v1.1.0

Version 1.1.0

Change models

• Compose all methods with same parameters into a single class
  • front: Evaluating all possible of all fronts (No need Reference front)
    • Ratio: Metrics Assessing the Number of Pareto Optimal Solutions in the Set
      • RNI: ratio_of_non_dominated_individuals
      • PDI: pareto_dominance_indicator (not implemented yet)
  • pfront (Pareto front): Evaluating single Pareto front (No need Reference front)
    • Distribution: Metrics Focusing on Distribution of the Solutions
      • UD = uniform_distribution
      • NDC = number_of_distinct_choices (not implemented yet)
  • tpfront (True Pareto front): Evaluating Pareto front vs Reference front
    • Ratio: Metrics Assessing the Number of Pareto Optimal Solutions in the Set
      • ER: error_ratio
      • ONVG: overall_non_dominated_vector_generation
    • Spread : Metrics Concerning Spread of the Solutions
      • MS = maximum_spread
    • Closeness: Metrics Measuring the Closeness of the Solutions to the True Pareto Front
      • GD: generational_distance
      • IGD: inverted_generational_distance
      • MPFE: maximum_pareto_front_error
    • Distribution: Metrics Focusing on Distribution of the Solutions
      • S: spacing
      • STE: spacing_to_extend
  • volume (need both Obtained front and Reference front): I kept this file since it using other library
    • HV
    • HAR

Change others

• Examples:
  • Add all examples for all metrics
  • Add example for multiple metrics called at the same time
• Add Change Log file
• Add README.md file
• Add support-data folder for test case

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Version 1.0.0 (First version)

Models

root.py file: contains all need functions such as

1. find non-dominated list function
2. print_messages
3. get_pareto_front_reference_front
4. find_reference_front
5. get_metrics_by_name
6. get_metrics_by_list
7. All Metric class will inherit this Root class.

Closeness: Metrics Measuring the Closeness of the Solutions to the True Pareto Front

1. GD: generational_distance
2. IGD: inverted_generational_distance
3. MPFE: maximum_pareto_front_error

Closeness_diversity: Metrics Measuring the Closeness of the Solutions to the True Pareto Front

1. HV: hyper_volume (using different library)
2. HAR: hyper_area_ratio (using different library)

Distribution: Metrics Focusing on Distribution of the Solutions

1. UD: uniform_distribution
2. S: spacing
3. STE: spacing_to_extend
4. NDC: number_of_distinct_choices (not implemented yet)

Ratio: Metrics Assessing the Number of Pareto Optimal Solutions in the Set

1. RNI: ratio_of_non_dominated_individuals
2. ER: error_ratio
3. ONVG: overall_non_dominated_vector_generation
4. PDI: pareto_dominance_indicator (not implemented yet)

Spread: Metrics Concerning Spread of the Solutions

1. MS: maximum_spread