Optimization Algorithms

Understand the algorithms that power SolverForge’s optimization.

Tags:

SolverForge uses a combination of algorithms to find high-quality solutions efficiently. Understanding these algorithms helps you tune solver performance.

Topics

Construction Heuristics - Build an initial solution quickly
Local Search - Improve the solution iteratively
Exhaustive Search - Find optimal solutions (for small problems)
Move Selectors - Reference for available move types

Algorithm Phases

SolverForge typically runs algorithms in phases:

1. Construction Heuristic
   └── Builds initial solution (fast, may be suboptimal)

2. Local Search
   └── Iteratively improves solution (most time spent here)

3. (Optional) Exhaustive Search
   └── Proves optimality (only feasible for small problems)

Construction Heuristics

Build an initial feasible solution quickly:

Algorithm	Description
First Fit	Assign first available value
First Fit Decreasing	Assign largest/most constrained entities first
Cheapest Insertion	Insert at lowest cost position
Allocate from Pool	Allocate entities from a pool

Local Search Algorithms

Iteratively improve the solution:

Algorithm	Description
Hill Climbing	Accept only improving moves
Tabu Search	Track recent moves to avoid cycles
Simulated Annealing	Accept worse moves with decreasing probability
Late Acceptance	Accept if better than solution from N steps ago
Great Deluge	Accept if within rising threshold