Multi-objective hybrid genetic algorithm for bicriteria network design problem

This paper considers the Bicriteria Network Design Problem (bNDP) with the two conflicting objectives of minimizing cost and maximizing flow. Network design problems where even one flow measure be maximized, are often NP-hard problems. But, in real-life applications, it is often the case that the network to be built is required to optimize multi-criteria simultaneously. Thus the calculation of the multi-criteria network design problems is a difficult task. This paper proposes a new Multiobjective Hybrid Genetic Algorithm (mo-hGA) approach, and shows how the performance of multiobjective genetic algorithm (moGA) can be improved by hybridization with Fuzzy Logic Control (FLC) and Local Search (LS). The main positive effect of the hybridization is the improvement in the convergence speed to the Pareto front.