Advanced Quantum Inspired Evolutionary Algorithm for Multivariate Optimization

In real life, there are many applications where we need to take care of multiple parameters to get the optimized result. Similarly, many scientific and engineering problems require optimization of various parameters to get desired results. Many algorithms work well with a few variables to get optimized results, but on increasing the number of variables, they do not perform well. In this paper, we proposed an advanced quantum-inspired evolutionary algorithm (A-QEAM) to solve optimization problems where the tuning of multiple parameters or variables is required. A-QEAM is characterized by the principle of quantum computing such as superposition and qubit. This algorithm uses a qubit in place of the classical bit. The proposed algorithm is tested on mathematical functions consisting of 2 variables, 10 variables, 30 variables, and 50 variables. The result shows that the proposed algorithm performs well even on increasing the number of variables.