fister et al. 2020b) | Specified | 5 (Fister et al. 2020b) | 10.000 (Fister et al. 2020b) | 200 | c1 = 2 c2 = 2 w = 0.7 | No |
Topic | Paper | Problems/models | Dimensionality | Number of objectives | Algorithms used | Objective function | Number of runs | Number of function calls | Population size | Other control parameters | Comparison of performance | Comments |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Epidemiology | Pinter et al. (2020) | MLP training to predict the number of infected cases and fatalities | 60 84 108 (?) | 1 | ICA (Atashpaz-Gargari and Lucas 2007) | RMSE (?) (probably, three different criteria are mentioned) | ? | Case 1: 12.000 case 2: 13.750 (?) | Case 1: 300 case 2: 250 | ? | No | In the paper neither the dimensionality nor the number of function calls is explicitly given. Dimensionality is estimated according to the number of MLP nodes; number of function calls is estimated according to data given in the paper |
Epidemiology | Yousefpour et al. (2020) | SEIR with government policies | 5 (?) | 2 | GA (?) | Specified | ? | 50.000 | 70 | Specified | No | Various control parameters are given, but the algorithm is not specified. Dimensionality is not clearly given in the paper |
Epidemiology and control | Hadi and Ali (2021) | Controller with use of SEIR | 5 (?) | 1 | Most Valuable Player Algorithm (Bouchekara 2017) | Specified | 1 | 400 (?) | 10 (?) | Specified | No | The details of the procedure applied are not clearly explained |
Patient diagnostics | Shaban et al. (2020) | Features selection from computer tommography images for classifiers | ? | 1 | GA (Khare and Burse 2016) | Accuracy | ? | 8 (?) | 4 (?) | Specified | No | |
Patient diagnostics and treatment prediction | Elghamrawy and Hassanien (2020) | Features selection for patient classification within AIMDP model | ? | 1 | WOA (Mirjalili and Lewis 2016) | ? | ? | ? | ? | ? | Unclear | No details, but it is shown that the AIMDP model without WOA-based features selection perform much poorer |
x-ray image diagnostics | Abdel-Basset et al. (2020b) | x-ray image segmentation | Threshold levels 10–100 | 1 | 1. improved MPA (Abdel-Basset et al. 2020b) 2. MPA (Faramarzi et al. 2020) 3. SCA (Mirjalili 2016b) 4. WOA (Abd Elaziz et al. 2017) 5. EOA (Abdel-Basset et al. 2020a) 6. HHA (Bao et al. 2019) 7. SSA (Wang et al. 2020b) | Specified | 20 | 3.000 | 20 | Unspecified | Average performance from multiple competitions: 1. improved MPA 2. MPA 3. EOA 4. WOA 5. HHA 6. SSA 7. SCA | |
x-ray image diagnostics | Altan and Karasu (2020) | Feature matrix coefficients for deep learning | ? | 1 | Chaotic SSA (Sayed et al. 2018) | Specified | ? | ? | ? | ? | No | |
x–ray image diagnostics | Ezzat et al. (2020) | Hybrid CNN hyperparameters | 3 | 1 | GSA (Rashedi et al. 2009) | Specified | ? | 450 | 30 | ? | No | |
x-ray image diagnostics | Medjahed and Ouali (2020) | Feature selection for patient classification by different models | 844 features | 1 | Specified | ? | 300 | 60 | Specified | No | ||
x-ray image diagnostics | Mishra et al. (2020) | CNN weights optimization | ? | 1 | WCA (Qiao et al. 2018) | Specified | ? | ? | ? | ? | No | |
x-ray image classification | Yousri et al. (2021) | Feature selection from patient images | ? | 1 | 1. CS (Yang and Deb 2009) 2. fractional-CS (Yousri and Mirjalili 2020) 3. fractional-CSML (Yosuri et al. 2021) 4. fractional-CSP ( Yosuri et al. 2021) 5. fractional-CSC ( Yosuri et al. 2021) 6. fractional-SCW ( Yosuri et al. 2021) 7. HHA (Heidari et al. 2019) 8. HGS (Hashim et al. 2019) 9. WOA (Mirjalili and Lewis 2016) 10. SSA (Ibrahim et al. 2019) 11. GWO (Ibrahim et al. 2018) 12. SGA (?) | Specified fitness and accuracy | ? | 750 | 15 | ? | Specific ranking of algorithms depends on the criteria used (12 different were presented), but for each case either fractional-CSML, fractional-CSW or fractional-CSC performed best | CSML, CSP, CSC and CSW refer to Cuckoo search that, instead of levy flight, uses Mittag–Leffler (CSML), Pareto (CSP), Cauchy (CSC) or Weibull (CSW) distributions. All these variants were defined in the paper by Yousri et al. (2021). Two different COVID-19 datasets and the best, mean and the worst fitness, as well as the best, mean and the worst accuracy were used for comparison of algorithms (together 12 criteria) |
x-ray image classification | Babukarthik et al. (2020) | DCNN architecture | ? | 1 | GA (Babukarthik et al. 2020) | Specified | ? | ? | ? | Discussed | No | Architectures of DCNN are evolved with genetic operators to find the best classifier |
x-ray image diagnostics | Vrbancic et al. (2020) | DCNN hyperparameter’s optimization | 4 | 1 | GWO for tuning (Vrbancic et al. 2019) | Specified | ? | 2.500 | 50 | ? | No | |
Computer tomography based diagnostics | Satapathy et al. (2020) | Thresholding in computer tomography scans | 3-level thresholding | 1 | CS (Yang and Deb 2009) | Specified | ? | 140.000 | 40 | ? | No | |
Computer tomography based diagnostics | Yao and Han (2020) | MLP network calibration | ? | 1 | BBO (Ma et al. 2012) | ? | ? | ? | ? | ? | No | Unclear BBO application |
Drug development | Cheng et al. (2020b) | Genetic operations on drug molecules | ? | 1 with penalty function | Graph-based GA (?) | Specified | ? | ? | ? | ? | No | Although not specified clearly in the paper, it seems that GA used is based on Pawar and Bichkar (2015) |
Contactless vehicle routing problem during COVID-19 pandemic for food distribution | Chen et al. (2020) | Contactless joint distribution model for food distribution in Wuhan, China | ? | 1 | 1. ABC with Tabu search operator and mechanism of progressive construction solution (Chen et al. 2020) 2. enhanced ABC (Szeto et al. 2011) 3. Tabu Search (Glover 1986) | Specified | 20 | ? | ? | Discussed | 1. ABC with Tabu search 2. Tabu Search 3. enhanced ABC | |
Vehicle routing problem during COVID-19 pandemic | Liu et al. (2020) | Model of medical waste transport routes | ? | 1 | Immune ACO with Tabu search (Liu et al. 2020) | Specified | ? | ? | ? | ? | No | |
Government actions during COVID-19 pandemic | Miralles-Pechuan et al. (2020) | Model daily actions performed by government within SEIR approach | 4 possible actions during 200 days = 4200 combinations | 1 | GA (Whitley 1994) | Specified | ? | 100.000 | 100 | Specified | Reinforcement learning is a better way to determine government actions during pandemic than GA |
2.1 DE and PSO for COVID-19 epidemiological models
2.2 DE and PSO for image-based COVID-19 diagnostics
2.3 Other applications of DE and PSO against COVID-19
2.4 Applications of other metaheuristics against COVID-19
3 Methodological aspects of differential evolution and particle swarm optimization applications
3.1 DE and PSO variants used against COVID-19
3.2 Number of allowed function calls
3.3 Number of repetitions
3.4 Population size
3.5 Other DE and PSO control parameters
3.6 Comparison of performance
Paper | Number of function calls | Population size |
---|---|---|
Lobato et al. (2020) | 6.250 | 25 |
Saif et al. (2021) | 5.000 | 25 |
Abdel_Basset et al. (2020c) | 4.500 | 30 |
Abd Elaziz et al. (2020a) | ? | ? |
Punitha et al. (2020) | ? | ? |
Haghshenas et al. (2020) | ? | ? |
Wu et al. (2020) | 100.000 | ? |
Zheng et al. (2020a) | ? | ? |
Zheng et al. (2020b | ? | ? |
Zou et al. (2020) | Other | 30 (PSO-DE) unclear for others |
Al-quaness et al. (2020a ) | 2.500 | 25 |
Al-quaness et al. (2020b) | 2.500 | 25 |
Al-quaness et al. (2021) | ? | ? |
Sazvar et al. (2020) | ? | ? |
Too and Mirjalili (2020) | 1.000 | 10 |
Canayaz (2020) | 2.000 | 20 |
El-Kenawy et al. (2020) | 400–800 | 10–20 |
Goel et al. (2020) | 900 | 30 |
Sahlol et al. (2020) | 300 | 15 |
Abd Elaziz et al. (2020b) | 2.000 | 20 |
Issa and Abd-Elaziz (2020) | ? | 40–700 |
Alrashidi et al. (2020) | ? | ? |
Abdel-Basset et al. (2020b) | 3.000 | 20 |
Chen et al. (2020) | ? | ? |
Lu et al. (2021) | ? | ? |
Al-qaness et al. (2021b) | 3.000 | 30 |
Yousri et al. (2021) | 750 | 15 |