v2.5.3-alpha.1

Update yaml for next version

v2.5.2

Update

• Fixed bug all fitness values are equals in function "get index roulette wheel selection" in Optimizer class

• Rename AdaptiveAEO by AugmentedAEO (Add reference)

• Update text of Dwarf Mongoose Optimization Algorithm belongs to Swarm-based group

• Fixed all tests and update all documents

• Update Termination class, you can now design multiple Stopping Conditions for Optimizer

• Bio-based group:

  • Add Brown-Bear Optimization Algorithm (BBOA)
    • Ref: A Novel Brown-bear Optimization Algorithm for Solving Economic Dispatch Problem

• Human-based group:

  • Add Heap-based optimizer (HBO)
    • Ref: Heap-based optimizer inspired by corporate rank hierarchy for global optimization
  • Add War Strategy Optimization (WarSO)
    • Ref: War Strategy Optimization Algorithm: A New Effective Metaheuristic Algorithm for Global Optimization
  • Add Human Conception Optimizer (HCO)
    • Ref: A novel Human Conception Optimizer for solving optimization problems

• Math-based group:

  • Add Q-Learning Embedded Sine Cosine Algorithm (QLESCA)
    • Ref: Q-learning embedded sine cosine algorithm (QLESCA)
  • Add Success History Intelligent Optimizer (SHIO)
    • Ref: Success history intelligent optimizer

• Physics-based group:

  • Add rime-ice (RIME)
    • Ref: RIME: A physics-based optimization
  • Add Energy Valley Optimizer (EVO)
    • Ref: Energy valley optimizer: a novel metaheuristic algorithm
  • Add Chernobyl Disaster Optimizer (CDO)
    • Ref: Chernobyl disaster optimizer (CDO): a novel meta-heuristic method for global optimization
  • Add Fick's Law Algorithm (FLA)
    • Ref: Not accepted yet

• Evolutionary-based group:

  • Add CMA-ES and Simple-CMA-ES
    • Ref: Completely derandomized self-adaptation in evolution strategies.

• Swarm-based group:

  • Add Wavelet Mutation and Quadratic Interpolation MRFO (WMQIMRFO)
    • Ref: An enhanced manta ray foraging optimization algorithm for shape optimization of complex CCG-Ball curves
  • Add Egret Swarm Optimization Algorithm (ESOA)
    • Ref: Egret Swarm Optimization Algorithm: An Evolutionary Computation Approach for Model Free Optimization
  • Add Sea-Horse Optimization (SeaHO)
    • Ref: Sea-horse optimizer: A nature-inspired meta-heuristic for global optimization and engineering application
  • Add Mountain Gazelle Optimizer (MGO)
    • Ref: Mountain Gazelle Optimizer: A new Nature-inspired Metaheuristic Algorithm for Global Optimization Problems
  • Add Golden jackal optimization (GJO)
    • Ref: Golden jackal optimization: A novel nature-inspired optimizer for engineering applications
  • Add Fox Optimizer (FOX)
    • Ref: FOX: a FOX-inspired optimization algorithm
  • Add Giant Trevally Optimizer (GTO)
    • Ref: Giant Trevally Optimizer (GTO): A Novel Metaheuristic Algorithm for Global Optimization and Challenging Engineering Problems

• Warning: The list of all algorithms below we should avoid to use it

  • Add Zebra Optimization Algorithm (ZOA)
    • Ref: Zebra Optimization Algorithm: A New Bio-Inspired Optimization Algorithm for Solving Optimization Algorithm
  • Add Osprey Optimization Algorithm (OOA)
    • Ref: Osprey optimization algorithm: A new bio-inspired metaheuristic algorithm for solving engineering optimization problems
  • Add Coati Optimization Algorithm (CoatiOA)
    • Ref: Coati Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems
  • Add Pelican Optimization Algorithm (POA)
    • Ref: Pelican optimization algorithm: A novel nature-inspired algorithm for engineering applications
  • Add Northern Goshawk Optimization (NGO)
    • Ref: Northern Goshawk Optimization: A New Swarm-Based Algorithm for Solving Optimization Problems
  • Add Serval Optimization Algorithm (ServalOA)
    • Ref: Serval Optimization Algorithm: A New Bio-Inspired Approach for Solving Optimization Problems
  • Add Siberian Tiger Optimization (STO)
    • Ref: Siberian Tiger Optimization: A New Bio-Inspired Metaheuristic Algorithm for Solving Engineering Optimization Problems
  • Add Walrus Optimization Algorithm (WaOA)
    • Ref: Walrus Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm
  • Add Tasmanian Devil Optimization (TDO)
    • Ref: Tasmanian devil optimization: a new bio-inspired optimization algorithm for solving optimization algorithm
  • Add Fennec Fox Optimization (FFO)
    • Ref: Fennec Fox Optimization: A New Nature-Inspired Optimization Algorithm
  • Add Teamwork Optimization Algorithm (TOA)
    • Ref: Teamwork Optimization Algorithm: A New Optimization Approach for Function Minimization/Maximization

• Notes on plagiarism and fake algorithm:

  • OOA and STO with the same exact code
  • POA ServalOA, NGO, WaOA, and TDO with almost the same exact code
  • ZOA and CoatiOA with almost the same exact code
  • FFO is swap two phases of POA
  • TOA is kinda same as OOA and POA

v2.5.2-alpha.2

Test publish on pypi

Optimization 2022

Update

• Add validator when variable can be both int/float value

• Add algorithms to evolutionary-based group:

  • EliteSingleGA and EliteMultiGA class

• Add algorithms to math-based group:

  • weIghted meaN oF vectOrs (INFO) algorithm
  • RUNge Kutta optimizer (RUN)
  • Circle Search Algorithm (CSA)

• Add algorithms to bio-based group:

  • Barnacles Mating Optimizer (BMO)
  • Symbiotic Organisms Search (SOS)
  • Seagull Optimization Algorithm (SOA)
  • Tunicate Swarm Optimization (TSA)

• Add algorithms to swarm-based group:

  • Hybrid Grey Wolf - Whale Optimization Algorithm (GWO_WOA)
  • Marine Predators Algorithm (MPO)
  • Honey Badger Algorithm (HBA)
  • Sand Cat Swarm Optimization (SCSO)
  • Tuna Swarm Optimization (TSO)
  • African Vultures Optimization Algorithm (AVOA)
  • Artificial Rabbits Optimization (ARO)
  • Artificial Gorilla Troops Optimization (AGTO)

• Add algorithms to human-based group:

  • Student Psychology Based Optimization (SPBO) (weak algorithm)
  • Dwarf Mongoose Optimization Algorithm (DMOA) (weak algorithm)

• Fix problem with 1 dimension

• Enhanced the get index roulette wheel selection in Optimizer class

• Update check parallel mode in Optimizer

• Update algorithms that don't support parallel modes

• Update the shebang #! with python codes

• Update examples

Multitask Solving, Hyper-parameter Tuning, Saving and Loading Model

Update

• Add save and load model functionalities in mealpy.utils.io module.
• Add object that hold global/current worst solution in history object
• Add method create_pop_group() in Optimizer class
• Add method before_initialization() in Optimizer class
• Refactor initialization() and after_initialization() in Optimizer class
• Remove before_evolve(), after_evolve(), and levy_flight() in Optimizer class
• Convert termination_start() and termination_end() to check_termination() in Optimizer class
• Remove boundary.py in utils
• Add set_parameters() and get_parameters() in all optimizers
• Update new Problem class, move problem parameter from Optimizer to solve() function.
• Fix bug printing same entry multiple times in logger.
• Fix bug exit() in Optimizer and utils package.
• Update new Termination class, move termination parameter from Optimizer to solve() function.
• Add Multitask class that can run multiple optimizers on multiple problems with multiple trials.
• Refactor all optimizers.
• Add Tuner class that can help tuning hyper-parameters of optimizer.
• Add examples how to build new optimizer.
• Add examples for Multitask and Tuner class.
• Update documents, examples, tests

v2.4.2

Update

• Add n_workers variable to solve() function in Optimizer class
  • n_workers only effect by parallel mode such as "process" and "thread"
  • n_workers default value is None and based on concurrent.futures module
• Add 1 more Optional input parameter to the fitness function
• Fix bug trajectory chart
• Update fitness and objective chart
• Remove supporting Python < 3.7, Mealpy only supports Python >=3.7
• Group probabilistic is merged into math-based group
• Update documents, examples, tests

v2.4.1

Update

• Add after_initialization(), termination_end() to Optimizer class
• Update create_solution(), initialization() in Optimizer class
• Add "starting_positions" parameter to solve() function in Optimizer class
• Fix missing amend_position function in GA
• Fix bug fitness value in history object
• Update 4 training modes in all algorithms

Type: Parallel (no effect on updating process of agents) has 2 training modes:

1. Process: Using multi-cores to update fitness for whole population
2. Thread: Using multi-threads to update fitness for whole population

Type: Sequential has 2 training modes

3. Swarm (no effect on updating process): Updating fitness after the whole population move
4. Single (effect on updating process): Updating fitness after each agent move

• Add agent's history and starting positions to docs.

v2.4.0

Update

• Add mealpy's support functions in terminal: help(mealpy), dir(mealpy)

• Add logger module (Logger class)

• Add validator module (Validator class)

• Change in Optimizer class:

  • remove function get_global_best_global_worst_solution()
  • replace save_optimization_process() by track_optimize_step() and track_optimize_process()
  • update input of Problem and Termination object in Optimizer.
  • add logger
  • add validator and update all algorithms
  • update function: get_special_solutions()
  • rename function: crossover_arthmetic_recombination() to crossover_arithmetic()
  • rename function: get_fitness_position() to get_target_wrapper()
  • rename function: update_fitness_population() to update_target_wrapper_population()

• A default method: generate_position() in Problem class.

• Due to nature's characteristics of different problems, 2 methods can be designed for Optimizer to fit the problem
  are generate_position() and amend_position(). Both methods are moved from Optimizer
  class to Problem class, the create_solution() in Optimizer class will call these methods to create a new solution.

• Update History and Problem class

  • design default amend_position function in Problem class
  • parameter: obj_weight changed to obj_weights
  • add parameter: save_population to control

• Add Pareto-like Sequential Sampling (PSS) to math_based group

v2.3.0

Update

• All algorithms have been updated with the amend_position function for solving the discrete problem.
• Required packages are version reduction to fit python 3.6
• Add examples of how to design and custom a new algorithm based on this framework
• Add examples mealpy solve discrete problems (combinatorial, permutation)

v2.2.0

Update models

• You can pass the Problem dictionary or Problem object to the model.

• You can pass the Termination dictionary or Termination object to the model.

• The objective function is renamed as fitness function (obj_func -> fit_func)

• The general format of a solution is: [position, target]

  • position: numpy vector (1-D array)
  • target: [fitness, list_objectives]
  • list_objectives: [objective 1, objective 2, ...]
  • After the training process, everything can be accessed via the objective "history" (model.history)

• You can name your model and name your fitness function when creating a model

  • model(problem, epoch, pop_size, ...., name='your model name', fit_name='your fitness function name')

• Add new algorithms:

  • Gradient-Based Optimizer (GBO) in math_based group
  • Chaos Game Optimization (CGO) in math_based group

• Remove all dummy algorithms (Not supported anymore)

• Fix bugs:

  • Find idx of min-distance in BRO algorithm
  • Update more strategy for GA algorithm
  • Update child selection process in MA algorithm

Update others

• examples: Update several scenarios for mealpy with other frameworks
• document: Add document website (https://mealpy.readthedocs.io/)