An optimization algorithm inspired by the States of Matter that improves
  the balance between exploration and exploitation
  The ability of an Evolutionary Algorithm (EA) to find a global optimal
solution depends on its capacity to find a good rate between exploitation of
found so far elements and exploration of the search space. Inspired by natural
phenomena, researchers have developed many successful evolutionary algorithms
which, at original versions, define operators that mimic the way nature solves
complex problems, with no actual consideration of the exploration/exploitation
balance. In this paper, a novel nature-inspired algorithm called the States of
Matter Search (SMS) is introduced. The SMS algorithm is based on the simulation
of the states of matter phenomenon. In SMS, individuals emulate molecules which
interact to each other by using evolutionary operations which are based on the
physical principles of the thermal-energy motion mechanism. The algorithm is
devised by considering each state of matter at one different
exploration/exploitation ratio. The evolutionary process is divided into three
phases which emulate the three states of matter: gas, liquid and solid. In each
state, molecules (individuals) exhibit different movement capacities. Beginning
from the gas state (pure exploration), the algorithm modifies the intensities
of exploration and exploitation until the solid state (pure exploitation) is
reached. As a result, the approach can substantially improve the balance
between exploration/exploitation, yet preserving the good search capabilities
of an evolutionary approach.
