Quantum Immune Clone for Solving Constrained Multi-Objective Optimization


This paper proposes a quantum immune clone algorithm to solve the constrained multi-objective optimization problem. Firstly, constraints deviation value is added to objective function value to form a new objective function value, which translates the constrained multi-objective optimization problem into an unconstrained multi-objective optimization problem. Secondly, it does not only retain the feasible non-dominated solutions, but also utilizes the non-feasible solutions which have small constraint deviation value and objective function value. The appearing of the non-feasible solutions expands the search scope and makes it easy to evolve solutions near the Pareto front. Then, a quantum rotating gate is designed to accelerate the computational speed. At last, crossover and mutation are used to obtain better individuals. Compared with the state-of-art algorithm, simulation results show that the proposed algorithm has a better improvement on GD distance and on the diversity.