@article{van2023groundwater, title={Groundwater level modeling using Augmented Artificial Ecosystem Optimization}, author={Van Thieu, Nguyen and Barma, Surajit Deb and Van Lam, To and Kisi, Ozgur and Mahesha, Amai}, journal={Journal of Hydrology}, volume={617}, pages={129034}, year={2023}, publisher={Elsevier}, doi={https://doi.org/10.1016/j.jhydrol.2022.129034} }

@article{ahmed2021comprehensive, title={A comprehensive comparison of recent developed meta-heuristic algorithms for streamflow time series forecasting problem}, author={Ahmed, Ali Najah and Van Lam, To and Hung, Nguyen Duy and Van Thieu, Nguyen and Kisi, Ozgur and El-Shafie, Ahmed}, journal={Applied Soft Computing}, volume={105}, pages={107282}, year={2021}, publisher={Elsevier}, doi={10.1016/j.asoc.2021.107282} }

# ⚙️ Usage

<details><summary><h2>🛠️ Installation</h2></summary>
Install the stable (latest) version from PyPI release:

* Install the alpha/beta version from PyPi

* Install the pre-release version directly from the source code:

* In case, you want to install the development version from Github:

After installation, check the version to ensure successful installation:

>> import mealpy

>> mealpy.__version__

>> print(mealpy.get_all_optimizers())

>> model = mealpy.get_optimizer_by_name("OriginalWOA")(epoch=100, pop_size=50)

</details>
💬 Decision Variables
Before we dive into some examples, let's briefly consider the type of problem you're aiming to solve with MEALPY. 
Understanding your specific problem and its desired solution can help you select the most appropriate approach.
To assist you in choosing the right tools, refer to the table below. It outlines different types of decision variables available in MEALPY, 
along with their syntax and common problem applications. This will guide you in defining your search space effectively.
<div align="center">
| Class             | Syntax                                                                                                          | Problem Types               |
|-------------------|-----------------------------------------------------------------------------------------------------------------|-----------------------------|
| FloatVar          | FloatVar(lb=(-10., )7, ub=(10., )7, name="delta")                                                           | Continuous Problem          |
| IntegerVar        | IntegerVar(lb=(-10., )7, ub=(10., )7, name="delta")                                                         | LP, IP, NLP, QP, MIP        |
| StringVar         | StringVar(valid_sets=(("auto", "backward", "forward"), ("leaf", "branch", "root")), name="delta")             | ML, AI-optimize             |
| BinaryVar         | BinaryVar(n_vars=11, name="delta")                                                                            | Networks                    |
| BoolVar           | BoolVar(n_vars=11, name="delta")                                                                              | ML, AI-optimize             |
| PermutationVar    | PermutationVar(valid_set=(-10, -4, 10, 6, -2), name="delta")                                                  | Combinatorial Optimization  |
| CategoricalVar    | CategoricalVar(valid_sets=(("auto", 2, 3, "backward", True), (0, "tournament", "round-robin")), name="delta") | MIP,  MILP                  |
| SequenceVar       | SequenceVar(valid_sets=((1, ), {2, 3}, [3, 5, 1]), return_type=list, name='delta')                            | Hyper-parameter tuning      |
| TransferBoolVar   | TransferBoolVar(n_vars=11, name="delta", tf_func="sstf_02")                                                   | ML, AI-optimize, Feature    |
| TransferBinaryVar | TransferBinaryVar(n_vars=11, name="delta", tf_func="vstf_04")                                                 | Networks, Feature Selection |

</div>
📚 Optimizer Classification
!MEALPY3-0-0
To explore the complete, up-to-date registry of supported optimizers and their categorical breakdown, 
please check out our Official Classification Table 
or consult the foundational research paper.
❌ Warning: Algorithms Suspected of Plagiarism
During our implementation and classification of metaheuristic optimization algorithms, we identified a set of methods that raise 
serious concerns regarding scientific integrity and originality. These algorithms are typically published under different names, 
but they appear to share:
The same core mathematical models, equations, and update rules.
Only superficial changes in naming, metaphors, or biological analogies.
Publications authored by the same or overlapping research groups.
Heavy criticism on public academic forums such as PubPeer, where many of these papers are flagged for self-plagiarism, redundant publication, or lack of novelty.
Some of these papers may be withdrawn or retracted in the future, as investigations unfold.
For these reasons, we strongly advise the exclusion of the following algorithms from scientific benchmarking, 
comparative studies, or any applications unless their originality is transparently validated.
**I have personally implemented these algorithms, which is why I can confidently say that they are nearly identical 
and likely cases of plagiarism. For this reason, I will no longer spend time coding such algorithms in the future. 
This warning is intended to help others avoid using or relying on these methods in their work.**
| Group | Name | Module | Class | Year | Paras |
| --- | --- | --- | --- | --- |-------| 
| Swarm | Coati Optimization Algorithm | CoatiOA | OriginalCoatiOA | 2023 | 2     |
| Swarm | Fennec For Optimization | FFO | OriginalFFO | 2022 | 2     |
| Swarm | Northern Goshawk Optimization | NGO | OriginalNGO | 2021 | 2     |
| Swarm | Osprey Optimization Algorithm | OOA | OriginalOOA | 2023 | 2     |
| Swarm | Pelican Optimization Algorithm | POA | OriginalPOA | 2023 | 2     |
| Swarm | Serval Optimization Algorithm | ServalOA | OriginalServalOA | 2022 | 2     |
| Swarm | Siberian Tiger Optimization | STO | OriginalSTO | 2022 | 2     |
| Swarm | Tasmanian Devil Optimization | TDO | OriginalTDO | 2022 | 2     |
| Swarm | Walrus Optimization Algorithm | WaOA | OriginalWaOA | 2022 | 2     |
| Swarm | Zebra Optimization Algorithm | ZOA | OriginalZOA | 2022 | 2     |
| Human | Teamwork Optimization Algorithm | TOA | OriginalTOA | 2021 | 2     |
⚠️ Ethical Reminder
Researchers and students are urged to exercise caution when referencing or applying the algorithms listed above. 
Using unoriginal or unethical work can compromise the scientific credibility of any downstream research and introduce misleading experimental results.
> 🔗 Check PubPeer1 and PubPeer2 to 
> find ongoing discussions and critiques from the academic community.

<details><summary><h3>💻 Define All Optimizers</h3></summary>

from mealpy import (StringVar, FloatVar, BoolVar, PermutationVar, CategoricalVar, IntegerVar, BinaryVar, TransferBinaryVar, TransferBoolVar) from mealpy import Tuner, Multitask, Problem, Optimizer, Termination, ParameterGrid from mealpy import get_all_optimizers, get_optimizer_by_name

from mealpy import BBO, PSO, GA, ALO, AO, ARO, AVOA, BA, BBOA, BMO, EOA, IWO from mealpy import GJO, FOX, FOA, FFO, FFA, FA, ESOA, EHO, DO, DMOA, CSO, CSA, CoatiOA, COA, BSA from mealpy import HCO, ICA, LCO, WarSO, TOA, TLO, SSDO, SPBO, SARO, QSA, ArchOA, ASO, CDO, EFO, EO, EVO, FLA from mealpy import HGSO, MVO, NRO, RIME, SA, WDO, TWO, ABC, ACOR, AGTO, BeesA, BES, BFO, ZOA, WOA, WaOA, TSO from mealpy import PFA, OOA, NGO, NMRA, MSA, MRFO, MPA, MGO, MFO, JA, HHO, HGS, HBA, GWO, GTO, GOA from mealpy import SBO, SMA, SOA, SOS, TPO, TSA, VCS, WHO, AOA, CEM, CGO, CircleSA, GBO, HC, INFO, PSS, RUN, SCA from mealpy import SHIO, TS, HS, AEO, GCO, WCA, CRO, DE, EP, ES, FPA, MA, SHADE, BRO, BSO, CA, CHIO, FBIO, GSKA, HBO from mealpy import TDO, STO, SSpiderO, SSpiderA, SSO, SSA, SRSR, SLO, SHO, SFO, ServalOA, SeaHO, SCSO, POA

Newly added module in version 3.0.3

if __name__ == "__main__": model = BBO.OriginalBBO(epoch=10, pop_size=30, p_m=0.01, n_elites=2) model = PSO.OriginalPSO(epoch=100, pop_size=50, c1=2.05, c2=20.5, w=0.4) model = PSO.LDW_PSO(epoch=100, pop_size=50, c1=2.05, c2=20.5, w_min=0.4, w_max=0.9) model = PSO.AIW_PSO(epoch=100, pop_size=50, c1=2.05, c2=20.5, alpha=0.4) model = PSO.P_PSO(epoch=100, pop_size=50) model = PSO.HPSO_TVAC(epoch=100, pop_size=50, ci=0.5, cf=0.1) model = PSO.C_PSO(epoch=100, pop_size=50, c1=2.05, c2=2.05, w_min=0.4, w_max=0.9) model = PSO.CL_PSO(epoch=100, pop_size=50, c_local=1.2, w_min=0.4, w_max=0.9, max_flag=7) model = GA.BaseGA(epoch=100, pop_size=50, pc=0.9, pm=0.05, selection="tournament", k_way=0.4, crossover="multi_points", mutation="swap") model = GA.SingleGA(epoch=100, pop_size=50, pc=0.9, pm=0.8, selection="tournament", k_way=0.4, crossover="multi_points", mutation="swap") model = GA.MultiGA(epoch=100, pop_size=50, pc=0.9, pm=0.8, selection="tournament", k_way=0.4, crossover="multi_points", mutation="swap") model = GA.EliteSingleGA(epoch=100, pop_size=50, pc=0.95, pm=0.8, selection="roulette", crossover="uniform", mutation="swap", k_way=0.2, elite_best=0.1, elite_worst=0.3, strategy=0) model = GA.EliteMultiGA(epoch=100, pop_size=50, pc=0.95, pm=0.8, selection="roulette", crossover="uniform", mutation="swap", k_way=0.2, elite_best=0.1, elite_worst=0.3, strategy=0) model = ABC.OriginalABC(epoch=1000, pop_size=50, n_limits=50) model = ACOR.OriginalACOR(epoch=1000, pop_size=50, sample_count=25, intent_factor=0.5, zeta=1.0) model = AGTO.OriginalAGTO(epoch=1000, pop_size=50, p1=0.03, p2=0.8, beta=3.0) model = AGTO.MGTO(epoch=1000, pop_size=50, pp=0.03) model = ALO.OriginalALO(epoch=100, pop_size=50) model = ALO.DevALO(epoch=100, pop_size=50) model = AO.OriginalAO(epoch=100, pop_size=50) model = ARO.OriginalARO(epoch=100, pop_size=50) model = ARO.LARO(epoch=100, pop_size=50) model = ARO.IARO(epoch=100, pop_size=50) model = AVOA.OriginalAVOA(epoch=100, pop_size=50, p1=0.6, p2=0.4, p3=0.6, alpha=0.8, gama=2.5) model = BA.OriginalBA(epoch=100, pop_size=50, loudness=0.8, pulse_rate=0.95, pf_min=0.1, pf_max=10.0) model = BA.AdaptiveBA(epoch=100, pop_size=50, loudness_min=1.0, loudness_max=2.0, pr_min=-2.5, pr_max=0.85, pf_min=0.1, pf_max=10.) model = BA.DevBA(epoch=100, pop_size=50, pulse_rate=0.95, pf_min=0., pf_max=10.) model = BBOA.OriginalBBOA(epoch=100, pop_size=50) model = BMO.OriginalBMO(epoch=100, pop_size=50, pl=4) model = EOA.OriginalEOA(epoch=100, pop_size=50, p_c=0.9, p_m=0.01, n_best=2, alpha=0.98, beta=0.9, gama=0.9) model = IWO.OriginalIWO(epoch=100, pop_size=50, seed_min=3, seed_max=9, exponent=3, sigma_start=0.6, sigma_end=0.01) model = SBO.DevSBO(epoch=100, pop_size=50, alpha=0.9, p_m=0.05, psw=0.02) model = SBO.OriginalSBO(epoch=100, pop_size=50, alpha=0.9, p_m=0.05, psw=0.02) model = SMA.OriginalSMA(epoch=100, pop_size=50, p_t=0.03) model = SMA.DevSMA(epoch=100, pop_size=50, p_t=0.03) model = SOA.OriginalSOA(epoch=100, pop_size=50, fc=2) model = SOA.DevSOA(epoch=100, pop_size=50, fc=2) model = SOS.OriginalSOS(epoch=100, pop_size=50) model = TPO.DevTPO(epoch=100, pop_size=50, alpha=0.3, beta=50., theta=0.9) model = TSA.OriginalTSA(epoch=100, pop_size=50) model = VCS.OriginalVCS(epoch=100, pop_size=50, lamda=0.5, sigma=0.3) model = VCS.DevVCS(epoch=100, pop_size=50, lamda=0.5, sigma=0.3) model = WHO.OriginalWHO(epoch=100, pop_size=50, n_explore_step=3, n_exploit_step=3, eta=0.15, p_hi=0.9, local_alpha=0.9, local_beta=0.3, global_alpha=0.2, global_beta=0.8, delta_w=2.0, delta_c=2.0) model = AOA.OriginalAOA(epoch=100, pop_size=50, alpha=5, miu=0.5, moa_min=0.2, moa_max=0.9) model = CEM.OriginalCEM(epoch=100, pop_size=50, n_best=20, alpha=0.7) model = CGO.OriginalCGO(epoch=100, pop_size=50) model = CircleSA.OriginalCircleSA(epoch=100, pop_size=50, c_factor=0.8) model = GBO.OriginalGBO(epoch=100, pop_size=50, pr=0.5, beta_min=0.2, beta_max=1.2) model = HC.OriginalHC(epoch=100, pop_size=50, neighbour_size=50) model = HC.SwarmHC(epoch=100, pop_size=50, neighbour_size=10) model = INFO.OriginalINFO(epoch=100, pop_size=50) model = PSS.OriginalPSS(epoch=100, pop_size=50, acceptance_rate=0.8, sampling_method="LHS") model = RUN.OriginalRUN(epoch=100, pop_size=50) model = SCA.OriginalSCA(epoch=100, pop_size=50) model = SCA.DevSCA(epoch=100, pop_size=50) model = SCA.QleSCA(epoch=100, pop_size=50, alpha=0.1, gama=0.9) model = SHIO.OriginalSHIO(epoch=100, pop_size=50) model = TS.OriginalTS(epoch=100, pop_size=50, tabu_size=5, neighbour_size=20, perturbation_scale=0.05) model = HS.OriginalHS(epoch=100, pop_size=50, c_r=0.95, pa_r=0.05) model = HS.DevHS(epoch=100, pop_size=50, c_r=0.95, pa_r=0.05) model = AEO.OriginalAEO(epoch=100, pop_size=50) model = AEO.EnhancedAEO(epoch=100, pop_size=50) model = AEO.ModifiedAEO(epoch=100, pop_size=50) model = AEO.ImprovedAEO(epoch=100, pop_size=50) model = AEO.AugmentedAEO(epoch=100, pop_size=50) model = GCO.OriginalGCO(epoch=100, pop_size=50, cr=0.7, wf=1.25) model = GCO.DevGCO(epoch=100, pop_size=50, cr=0.7, wf=1.25) model = WCA.OriginalWCA(epoch=100, pop_size=50, nsr=4, wc=2.0, dmax=1e-6) model = CRO.OriginalCRO(epoch=100, pop_size=50, po=0.4, Fb=0.9, Fa=0.1, Fd=0.1, Pd=0.5, GCR=0.1, gamma_min=0.02, gamma_max=0.2, n_trials=5) model = CRO.OCRO(epoch=100, pop_size=50, po=0.4, Fb=0.9, Fa=0.1, Fd=0.1, Pd=0.5, GCR=0.1, gamma_min=0.02, gamma_max=0.2, n_trials=5, restart_count=50) model = DE.OriginalDE(epoch=100, pop_size=50, wf=0.7, cr=0.9, strategy=0) model = DE.JADE(epoch=100, pop_size=50, miu_f=0.5, miu_cr=0.5, pt=0.1, ap=0.1) model = DE.SADE(epoch=100, pop_size=50) model = DE.SAP_DE(epoch=100, pop_size=50, branch="ABS") model = EP.OriginalEP(epoch=100, pop_size=50, bout_size=0.05) model = EP.LevyEP(epoch=100, pop_size=50, bout_size=0.05) model = ES.OriginalES(epoch=100, pop_size=50, lamda=0.75) model = ES.LevyES(epoch=100, pop_size=50, lamda=0.75) model = ES.CMA_ES(epoch=100, pop_size=50) model = ES.Simple_CMA_ES(epoch=100, pop_size=50) model = FPA.OriginalFPA(epoch=100, pop_size=50, p_s=0.8, levy_multiplier=0.2) model = MA.OriginalMA(epoch=100, pop_size=50, pc=0.85, pm=0.15, p_local=0.5, max_local_gens=10, bits_per_param=4) model = SHADE.OriginalSHADE(epoch=100, pop_size=50, miu_f=0.5, miu_cr=0.5) model = SHADE.L_SHADE(epoch=100, pop_size=50, miu_f=0.5, miu_cr=0.5) model = BRO.OriginalBRO(epoch=100, pop_size=50, threshold=3) model = BRO.DevBRO(epoch=100, pop_size=50, threshold=3) model = BSO.OriginalBSO(epoch=100, pop_size=50, m_clusters=5, p1=0.2, p2=0.8, p3=0.4, p4=0.5, slope=20) model = BSO.ImprovedBSO(epoch=100, pop_size=50, m_clusters=5, p1=0.25, p2=0.5, p3=0.75, p4=0.6) model = CA.OriginalCA(epoch=100, pop_size=50, accepted_rate=0.15) model = CHIO.OriginalCHIO(epoch=100, pop_size=50, brr=0.15, max_age=10) model = CHIO.DevCHIO(epoch=100, pop_size=50, brr=0.15, max_age=10) model = FBIO.OriginalFBIO(epoch=100, pop_size=50) model = FBIO.DevFBIO(epoch=100, pop_size=50) model = GSKA.OriginalGSKA(epoch=100, pop_size=50, pb=0.1, kf=0.5, kr=0.9, kg=5) model = GSKA.DevGSKA(epoch=100, pop_size=50, pb=0.1, kr=0.9) model = HBO.OriginalHBO(epoch=100, pop_size=50, degree=3) model = HCO.OriginalHCO(epoch=100, pop_size=50, wfp=0.65, wfv=0.05, c1=1.4, c2=1.4) model = ICA.OriginalICA(epoch=100, pop_size=50, empire_count=5, assimilation_coeff=1.5, revolution_prob=0.05, revolution_rate=0.1, revolution_step_size=0.1, zeta=0.1) model = LCO.OriginalLCO(epoch=100, pop_size=50, r1=2.35) model = LCO.ImprovedLCO(epoch=100, pop_size=50) model = LCO.DevLCO(epoch=100, pop_size=50, r1=2.35) model = WarSO.OriginalWarSO(epoch=100, pop_size=50, rr=0.1) model = TOA.OriginalTOA(epoch=100, pop_size=50) model = TLO.OriginalTLO(epoch=100, pop_size=50) model = TLO.ImprovedTLO(epoch=100, pop_size=50, n_teachers=5) model = TLO.ETLBO(epoch=100, pop_size=50, elite_size=4) model = SSDO.OriginalSSDO(epoch=100, pop_size=50) model = SPBO.OriginalSPBO(epoch=100, pop_size=50) model = SPBO.DevSPBO(epoch=100, pop_size=50) model = SARO.OriginalSARO(epoch=100, pop_size=50, se=0.5, mu=50) model = SARO.DevSARO(epoch=100, pop_size=50, se=0.5, mu=50) model = QSA.OriginalQSA(epoch=100, pop_size=50) model = QSA.DevQSA(epoch=100, pop_size=50) model = QSA.OppoQSA(epoch=100, pop_size=50) model = QSA.LevyQSA(epoch=100, pop_size=50) model = QSA.ImprovedQSA(epoch=100, pop_size=50) model = ArchOA.OriginalArchOA(epoch=100, pop_size=50, c1=2, c2=5, c3=2, c4=0.5, acc_max=0.9, acc_min=0.1) model = ASO.OriginalASO(epoch=100, pop_size=50, alpha=50, beta=0.2) model = CDO.OriginalCDO(epoch=100, pop_size=50) model = EFO.OriginalEFO(epoch=100, pop_size=50, r_rate=0.3, ps_rate=0.85, p_field=0.1, n_field=0.45) model = EFO.DevEFO(epoch=100, pop_size=50, r_rate=0.3, ps_rate=0.85, p_field=0.1, n_field=0.45) model = EO.OriginalEO(epoch=100, pop_size=50) model = EO.AdaptiveEO(epoch=100, pop_size=50) model = EO.ModifiedEO(epoch=100, pop_size=50) model = EVO.OriginalEVO(epoch=100, pop_size=50) model = FLA.OriginalFLA(epoch=100, pop_size=50, C1=0.5, C2=2.0, C3=0.1, C4=0.2, C5=2.0, DD=0.01) model = HGSO.OriginalHGSO(epoch=100, pop_size=50, n_clusters=3) model = MVO.OriginalMVO(epoch=100, pop_size=50, wep_min=0.2, wep_max=1.0) model = MVO.DevMVO(epoch=100, pop_size=50, wep_min=0.2, wep_max=1.0) model = NRO.OriginalNRO(epoch=100, pop_size=50) model = RIME.OriginalRIME(epoch=100, pop_size=50, sr=5.0) model = SA.OriginalSA(epoch=100, pop_size=50, temp_init=100, step_size=0.1) model = SA.GaussianSA(epoch=100, pop_size=50, temp_init=100, cooling_rate=0.99, scale=0.1) model = SA.SwarmSA(epoch=100, pop_size=50, max_sub_iter=5, t0=1000, t1=1, move_count=5, mutation_rate=0.1, mutation_step_size=0.1, mutation_step_size_damp=0.99) model = WDO.OriginalWDO(epoch=100, pop_size=50, RT=3, g_c=0.2, alp=0.4, c_e=0.4, max_v=0.3) model = TWO.OriginalTWO(epoch=100, pop_size=50) model = TWO.EnhancedTWO(epoch=100, pop_size=50) model = TWO.OppoTWO(epoch=100, pop_size=50) model = TWO.LevyTWO(epoch=100, pop_size=50) model = ABC.OriginalABC(epoch=100, pop_size=50, n_limits=50) model = ACOR.OriginalACOR(epoch=100, pop_size=50, sample_count=25, intent_factor=0.5, zeta=1.0) model = AGTO.OriginalAGTO(epoch=100, pop_size=50, p1=0.03, p2=0.8, beta=3.0) model = AGTO.MGTO(epoch=100, pop_size=50, pp=0.03) model = BeesA.OriginalBeesA(epoch=100, pop_size=50, selected_site_ratio=0.5, elite_site_ratio=0.4, selected_site_bee_ratio=0.1, elite_site_bee_ratio=2.0, dance_radius=0.1, dance_reduction=0.99) model = BeesA.CleverBookBeesA(epoch=100, pop_size=50, n_elites=16, n_others=4, patch_size=5.0, patch_reduction=0.985, n_sites=3, n_elite_sites=1) model = BeesA.ProbBeesA(epoch=100, pop_size=50, recruited_bee_ratio=0.1, dance_radius=0.1, dance_reduction=0.99) model = BES.OriginalBES(epoch=100, pop_size=50, a_factor=10, R_factor=1.5, alpha=2.0, c1=2.0, c2=2.0) model = BFO.OriginalBFO(epoch=100, pop_size=50, Ci=0.01, Ped=0.25, Nc=5, Ns=4, d_attract=0.1, w_attract=0.2, h_repels=0.1, w_repels=10) model = BFO.ABFO(epoch=100, pop_size=50, C_s=0.1, C_e=0.001, Ped=0.01, Ns=4, N_adapt=2, N_split=40) model = ZOA.OriginalZOA(epoch=100, pop_size=50) model = WOA.OriginalWOA(epoch=100, pop_size=50) model = WOA.HI_WOA(epoch=100, pop_size=50, feedback_max=10) model = WaOA.OriginalWaOA(epoch=100, pop_size=50) model = TSO.OriginalTSO(epoch=100, pop_size=50) model = TDO.OriginalTDO(epoch=100, pop_size=50) model = STO.OriginalSTO(epoch=100, pop_size=50) model = SSpiderO.OriginalSSpiderO(epoch=100, pop_size=50, fp_min=0.65, fp_max=0.9) model = SSpiderA.DevSSpiderA(epoch=100, pop_size=50, r_a=1.0, p_c=0.7, p_m=0.1) model = SSO.OriginalSSO(epoch=100, pop_size=50) model = SSA.OriginalSSA(epoch=100, pop_size=50, ST=0.8, PD=0.2, SD=0.1) model = SSA.DevSSA(epoch=100, pop_size=50, ST=0.8, PD=0.2, SD=0.1) model = SRSR.OriginalSRSR(epoch=100, pop_size=50) model = SLO.OriginalSLO(epoch=100, pop_size=50) model = SLO.ModifiedSLO(epoch=100, pop_size=50) model = SLO.ImprovedSLO(epoch=100, pop_size=50, c1=1.2, c2=1.5) model = SHO.OriginalSHO(epoch=100, pop_size=50, h_factor=5.0, n_trials=10) model = SFO.OriginalSFO(epoch=100, pop_size=50, pp=0.1, AP=4.0, epsilon=0.0001) model = SFO.ImprovedSFO(epoch=100, pop_size=50, pp=0.1) model = ServalOA.OriginalServalOA(epoch=100, pop_size=50) model = SeaHO.OriginalSeaHO(epoch=100, pop_size=50) model = SCSO.OriginalSCSO(epoch=100, pop_size=50) model = POA.OriginalPOA(epoch=100, pop_size=50) model = PFA.OriginalPFA(epoch=100, pop_size=50) model = OOA.OriginalOOA(epoch=100, pop_size=50) model = NGO.OriginalNGO(epoch=100, pop_size=50) model = NMRA.OriginalNMRA(epoch=100, pop_size=50, pb=0.75) model = NMRA.ImprovedNMRA(epoch=100, pop_size=50, pb=0.75, pm=0.01) model = MSA.OriginalMSA(epoch=100, pop_size=50, n_best=5, partition=0.5, max_step_size=1.0) model = MRFO.OriginalMRFO(epoch=100, pop_size=50, somersault_range=2.0) model = MRFO.WMQIMRFO(epoch=100, pop_size=50, somersault_range=2.0, pm=0.5) model = MPA.OriginalMPA(epoch=100, pop_size=50) model = MGO.OriginalMGO(epoch=100, pop_size=50) model = MFO.OriginalMFO(epoch=100, pop_size=50) model = JA.OriginalJA(epoch=100, pop_size=50) model = JA.LevyJA(epoch=100, pop_size=50) model = JA.DevJA(epoch=100, pop_size=50) model = HHO.OriginalHHO(epoch=100, pop_size=50) model = HGS.OriginalHGS(epoch=100, pop_size=50, PUP=0.08, LH=10000) model = HBA.OriginalHBA(epoch=100, pop_size=50) model = GWO.OriginalGWO(epoch=100, pop_size=50) model = GWO.RW_GWO(epoch=100, pop_size=50) model = GTO.OriginalGTO(epoch=100, pop_size=50, A=0.4, H=2.0) model = GTO.Matlab101GTO(epoch=100, pop_size=50) model = GTO.Matlab102GTO(epoch=100, pop_size=50) model = GOA.OriginalGOA(epoch=100, pop_size=50, c_min=0.00004, c_max=1.0) model = GJO.OriginalGJO(epoch=100, pop_size=50) model = FOX.OriginalFOX(epoch=100, pop_size=50, c1=0.18, c2=0.82) model = FOA.OriginalFOA(epoch=100, pop_size=50) model = FOA.WhaleFOA(epoch=100, pop_size=50) model = FOA.DevFOA(epoch=100, pop_size=50) model = FFO.OriginalFFO(epoch=100, pop_size=50) model = FFA.OriginalFFA(epoch=100, pop_size=50, gamma=0.001, beta_base=2, alpha=0.2, alpha_damp=0.99, delta=0.05, exponent=2) model = FA.OriginalFA(epoch=100, pop_size=50, max_sparks=50, p_a=0.04, p_b=0.8, max_ea=40, m_sparks=50) model = ESOA.OriginalESOA(epoch=100, pop_size=50) model = EHO.OriginalEHO(epoch=100, pop_size=50, alpha=0.5, beta=0.5, n_clans=5) model = DO.OriginalDO(epoch=100, pop_size=50) model = DMOA.OriginalDMOA(epoch=100, pop_size=50, n_baby_sitter=3, peep=2) model = DMOA.DevDMOA(epoch=100, pop_size=50, peep=2) model = CSO.OriginalCSO(epoch=100, pop_size=50, mixture_ratio=0.15, smp=5, spc=False, cdc=0.8, srd=0.15, c1=0.4, w_min=0.4, w_max=0.9) model = CSA.OriginalCSA(epoch=100, pop_size=50, p_a=0.3) model = CoatiOA.OriginalCoatiOA(epoch=100, pop_size=50) model = COA.OriginalCOA(epoch=100, pop_size=50, n_coyotes=5) model = BSA.OriginalBSA(epoch=100, pop_size=50, ff=10, pff=0.8, c1=1.5, c2=1.5, a1=1.0, a2=1.0, fc=0.5)

## Newly added algorithms in version 3.0.3 model = GWO.GWO_WOA(epoch=100, pop_size=50) model = GWO.IGWO(epoch=1000, pop_size=50, a_min = 0.02, a_max = 2.2) model = GWO.ChaoticGWO(epoch=1000, pop_size=50, chaotic_name="chebyshev", initial_chaotic_value=0.7) model = GWO.FuzzyGWO(epoch=1000, pop_size=50, fuzzy_name="increase") model = GWO.IncrementalGWO(epoch=1000, pop_size=50, explore_factor=1.5) model = GWO.ExGWO(epoch=1000, pop_size=50) model = GWO.DS_GWO(epoch=1000, pop_size=50, explore_ratio=0.4, n_groups=5) model = GWO.IOBL_GWO(epoch=1000, pop_size=50) model = GWO.OGWO(epoch=1000, pop_size=50, miu_factor=2.0, jumping_rate=0.05) model = GWO.ER_GWO(epoch=1000, pop_size=50, a_initial=2.0, a_final=0.0, miu_factor=1.0001) model = GWO.CG_GWO(epoch=1000, pop_size=50) model = ESO.OriginalESO(epoch=1000, pop_size=50) model = AO.AAO(epoch=1000, pop_size=50, sharpness=10.0, sigmoid_midpoint=0.5) model = EPC.DevEPC(epoch=1000, pop_size=50, heat_damping_factor=0.95, mutation_factor=0.1, spiral_a=1.0, spiral_b=0.5) model = SMO.DevSMO(epoch=1000, pop_size=50, max_groups = 5, perturbation_rate = 0.7) model = SquirrelSA.OriginalSquirrelSA(epoch=1000, pop_size=50, n_food_sources=4, predator_prob=0.1, gliding_constant=1.9, scaling_factor=18, beta=1.5) model = AFT.OriginalAFT(epoch=1000, pop_size=50) model = CDDO.OriginalCDDO(epoch=1000, pop_size=50, pattern_size=10, creativity_rate=0.1) model = FDO.OriginalFDO(epoch=1000, pop_size=50, weight_factor=0.1) model = LSHADEcnEpSin.OriginalLSHADEcnEpSin(epoch=1000, pop_size=50, miu_f = 0.5, miu_cr = 0.5, freq = 0.5, memory_size = 5, ps = 0.5, pc = 0.4, pop_size_min = 10) model = IMODE.OriginalIMODE(epoch=1000, pop_size=50, memory_size=5, archive_size=20)

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✅ Examples
Simple Benchmark Function
MEALPY allows you to define your optimization problem in a couple of ways.
1. Define Problem as a Dictionary
You can quickly define your problem using a Python dictionary. However, this approach is only valid for problems with float decision variables.

def objective_function(solution): return np.sum(solution**2)

problem = { "obj_func": objective_function, "bounds": FloatVar(lb=(-100., )30, ub=(100., )30), "minmax": "min", "log_to": "console", }

Run the algorithm

#### 2. Define a Custom Problem Class
For more complex scenarios, especially when your decision variables are not exclusively FloatVar, 
we recommend defining a custom class that inherits from the Problem class.
Let's demonstrate this with a simple "Squared" class.

In the __init__ method of your custom Problem class (e.g., Squared class), you must set the bounds and minmax attributes of the problem.

+ bounds: Defines the search space and the type of decision variables (e.g., FloatVar, IntegerVar).

+ minmax: A string indicating whether the problem is a minimization ("min") or maximization ("max") problem.

After defining the initialization, you must override the abstract method obj_func(). This method is the core of your problem definition:

+ It takes a single parameter: solution (the encoded solution vector generated by the optimizer).
+ It must return the objective function value (or fitness) for the given solution.
The resulting code structure for a custom problem class would look similar to the snippet below. 
You can include any additional parameters you need in your custom class (like 'data' or 'name' in this example).

Our custom problem class

def obj_func(self, solution): return np.sum(solution ** 2)

Now, we define an algorithm, and pass an instance of our Squared class as the problem argument.

Show some attributes