Heuristic — swarm metaheuristic

Gravitational Search Algorithm


Alias	`gsa`, `gravitational_search`
Type	Heuristic — swarm metaheuristic
Auto-seeds from	`shuffle` (random swarm)

Description

Physics-inspired swarm metaheuristic where each agent is a candidate tour with a mass proportional to its fitness and a velocity (an ordered list of position swaps). Heavier agents (shorter tours) attract lighter ones; each epoch the gravitational constant decays, shifting the algorithm from broad exploration to convergence.

TSP-specific adaptations (deviations from Rashedi et al. 2009):

Adaptation	Reason
Discrete velocity as swap list	TSP has no continuous position space; swap sequences from `swap_sequence(i,j)` approximate the continuous update
Spread-based mass: `m_i = (worst − cost_i) / Σ(worst − cost_j)`	Directly from Rashedi 2009; maps fitness to mass with guaranteed [0,1] range
Uniform-mass fallback when all costs equal	Prevents NaN when the population converges to identical tour costs
Fixed inertia `W = 0.0`	Empirically: carrying stale swap indices across epochs added noise faster than gravity overcame it; inertia disabled after tuning on berlin52
Fixed Kbest = ⌈N/2⌉	v1 simplification; v2 follow-up will decay K linearly from N → 1 (full Rashedi fidelity)
Velocity cap at `⌈0.35 · n⌉` swaps	Prevents tour scrambling; same cap as PSO
PSO-style always-accept position	Simplifies update loop; gbest tracked separately

Auto-expands to pipeline(shuffle, gsa).

Options

Flag	Description	Default
`--epochs`	Maximum iterations	10 000
`--n_nearest`	Number of agents (floored at 25)	25

Usage

teeline solve gsa -i ./data/tsplib/berlin52.tsp
teeline solve gravitational_search -i ./data/tsplib/berlin52.tsp --epochs=500
teeline solve gsa -i ./data/tsplib/berlin52.tsp --n_nearest=50

References

Rashedi, E., Nezamabadi-pour, H. & Saryazdi, S. (2009) — GSA: A Gravitational Search Algorithm. Information Sciences, 179(13), 2232–2248.
Gravitational search algorithm (Wikipedia)