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The ability of an Evolutionary Algorithm (EA) to find a global optimalsolution depends on its capacity to find a good rate between exploitation offound so far elements and exploration of the search space. Inspired by naturalphenomena, researchers have developed many successful evolutionary algorithmswhich, at original versions, define operators that mimic the way nature solvescomplex problems, with no actual consideration of the exploration/exploitationbalance. In this paper, a novel nature-inspired algorithm called the States ofMatter Search (SMS) is introduced. The SMS algorithm is based on the simulationof the states of matter phenomenon. In SMS, individuals emulate molecules whichinteract to each other by using evolutionary operations which are based on thephysical principles of the thermal-energy motion mechanism. The algorithm isdevised by considering each state of matter at one differentexploration/exploitation ratio. The evolutionary process is divided into threephases which emulate the three states of matter: gas, liquid and solid. In eachstate, molecules (individuals) exhibit different movement capacities. Beginningfrom the gas state (pure exploration), the algorithm modifies the intensitiesof exploration and exploitation until the solid state (pure exploitation) isreached. As a result, the approach can substantially improve the balancebetween exploration/exploitation, yet preserving the good search capabilitiesof an evolutionary approach.

An optimization algorithm inspired by the States of Matter that improves the balance between exploration and exploitation