Flips that improve the objective value are accepted automatically. When the solver is sweeping for a binary problem, each decision variable is "flipped" based on the objective value impact of that flip. At lower temperatures, moves that don't improve the objective value are less likely to be accepted. In the context of optimization problems, the algorithm starts at an initial high-temperature state where "bad" moves in the system are accepted with a higher probability (low beta, or beta_start), and then slowly "cools" on each sweep until the state reaches the lowest specified temperature (high beta, or beta_stop). In the implementation of this solver, the temperature of a state is represented by parameter beta - the inverse of temperature with the Boltzmann constant set to 1 ($\beta = 1 / T$). The algorithm simulates a state of varying temperatures where the temperature of a state influences the decision-making probability at each step.
Simulated annealing is a Monte Carlo search method named from the heating-cooling methodology of metal annealing.
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