Metaheuristics are methods that guide other procedures (heuristic or truncated exact methods) to enable them to overcome the trap of local optimality for complex optimization problems. Four metaheuristics have primarily been applied with some success to simulation optimization: simulated annealing, genetic algorithms, tabu search and scatter search. In these, tabu search and scatter search have confirmed to be by far the most efficient, and are at the essence of the simulation optimization software that is now most extensively used. Tabu Search (TS) is prominent by introducing adaptive memory into metaheuristic search, together with associated strategies for using such memory, providing it to penetrate complexities that often confound other approaches. Scatter Search is an progressive (population-based) algorithm that architect solutions by connecting others. Scatter search is designed to operate on a set of points, called reference points, that constitute good solutions obtained from previous solution efforts.
- Global & Black Box Optimization
- Discrete Optimization via Simulation
- Stochastic Approximation Methods
- Sample Average Approximation
- Stochastic Gradient Estimation
- Gradient Based Search Method
- Response Surface Methodology