An algorithm based on particle swarm optimization for multiobjective bilevel linear problems

- Improved MOPSO for multiobjective (at the upper level) bilevel linear problems.
- A hybrid scheme for the selection of the global best particles is proposed.
- An adaptive mutation is also introduced.
- The incorporation of these mechanisms led to significantly better results.

This paper presents an improved multiple objective particle swarm optimization (MOPSO) algorithm to solve bilevel linear programming problems with multiple objective functions at the upper level. The algorithm aims to produce a good approximation of the entire Pareto front of the problem. We have previously designed a MOPSO algorithm for the same class of problems, in which several techniques for the global best selection were tested, including a new one. The algorithm revealed a good convergence towards the Pareto front but the diversity of the solutions was a drawback. The algorithm we propose herein uses a hybrid strategy for the global best selection and an adaptive mutation operator. The incorporation of these mechanisms led to an improved algorithm, which also showed better overall performance than considering alternative options usually employed in MOPSO algorithms. The algorithm and computational results are presented.