Sentence examples for mixed integer search from inspiring English sources

ACOmi extends ant colony optimization meta-heuristic [ 28] to handle mixed integer search domains.

Finally, eSS is an enhanced version of the scatter search for mixed integer search domain.

A novel multi objective optimization algorithm capable of handling a multitude of objectives in mixed integer nonlinear search space and a novel decision making methodology are integrated with a sequential modular process simulator to optimize processes with a multitude of objectives rigorously and efficiently.

In general, there are three different approaches to solve such a mixed integer programming problem: The exhaustive search checks all possible subcarrier assignments[16, 18], relaxing of constraints transforms integers into real valued variables[11, 14] and dual decomposition searches for the optimal CA in the dual domain[16, 17].

In this work, we use three different global stochastic methods: mixed-integer tabu search (MITS) [ 24], mixed-integer ant colony optimization (ACOmi) [ 25] and the enhanced scatter search eSS described in [ 23].

We propose one mixed integer linear programming model, nine local search procedures and five metaheuristic algorithms.

In particular, we will see how, in a portfolio setting, it can be advantageous to use a more "risk-seeking" strategy with a high variance in run time, such as a randomized depth-first search approach in mixed integer programming versus the more traditional best-bound approach.

A bi-level nonlinear mixed integer programming model is constructed and a tabu search method with different local search strategies and neighborhood evaluation methods is then developed for tackling the circulator service network design problem.

The MLACO was tested on 20 CFTPPs and solutions were compared to those obtained from other approaches including a mixed integer programming (MIP) solver, a parameter iterative local search (ParamILS) method, and an exhaustive ACO parameter search method.

A mixed integer formulation and an adaptive large neighborhood search heuristic are proposed.

In both methods approximate mixed integer programming models are solved at each search iteration.