Publication
CEC 2015
Conference paper

Performance of a steady state quantum genetic algorithm for multi/many-objective engineering optimization problems

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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.

Date

10 Sep 2015

Publication

CEC 2015

Authors

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