TABU SEARCH FOR NONLINEAR AND PARAMETRIC OPTIMIZATION (WITH LINKS TO GENETIC ALGORITHMS)

In spite of the widespread importance of nonlinear and parametric opti mization, many standard solution methods allow a large gap between loc al optimality and global optimality, inviting consideration of metaheu ristics capable of reducing such gaps. We identify ways to apply the t abu search metaheuristic to nonlinear optimization problems from both continuous and discrete settings. The step beyond strictly combinatori al settings enlarges the domain of problems to which tabu search is ty pically applied. We show how tabu search can be coupled with direction al search and scatter search approaches to solve such problems. In add ition, we generalize standard weighted combinations (as employed in sc atter search) to include structured weighted combinations capable of s atisfying specified feasibility conditions (e.g., mapping weighted com binations of scheduling, partitioning and covering solutions into new solutions of the same type). The outcome suggests ways to exploit pote ntial links between scatter search and genetic algorithms, and also pr ovides a basis for integrating genetic algorithms with tabu search.