Engineering Commons^™

Incorporating Memory And Learning Mechanisms Into Meta-Raps, Arif Arin

Engineering Management & Systems Engineering Theses & Dissertations

Due to the rapid increase of dimensions and complexity of real life problems, it has become more difficult to find optimal solutions using only exact mathematical methods. The need to find near-optimal solutions in an acceptable amount of time is a challenge when developing more sophisticated approaches. A proper answer to this challenge can be through the implementation of metaheuristic approaches. However, a more powerful answer might be reached by incorporating intelligence into metaheuristics.

Meta-RaPS (Metaheuristic for Randomized Priority Search) is a metaheuristic that creates high quality solutions for discrete optimization problems. It is proposed that incorporating memory and learning …

A Comparison Of Genetic Algorithm Parametrization On Synthetic Optimization Problems, Mehmet Eravsar

Theses and Dissertations

Meta-heuristics have been deployed to solve many hard combinatorial and optimization problems. Parameterization of meta-heuristics is an important challenging aspect of meta-heuristic use since many of the features of these algorithms cannot be explained theoretically. Experiences with Genetic Algorithms (GA) applied to Multidimensional Knapsack Problems (MKP) have shown that this class of algorithm is very sensitive to parameterization. Many studies use standard test problems, which provide a firm basis for study comparisons but ignore the effect of problem correlation structure. This thesis applies GA to MKP. A new random repair operator, which projects infeasible solutions into feasible region, is proposed. …

Solving The Multidimensional Multiple Knapsack Problem With Packing Constraints Using Tabu Search, Jonathan M. Romaine

Theses and Dissertations

This paper presents a methodology for solving military aircraft load-scheduling problems modeled as a multidimensional multiple knapsack problem, Because of the computational time associated with applying conventional algorithms to this type of problem, we employ tabu search to determine how much cargo a heterogeneous group of aircraft can carry. This study extends the previous work of Chocolaad in two areas. First, we modify Chocolaad's algorithms to solve the multiple (rather than the single) knapsack problem under the constraints he defined for the Airlift Loading Problem. Second, we drop his assumption of a homogeneous group of aircraft. We validate our model …