A New Dimension in Metaheuristics: A Survey of Quantum-Inspired Evolutionary Search

Abstract

Quantum-inspired evolutionary algorithms (QIEAs) have emerged as an increasingly prominent subset of metaheuristic methods that draw conceptual parallels from quantum mechanics, particularly superposition and interference, to augment conventional evolutionary computation. This paper offers a concise yet critical overview of developments in this area. It begins by revisiting the foundational principles that underpin QIEAs, including qubit representation and the general structure of the quantum-inspired genetic algorithm (QIGA). The main section provides a systematic synthesis of the literature, identifying the evolution of algorithmic variants ranging from binary-coded to real-valued models and discussing their demonstrated effectiveness in diverse problem domains such as combinatorial optimization, image analysis, and energy systems. The review notes that while empirical studies consistently report encouraging performance, much of the existing work remains oriented toward application rather than theory. In particular, there is limited progress in establishing rigorous analytical models and standardized parameter-setting procedures. The paper concludes by outlining directions for future research, suggesting that deeper theoretical inquiry, the exploration of sophisticated hybridization strategies, and eventual integration with actual quantum computing platforms may significantly advance the field.