Affiliations: School of Computing, University of Portsmouth, Lion Terrace, Portsmouth, UK
Correspondence:
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Corresponding author: Ivan Jordanov, School of Computing, University of Portsmouth, Lion Terrace, Portsmouth, PO1 3HE, UK. E-mail: [email protected].
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.
Keywords: Quantum evolutionary methods, estimation of distribution algorithms, performance metrics, global optimization, multimodal functions, real value problems
