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

TL;DR

This paper introduces the States of Matter Search (SMS), a novel evolutionary algorithm inspired by physical states of matter, which dynamically balances exploration and exploitation to improve optimization performance.

Contribution

The paper presents a new nature-inspired algorithm that models exploration and exploitation phases based on states of matter, enhancing search efficiency in evolutionary algorithms.

Findings

Improves balance between exploration and exploitation.

Enhances global search capabilities.

Adapts exploration/exploitation ratios dynamically.

Abstract

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.