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

Objective

Required string input.

Dimensions

Required integer input.

Particles

Required integer input.

Iterations

Required integer input.

Minimum bound

Required number input.

Maximum bound

Required number input.

Seed

Required string input.

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

The resulting best solution 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.

• Ant Colony TSP Solver

  Search for a short traveling-salesperson tour with a reproducible ant-colony heuristic.

• Non-Convex Multistart Solver

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

• Diagonal Trust Region Optimizer Step

  Solve a convex diagonal quadratic trust-region subproblem for a bounded Euclidean step. This is a diagonal model, not a full-Hessian solver.