A versatile quantum-inspired evolutionary algorithm

Abstract

Abstract-This study points out some weaknesses of existing Quantum-Inspired Evolutionary Algorithms (QEA) and explains in particular how hitchhiking phenomenons can slow down the discovery of optimal solutions and encourage premature convergence. A new algorithm, called Versatile Quantuminspired Evolutionary Algorithm (vQEA), is proposed. With vQEA, the attractors moving the population through the search space are replaced at every generation without considering their fitness. The new algorithm is much more reactive. It always adapts the search toward the last promising solution found thus leading to a smoother and more efficient exploration.

On the Use of Quantum-inspired Optimization Techniques for Training Spiking Neural Networks: A New Method Proposed

“…In order to provide an efficient and accurate solution to the simultaneous optimization task of features and parameters of an ESNN, for its interesting properties in terms of solution quality and convergence speed, a Versatile Quantum-inspired Evolutionary Algorithm (vQEA) [9] has been used in [10]. The method evolves in parallel a number of independent probability vectors, which interact at certain time intervals with each other, forming a multi-model Estimation of Distribution Algorithm (EDA) [11].…”

Quantum Henry gas solubility optimization algorithm for global optimization

“…After converting a test suite reduction problem to the standard optimization problem, a novel scheme to evolutionary algorithm using quantum bit in comparison with its original bit is presented in a paper titled as Reduced Quantum Genetic Algorithm (RQGA) [49]. To tackle some drawbacks of the quantum-inspired evolutionary algorithms and describing how the hitchhiking problem can slow down to find optimal solution and trapped in premature convergence, a Versatile Quantum-inspired Evolutionary Algorithm (VQEA) is introduced in Platel et.al [50]. In this algorithm, the attractor agents change their positions among the population via the search space and relocated at every generation without considering their fitness.…”

Phishing web image segmentation based on improving spectral clustering

“…Spectral clustering based on graph theory can cluster data sets with any shape and any size which has become one of the most popular modern clustering algorithms [3] , but the traditional spectral clustering algorithms could not converge to the global optimal solution. Quantum-inspired Evolutionary Algorithm (QEA) is a kind of evolutionary algorithm based on the principle of quantum computation [4] . In order to search global optimal solution of optimization problem, it uses qubit instead of binary chromosomes of GA, quantum rotation gate instead of choose and crossover operator [5] .…”