List of Algorithm Components¶

This page provides a comprehensive overview of all algorithm components available in the Mork framework. Components are organized by their role in the optimization process.

Metaheuristics¶

High-level algorithmic strategies that guide the search process. These are the main algorithms you'll use to solve optimization problems.

Algorithm	Description	Documentation
Variable Neighborhood Search (VNS)	Systematically changes neighborhood structures to escape local optima	VNS Documentation
Simulated Annealing (SA)	Temperature-based probabilistic technique inspired by metallurgy	SA Documentation
Iterated Greedy (IG)	Destruction-reconstruction metaheuristic that iteratively destroys and rebuilds solutions	IG Documentation
Scatter Search	Population-based metaheuristic using reference sets and solution combination	Scatter Search Documentation
Multi-Start Algorithm	Simple but effective strategy that runs constructive+improvement methods multiple times	Multi-Start Documentation

Constructive Methods¶

Components that build initial solutions from scratch. These methods create feasible solutions that can then be improved.

Constructor	Description	Documentation
Constructive (Base)	Abstract base class for all constructive methods	Constructive Documentation
GRASP Constructive	Greedy Randomized Adaptive Search Procedure for construction phase	GRASP Documentation
Reconstructive	Specialized constructor for rebuilding partially destroyed solutions	Reconstructive Documentation

Improvement Methods¶

Components that take a solution and try to improve it. These methods cannot return worse solutions than their input.

Improver	Description	Documentation
Improver (Base)	Abstract base class for all improvement methods	Improver Documentation
Local Search	Base class for local search algorithms that explore neighborhoods and host the supported descent strategies	Local Search Documentation
LocalSearchBestImprovement	Local search that always picks the best improving move in the neighborhood	Best-improvement strategy
LocalSearchFirstImprovement	Local search that applies the first improving move found	First-improvement strategy
Simulated Annealing (as Improver)	SA can be used as an improvement method with temperature-based acceptance	SA Documentation
Variable Neighborhood Descent (VND)	Systematic exploration of multiple neighborhood structures in a descent manner	VND Documentation

Shake/Perturbation Methods¶

Components that perturb solutions to escape local optima. Unlike improvers, these can worsen the solution.

Shake Method	Description	Documentation
Shake (Base)	Abstract base class for all perturbation methods	Shake Documentation
Destructive	Interface for destruction operators that remove parts of a solution	Destructive Documentation
Random Move Shake	Simple perturbation that applies random moves to the solution	Random Move Documentation

Component Integration¶

All components follow a consistent design pattern and can be combined flexibly:

graph TD
    A[Instance] --> B[Constructive]
    B --> C[Initial Solution]
    C --> D[Improver]
    D --> E[Improved Solution]
    E --> F{Continue?}
    F -->|Yes| G[Shake]
    G --> H[Perturbed Solution]
    H --> D
    F -->|No| I[Final Solution]

Using Components¶

Components are designed to be:

Modular: Each component has a single, well-defined responsibility
Composable: Components can be combined in different ways to create complex algorithms
Reusable: The same component can be used in multiple algorithms
Extensible: Easy to create new components by extending existing ones

Advanced Topics¶

Component Autodetection¶

Components are automatically detected by the framework when annotated with @AlgorithmComponent or when extending framework-provided base classes.

Automatic Algorithm Configuration¶

Components can be automatically configured using irace integration. See irace documentation for details.

Custom Component Types¶

You can create your own component hierarchies by extending existing types or creating new ones. See intro documentation for more information.