A Novel Evolutionary Algorithm with Neighborhood Search for Project Portfolios Optimization Problem

Abstract

This paper proposes a quantum-inspired evolutionary algorithm with neighborhood search (called QEANS) to solve the project portfolios optimization problem with limited multiple resources and bounded risks for each project portfolio. The decision concerns how to find an optimal or best assignment of projects to a set of project portfolios that maximizes the total profit. The studied problem is formulated by a 0-1 linear programming model, and a quantum-inspired evolutionary algorithm with neighborhood search is proposed to solve it. In specific, each problem solution is encoded by a Q-bits matrix, which is updated by quantum-rotation gate. In addition, crossover and mutation operators are integrated so as to increase the population diversity. Furthermore, an effective repairing procedure is proposed for dealing with the generated infeasible solutions. To prevent the local optimum, a specific neighborhood search procedure is also proposed. Randomly generated instances are used to test and justify the effectiveness of the proposed QEANS. The obtained results indicate that the proposed QEANS is effective.