Isolation strategy for multi-objective quantum-inspired evolutionary algorithm

Abstract

In general, as the size of the problem or the number of objectives to be optimised increases in multi-objective optimisation problems, the distribution range of the Pareto optimal solution set in the search space expands. Expanding the search space makes it difficult for the variable information of other solutions to contribute to generating new solutions. This study proposes a multi-objective quantum-inspired evolutionary algorithm based on isolation strategy (MQEA/I) and a novel lookup table of rotation angle for updating the probability amplitude. In MQEA/I, each individual basically evolves in isolation using the personal best solution and can automatically shift from global search to local search. MQEA/I has only one parameter, the rotation angle, except for the population size and the termination condition. Our experimental results using multi-objective 0-1 knapsack problems show that MQEA/I obtained a more accurate non-dominated solution set than NSGA-II and SPEA2 in problems with many objectives and items.