Use this optimization step or heuristic to study an explicitly defined objective and constraints with reproducible parameters, initialization, and stopping rules.

Distance matrix

Required list input.

Ant count

Required integer input.

Iterations

Required integer input.

Evaporation

Required number input.

Seed

Required string input.

Search for a short traveling-salesperson tour with a reproducible ant-colony heuristic. The tool evaluates the supplied inputs together and returns the named outputs below; it does not infer omitted operating conditions or change the units shown.^[1]

Best tour

The resulting best tour returned as an object.

• Convergence and solution quality depend on smoothness, convexity, scaling, gradients, conditioning, hyperparameters, randomness, constraints, and implementation details. Non-convex methods need not find a global optimum.
• Use finite inputs in the displayed units and preserve more precision than the final presentation requires. Independently verify safety-critical, financial, compliance, or production decisions.

Scale variables, monitor objective and constraint residuals, compare starts and algorithms, and verify small instances or local optimality with an independent solver.

• Particle Swarm Optimization Solver

  Minimize Sphere or Rastrigin functions with a reproducible bounded particle swarm.

• Non-Convex Multistart Solver

  Run bounded multistart gradient descent on a selected one-dimensional non-convex benchmark.

• Robust Minimax Optimization Solver

  Find a one-dimensional decision that minimizes worst squared loss across supplied scenarios.