Solving Combinatorial Optimization problems with Quantum inspired Evolutionary Algorithm Tuned using a Novel Heuristic Method

TL;DR

This paper introduces a novel heuristic method for tuning parameters of Quantum inspired Evolutionary Algorithms, significantly improving their performance on combinatorial optimization problems.

Contribution

A new heuristic tuning framework for canonical QEA is proposed, enhancing its efficiency and effectiveness across various optimization problems.

Findings

Tuned QEA outperforms canonical QEA on discrete combinatorial problems.

The parameter tuning framework is adaptable to other algorithms.

Performance improvements validate the effectiveness of the proposed method.

Abstract

Quantum inspired Evolutionary Algorithms were proposed more than a decade ago and have been employed for solving a wide range of difficult search and optimization problems. A number of changes have been proposed to improve performance of canonical QEA. However, canonical QEA is one of the few evolutionary algorithms, which uses a search operator with relatively large number of parameters. It is well known that performance of evolutionary algorithms is dependent on specific value of parameters for a given problem. The advantage of having large number of parameters in an operator is that the search process can be made more powerful even with a single operator without requiring a combination of other operators for exploration and exploitation. However, the tuning of operators with large number of parameters is complex and computationally expensive. This paper proposes a novel heuristic method for tuning parameters of canonical QEA. The tuned QEA outperforms canonical QEA on a class of discrete combinatorial optimization problems which, validates the design of the proposed parameter tuning framework. The proposed framework can be used for tuning other algorithms with both large and small number of tunable parameters.