Research Area:  Metaheuristic Computing

Abstract:

The purpose of multi-objective optimization is to simultaneously optimize several objective functions that are usually in conflict with each other. An acceptable solution is one that can strike a trade-off between the results of these functions. Although, multi-objective evolutionary algorithms have a good history in solving multi-objective problems, how to find more accurate and diverse solutions set at an acceptable time is still a challenge. In this study, a quantum-inspired multi-objective harmony search algorithm is proposed to solve multi-objective optimization problems. In this algorithm, a new quantum mutation strategy is proposed, which is a combination of harmony improvisation operators and a quantum adaptive rotation gate. While the use of the rotation gate leads to the move to further solutions and complete coverage of the problem space, the improvisation operators (PAR and BW) trigger tiny impulses and mutate into neighbor solutions. The advantage of such an algorithm is to strengthen the balance between the exploration and exploitation processes. Also, the crowding distance metric of the elitism strategy ensures the production of solutions with maximum variety in the problem space. The results of the implementation of this algorithm on multi-objective benchmark functions indicate significant improvement in criteria such as the distance to Pareto optimal, the scattering, and the convergence rate compared to the state-of-the-art methods.

Keywords:  
Multi-objective optimization
Pareto dominance
Quantum evolutionary algorithms
Harmony search algorithm
Elitism

Author(s) Name:   Alireza Sadeghi Hesar, Seyed Reza Kamel & Mahboobeh Houshmand

Publisher name:  Springer

DOI:  10.1007/s00500-021-05799-x

Volume Information:  volume 25, pages 9427–9439 (2021)

Paper Link:   https://link.springer.com/article/10.1007/s00500-021-05799-x