Metaheuristics

• A metaheuristic is a heuristic designed to find or select a heuristic that gives a “good enough” solution to an optimization problem, especially applicable for NP hard problems.

• Metaheuristics are generally applicable, but do not guarantee optimality or polynomial running time

  • From the perspective of logic, Metaheuristics are inductive and abductive rather than deductive.

• For each heuristic, let be the function being optimized. is an element in the solution space of the problem.

Metropolis-Hastings Algorithm §

• The Metropolis-Hastings Algorithm is a Monte Carlo method for generating random samples from a probability distribution whose distribution is difficult to compute.
• The goal is to construct a Markov Process that simulates the behavior of — it has the same stationary behavior as .

g(x'|x) : = distribution giving the probability of generating x' given x

t = 0
x_0 := initial solution chosen

loop:
	Generate x'~ g(x' | x)

	Calculate the acceptance probability (defined below)

	Accept or reject based on the acceptance probability.
	if accept:
		x_{t+1} = x'
	else: 
		x_{t+1} = x_t
	t =  t + 1

• The acceptance probability is defined as follows

Simulated Annealing §

• Inspired by the annealing process in metallurgy where metals are heated and cooled to improve strength.
• We introduce the following
  • A cost function that determines the cost of .
  • A temperature hyperparameter in the same units as the cost function
  • A neighborhood of , denoted . We sample from this neighborhood, denoted .
  • A convergence criterion.
  • A cooling schedule which determines the temperature at time .

t = 0
x_0 := initial solution
x* := optimal solution found so far. Initialize as x_0

while covnergence criterion not met:
	Genenrate x' ~ N(x)
	Get temperature T from cooling schedule at time t.

	Calculate acceptance probability (see below)

	if accept:
		x* = x'
	else: 
		x* = x_t

	t = t + 1

return x*

• The acceptance probability is defined as

  Where is the Boltzmann constant.

• The behavior of the above function is that of a Boltzmann distribution, mimicking the behavior of particles in thermodynamic equilibrium.

Animal-Inspired Search §

• ¹ propose a new swarm optimization approach called sparrow swarm search inspired by group wisdom, foraging and anti-predation observed in sparrows. Sparrows have two classes — producers and scroungers. Sparrows follow the following rules
  • Producers have high energy reserves. The level of energy reserves depends on the assessment of fitness values of the individuals.
  • Sparrows detect predators and send an alarm. When the alarm is strong enough, producers lead all scroungers to a safe area.
  • Each sparrow can become a producer as long as it searches for the better food sources, but the proportion of the producers and the scroungers is unchanged in the whole population
  • Sparrows with higher energy are producers. Starving scroungers fly to other places for food to get more energy.
  • The scroungers follow the producer who can provide the best food to search for food.
  • The sparrows at the edge of the group quickly move toward the safe area to get a better position when aware of danger, while the sparrows in the middle of the group randomly walk in order to be close to others.

Sparrow Swarm Search Algorithm from Xue and Shen (2020)

Links §

• Metaheuristics
• Fuzzy Computation

Footnotes §

1. Xue and Shen (2020) A novel swarm intelligence optimization approach: sparrow search algorithm ↩