v3.0.3

General Updates

• Updated project configuration files: setup.py, requirements.txt, and MANIFEST.in.
• Restructured README for better clarity.
• Updated examples and docs with new usage guides.
• Changed license from GPL GNU v3 to MIT.

Bug Fixes

• Fixed n_workers bug in the Optimizer class.
• Fixed bug in parallel mode when passing starting_positions in Optimizer.
• Fixed incorrect handling of fitness as position in AO class.

New Features

1. New Algorithm Categories

• sota_based group: A new category for state-of-the-art algorithms from CEC competitions.
  • Added LSHADEcnEpSin (OriginalLSHADEcnEpSin class).
  • Added IMOTE (OriginalIMOTE class).

2. New Modules

• Added chaotic module.
• Added fuzzy module.

3. Swarm-Based Algorithms

• Grey Wolf Optimizer (GWO) module: Added multiple variants:
  • GWO_WOA, IGWO, ChaoticGWO, FuzzyGWO, IncrementalGWO, ExGWO,
    DS_GWO, IOBL_GWO, OGWO, ER_GWO, CG_GWO.
• Aquila Optimizer (AO) module:
  • Added Adaptive Aquila Optimizer (AAO).
• Added new standalone algorithms:
  • Emperor Penguins Colony (EPC).
  • Spider Monkey Optimization (SMO).
  • Squirrel Search Algorithm (SquirrelSA).
  • Fitness Dependent Optimizer (FDO) (FDO module).

4. Physics-Based Algorithms

• Added Electrical Storm Optimization (ESO) module with OriginalESO class.

5. Human-Based Algorithms

• Added Ali Baba and the Forty Thieves (AFT) (AFT module).
• Added Child Drawing Development Optimization (CCDO) (CCDO module).

✅ This release focuses on:

• Expanding algorithm coverage (swarm-based, physics-based, human-based, and sota-based groups).
• Improving documentation and examples.
• Fixing critical bugs in the optimizer core.
• Transitioning license to MIT for broader usage.

v3.0.2

• Fix bug infinite loop in JADE, OriginalSHADE, and L_SHADE optimizers.
• Fig bug check input conditions in Validator class.
• Fig bug set up init function in Problem class.
• Fix bug in get_roulette_wheel_selection_index() function in Optimizer class.
• Fix bug counting number of function evaluation in Optimizer class.
• Fix bug divide by zero in FLA, BeesA, DMOA, ESOA, FA, SSpiderO, GCO , MRFO, and AVOA.
• Fix bug compare np.all in NRO.
• Fix bug random number verse random vector in MFO.
• Fix bug epoch in MGO.
• Fix bug parallelization in CL-PSO.
• Fix bug compare fitness with Agent in Agent class.
• Replace MixSetVar by CategoricalVar in space module.
• Add SequenceVar to space module for handling tuple, list, and set variables.
• Update get_optimizer_by_class() and get_optimizer_by_name() functions in mealpy module to support new classes.
• Fix bug lower bound in TransferBinaryVar and TransferBoolVar classes.
• Fix bug out of range for choice in GSKA optimizer.
• Update example comment in IWO, SBO, SMA, SA, GTO, GWO, HGS optimizers.
• Update docs and examples.

v3.0.1

• Add transfer function module (please read this paper )
• Add two new datatypes: TransferBinaryVar and TransferBoolVar.
• Fix bug un-order variables in PermutationVar data type.
• Update data type of encoded solution in BoolVar data type.
• Update correct function in BoolVar and BinaryVar.
• Fix bug reproduce results in GA, WCA, and EHO optimizers.
• Fix bug higher probability of 0 value in IntegerVar data type.

v3.0.0

Based on our new proposed classes, solving continuous and discrete problems is never that easy.

Add

• space module with: FloatVar, IntegerVar, StringVar, BoolVar, PermutationVar, BinaryVar, and MixedSetVar classes
  • FloatVar: handle problem with solution's format as float value
  • IntegerVar: handle problem with solution's format as integer value
  • StringVar: handle problem with solution's format as string value
  • BoolVar: handle problem with solution's format as boolean value (True or False)
  • PermutationVar: handle problem with solution's format as permutation value
  • BinaryVar: handle problem with solution's format as binary value (0 or 1)
  • MixedSetVar: handle problem with solution's format as mixed discrete set
• target module with Target class contains:
  • objectives (list), weights (list) to calculate fitness, and fitness (number)
• agent module with: Agentte class that is a placeholder for a search agent, it contains at least two attributes:
  • solution (position - np.ndarray), and a target object

Update

• Convert all optimizers to use new classes
• Convert Tuner and MultiTask classes
• Rename all un-official (developed by our team) optimizers to DevOptimizerName
• Update tests and documents
• Update some examples, not all examples have converted yet (utils and applications folders)

v2.5.4

• Remove deepcopy() to improve the computational speed
• Update the parameter's order in Tuner class
• Update the saving's bug when using Termination in Multitask
• Remove ILA optimizer
• Rename "amend_position()" definition in some algorithms to "bounded_position()".
• Add a "amend_position()" function in Optimizer class. This function will call two functions.
  • bounded_position() from optimizer. This means for optimizer level (get in valid range of position)
  • amend_position() from problem. This means for problem level (transform to the correct solution)
• Fix bugs coefficients in GWO-based optimizers.
• Fig bug self.epoch in SCSO optimizer.
• Fix bug self.dyn_pop_size when pop_size is small value
• Move SHADE-based optimizers from DE to SHADE module in evolutionary_based group
• Add Improved Grey Wolf Optimization (IGWO) in GWO algorithm
• Add Tabu Search (TS) to math-based group
• Add get_all_optimizers() and get_optimizer_by_name() in Mealpy
• Rename the OriginalSA to SwarmSA in SA optimizer
• Add the OriginalSA and GaussianSA in SA optimizer
• Update parameters in OriginalHC and SwarmHC
• Update ParameterGrid class to produce the dict with same order as original input
• Add export_figures() to Tuner class. It can draw the hyperparameter tuning process.
• Fix several bugs in docs folders.

v2.5.4-alpha.6

• Fix bug self.dyn_pop_size when pop_size is small value
• Move SHADE-based optimizers from DE to SHADE module in evolutionary_based group
• Add Improved Grey Wolf Optimization (IGWO) in GWO algorithm
• Add Tabu Search (TS) to math-based group
• Add get_all_optimizers() and get_optimizer_by_name() in Mealpy

v2.5.4-alpha.5

• Fig bug self.epoch in SCSO optimizer.

v2.5.4-alpha.4

• Update documents for MultiTask
• Fix bugs coefficients in GWO-based optimizers.

v2.5.4-alpha.3

Release new alpha version

v2.5.4-alpha.2

• Update the parameter's order in Tuner class
• Update the saving's bug when using Termination in Multitask
• Remove ILA optimizer
• Rename "amend_position()" definition in some algorithms to "bounded_position()".
• Add a "amend_position()" function in Optimizer class. This function will call two functions.
  • bounded_position() from optimizer. This means for optimizer level (get in valid range of position)
  • amend_position() from problem. This means for problem level (transform to the correct solution)