The hybridization of population-based meta-heuristics and local search strategies is an effective algorithmic proposal for solving complex continuous optimization problems. Such hybridization becomes much more effective when the local search heuristics are applied in the most promising areas of the solution space. This paper presents a hybrid method based on Clustering Search (CS) to solve cont...

The work presented in this paper could be thought of as a case based hyper-heuristic approach for examination timetabling problems. A hyper-heuristic can be taken to be an automated approach to choose heuristics. Heuristics and meta-heuristics are employed in this capacity in [1] and [2]. In this paper the case-based paradigm is explored as a heuristic selector for examination timetabling probl...

Vehicle routing problems have been extensively studied using a variety of search methods and meta-heuristics. However, most search methods are carried out using only a single improvement heuristic. Here, we report on a study using four improvement heuristics embedded in a simple steepest descent search engine. We conclude that reasonable results can be produced quickly by this simple method. A ...

This paper proposes a three-stage matheuristic solution strategy for the capacitated multi-commodity fixed-cost network design problem with design-balance constraints. The proposed matheuristic combines exact and neighbourhood-based methods. The proposed tabu-search and path-relinking meta-heuristics cooperate to generate as many feasible solutions as possible. The two meta-heuristics incorpora...

In this communication Simulated Annealing and Genetic Algorithms, are applied to the graph partitioning problem. These techniques mimic processes in statistical mechanics and biology, respectively, and are the most popular meta-heuristics or general-purpose optimization strategies. A hybrid algorithm for circuit partitioning, which uses tabu search to improve the simulated annealing meta-heuris...

The paradigm of distributed computation in heterogeneous resources, grid computing, has given rise to a large amount of research on resource scheduling. This paper presents a Meta-Scheduler for grid computing that does not need any given information about tasks length or tasks arrival time unlike traditional dynamic heuristics. Our Meta-Scheduler is of multi-criteria type, because it solves two...

Large-scale unconditional and conditional vertex p-centre problems are solved using two meta-heuristics. One is based on a three-stage approach whereas the other relies on a guided multi-start principle. Both methods incorporate Variable Neighbourhood Search, exact method, and aggregation techniques. The methods are assessed on the TSP dataset which consist of up to 71,009 demand points with p ...

Meta-heuristics are an effective paradigm for solving large-scale combinatorial optimization problems. However, the development of such algorithms is often very time-consuming as they have to be designed for a concrete problem class with little or no opportunity for reuse. In this paper, we present a generic software framework that is able to handle different types of combinatorial optimization...

Abstract Optimization problems are frequent in several fields, such as the different branches of Engineering. In some cases, objective function exposes mathematically exploitable properties to find exact solutions. However, when it is not case, heuristics appreciated. This situation occurs involves numerical simulations and sophisticated models reality. Then, population-based meta-heuristics, g...

Recently, a number of papers have shown that for many domains, using multiple heuristics in independent searches performs better than combining them into a single heuristic. Furthermore, using a large number of “weak” heuristics could potentially eliminate the need for the careful design of a few. The standard approach to distribute computation in these multi-heuristic searches is to rotate thr...