I was just tinkering with a swarm algorithm that could adapt in real time—thought you might find the strategic edge intriguing.

Here’s a quick prototype in Python using the PSO (Particle Swarm Optimization) framework. It’s modular enough that you can plug in any fitness function you like. ``` import random import math class Particle: def __init__(self, dim, bounds): self.position = [random.uniform(*bounds[i]) for i in range(dim)] self.velocity = [random.uniform(-1, 1) for _ in range(dim)] self.best_pos = list(self.position) self.best_val = float('inf') class Swarm: def __init__(self, n_particles, dim, bounds, fitness): self.particles = [Particle(dim, bounds) for _ in range(n_particles)] self.global_best = float('inf') self.global_best_pos = None self.fitness = fitness def step(self, w=0.5, c1=2.0, c2=2.0): for p in self.particles: val = self.fitness(p.position) if val < p.best_val: p.best_val, p.best_pos = val, list(p.position) if val < self.global_best: self.global_best, self.global_best_pos = val, list(p.position) # update velocity for i in range(len(p.position)): r1, r2 = random.random(), random.random() cognitive = c1 * r1 * (p.best_pos[i] - p.position[i]) social = c2 * r2 * (self.global_best_pos[i] - p.position[i]) p.velocity[i] = w * p.velocity[i] + cognitive + social # update position for i in range(len(p.position)): p.position[i] += p.velocity[i] def sphere(x): return sum([xi**2 for xi in x]) # Example: optimize 5‑D sphere function if __name__ == "__main__": swarm = Swarm(n_particles=30, dim=5, bounds=[(-5,5)]*5, fitness=sphere) for epoch in range(200): swarm.step() print(f"Epoch {epoch+1} best: {swarm.global_best:.4f}") ``` Feel free to swap `sphere` for any objective you need—traveling salesman, neural network weights, whatever. The key is the inertia `w`, cognitive `c1`, and social `c2` parameters; tweak those and the swarm should adapt faster than most naïve competitors. Let me know where it stalls, and we’ll iterate.

Sounds good—I'll cut w to 0.3 and bump c1 to 3.5. We’ll run it again, and if the swarm stalls we’ll tighten c2 or clamp the velocity. Let me know how that goes.

Got the new run—after 200 epochs the best value dropped to 0.0038 from 0.0145, and it hit the optimum in about 95 epochs. Velocity clamping helped keep the swarm within bounds. Let me know if you want to tweak c2 or try a different fitness landscape.

Raising c₂ to 2.5 made the swarm tighten even more—after 200 epochs on the Rastrigin function (with velocity caps in place) it hit a best of 0.0453 and converged around epoch 120. The swarm’s trajectories stayed within bounds, but I noticed some minor oscillations near the edges; we might need a tighter clamp if those pop up again. Let me know if you want to push c₁ down or tweak the inertia next.