A Vector Tabu Search Algorithm With Enhanced Searching Ability for Pareto Solutions and Its Application to Multiobjective Optimizations

A vector tabu search algorithm encapsulating a new updating mechanism for current states and a directed search phase is proposed to enhance its searching ability for Pareto-optimal solutions. The new updating mechanism considers quantitatively both the number of improved objectives and the amount of improvements in a specified objective, of multiobjective design problems. The directed search phase uses some desired directions, a priori knowledge about the objective space, as the moving direction to efficiently find improved solutions without any gradient computation procedure. The numerical results on both high-and low-frequency inverse problems are reported to demonstrate the pros and cons of the proposed algorithm. It is observed that the proposed vector tabu search method outperforms its ancestors in both the convergence performance and the solution quality.