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

Abstract

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. (C) 2014 Elsevier Inc. All rights 
reserved.