### Performance of a Steady State Quantum Genetic Algorithm for Multi/Many-objective Engineering Optimization Problems

Abstract

In this paper, we introduce a novel decomposition based steady state quantum genetic algorithm for the solution of
engineering optimization problems. Systematic sampling is used to generate reference directions and a small population
of quantum individuals (solutions with variables represented as Q-bits) is evolved using a simple variation operator.
A solution represented using Q-bits has the ability to probabilistically represent a number of solutions defined through
observation. We exploit the benefits of quantum representation within a steady state evolution scheme and illustrate the
behavior of the algorithm using unconstrained DTLZ2 test problem involving 2, 3, 5, and 8 objectives and a set of
multi/many-objective constrained engineering design optimization problems. The underlying motivation of quantum representation
stems from its ability to represent multiple states which offers the potential to evolve a small population of solutions.
This aspect is magnified even further when one attempts to solve a many objective optimization problem where evolution of
a large population of solutions may not be practically viable. The proposed approach is expected to gain more attention in
near future as quantum computing infrastructures become more readily available.