A Study of Memetic Search with Multi-parent Combination for UBQP

We present a multi-parent hybrid genetic–tabu algorithm (denoted by GTA) for the Unconstrained Binary Quadratic Programming (UBQP) problem, by incorporating tabu search into the framework of genetic algorithm. In this paper, we propose a new multi-parent combination operator for generating offspring solutions. A pool updating strategy based on a quality-and-distance criterion is used to manage the population. Experimental comparisons with leading methods for the UBQP problem on 25 large public instances demonstrate the efficacy of our proposed algorithm in terms of both solution quality and computational efficiency

Elementary landscape decomposition of the 0-1 unconstrained quadratic optimization

Journal of Heuristics, 19(4), pp.711-728Landscapes’ theory provides a formal framework in which combinatorial optimization problems can be theoretically characterized as a sum of an especial kind of landscape called elementary landscape. The elementary landscape decomposition of a combinatorial optimization problem is a useful tool for understanding the problem. Such decomposition provides an additional knowledge on the problem that can be exploited to explain the behavior of some existing algorithms when they are applied to the problem or to create new search methods for the problem. In this paper we analyze the 0-1 Unconstrained Quadratic Optimization from the point of view of landscapes’ theory. We prove that the problem can be written as the sum of two elementary components and we give the exact expressions for these components. We use the landscape decomposition to compute autocorrelation measures of the problem, and show some practical applications of the decomposition.Spanish Ministry of Sci- ence and Innovation and FEDER under contract TIN2008-06491-C04-01 (the M∗ project). Andalusian Government under contract P07-TIC-03044 (DIRICOM project)

The parallel row ordering problem (PROP)

Given a partitioning of the set of facilities into two subsets, the PROP asks to arrange one subset of facilities on one line and the other subset of facilities on a parallel line with the goal of minimizing the weighted sum of the distances between all pairs of facilities. The distance between two facilities (with given lengths) is taken as the x-distance between their centers. Each distance is weighted by the material transportation cost per distance unit between the corresponding facilities. For solving the PROP, I have developed a memetic algorithm (MA) utilizing a fast local search procedure which is based on repeatedly applying insertion moves to the current solution. The source code (in the C++ programming language, Borland version) of MA can be found in MA_code.zip.The algorithm was tested on the PROP instances with up to 500 facilities. This set of instances can be found in the file PROP_instances.zip. Instance is provided in the following format: - the total number of facilities; - the number of facilities in the first row; - the lengths of facilities; - the (symmetric) flow matrix (its upper triangle is used in the objective function).THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV