Quantum Inspired Evolutionary Algorithms with improved rotation gates for real-coded synthetic and real world optimization problems

Ivan Jordanov, Joe Wright

Research output: Contribution to journalArticlepeer-review

193 Downloads (Pure)

Abstract

We investigate two modified Quantum Evolutionary methods for solving real value problems. The Quantum Inspired Evolutionary Algorithms (QIEA) were originally used for solving binary encoded problems and their signature features follow superposition of multiple states on a quantum bit and a rotation gate. In order to apply this paradigm to real value problems, we propose two quantum methods Half Significant Bit (HSB) and Stepwise Real QEA (SRQEA), developed using binary and real encoding respectively, while keeping close to the original quantum computing metaphor. We evaluate our approaches against sets of multimodal mathematical test functions and real world problems, using five performance metrics and include comparisons to published results. We report the issues encountered while implementing some of the published real QIEA techniques. Our methods focus on introducing and implementing new rotation gate operators used for evolution, including a novel mechanism for preventing premature convergence in the binary algorithm. The applied performance metrics show superior results for our quantum methods on most of the test problems (especially for the more complex and challenging ones), demonstrating faster convergence and accuracy.
Original languageEnglish
Pages (from-to)203-223
Number of pages21
JournalIntegrated Computer-Aided Engineering
Volume24
Issue number3
DOIs
Publication statusPublished - 1 Sep 2017

Keywords

  • quantum evolutionary methods
  • real value problems
  • multimodal functions
  • global optimization
  • performance metrics
  • estimation of distribution algorithms
  • simulation evaluation

Fingerprint

Dive into the research topics of 'Quantum Inspired Evolutionary Algorithms with improved rotation gates for real-coded synthetic and real world optimization problems'. Together they form a unique fingerprint.

Cite this