A Weight-coded Evolutionary Algorithm for the Multidimensional Knapsack Problem

A revised weight-coded evolutionary algorithm (RWCEA) is proposed for solving multidimensional knapsack problems. This RWCEA uses a new decoding method and incorporates a heuristic method in initialization. Computational results show that the RWCEA performs better than a weight-coded evolutionary algorithm proposed by Raidl (1999) and to some existing benchmarks, it can yield better results than the ones reported in the OR-library.Comment: Submitted to Applied Mathematics and Computation on April 8, 201

Structural Evolution: a genetic algorithm method to generate structurally optimal Delaunay triangulated space frames for dynamic loads

An important principle in the architectural design process is the quest for the optimum solution, a quest which is in this study structurally motivated and necessarily computationally oriented given its high complexity in nature. The present research project suggests an evolutionary algorithm that draws its power from the literal interpretation of the natural system's reproductive process at a microscopic scale with the scope of generating optimal Delaunay triangulated space frames for dynamic loads. The algorithm repositions a firm number of nodes within a space envelope, by establishing Delaunay tetrahedra and consequently creating adaptable optimised space frame topologies. The arbitrarily generated tetrahedralised structure is compared against a canonical designed one, whilst several experiments are conducted in order to investigate whether -and to what degree- the genetic algorithm method is appropriate for searching discontinuous and difficult solution spaces or not. The results of this comparison indicate that the method proposed has advantageous properties while being capable of generating an optimum structure that exceeds statically the performance of an engineered tetrahedralised space frame

Using ACO metaheuristic for MWT problem

Globally optimal triangulations are difficult to be found by deterministic methods as, for most type of criteria, no polynomial algorithm is known. In this work, we consider the Minimum Weight Triangulation (MWT) problem of a given set of n points in the plane. This paper shows how the Ant Colony Optimization (ACO) metaheuristic can be used to find high quality triangulations. For the experimental study we have created a set of instances for MWT problem since no reference to benchmarks for these problems were found in the literature. Through the experimental evaluation, we assess the applicability of the ACO metaheuristic for MWT problem

Simulated Annealing Algorithm for the Linear Ordering Problem: The Case of Tanzania Input Output Tables

Linear Ordering is a problem of ordering the rows and columns of a matrix such that the sum of the upper triangle values is as large as possible. The problem has many applications including aggregation of individual preferences, weighted ancestry relationships and triangulation of input-output tables in economics. As a result, many researchers have been working on the problem which is known to be NP-hard. Consequently, heuristic algorithms have been developed and implemented on benchmark data or specific real-world applications. Simulated Annealing has seldom been used for this problem. Furthermore, only one attempt has been done on the Tanzanian input output table data. This article presents a Simulated Annealing approach to the problem and compares results with previous work on the same data using Great Deluge algorithm. Three cooling schedules are compared, namely linear, geometric and Lundy & Mees. The results show that Simulated Annealing and Great Deluge provide similar results including execution time and final solution quality. It is concluded that Simulated Annealing is a good algorithm for the Linear Ordering problem given a careful selection of required parameters. Keywords: Combinatorial Optimization; Linear Ordering Problem; Simulated Annealing; Triangulation; Input Output table

Globally optimal triangulations of minimum weight using Ant Colony Optimization metaheuristic

Globally optimal triangulations are difficult to be found by deterministic methods as, for most type of criteria, no polynomial algorithm is known. In this work, we consider the Minimum Weight Triangulation (MWT) problem of a given set of n points in the plane. Our aim is to show how the Ant Colony Optimization (ACO) metaheuristic can be used to search for globally optimal triangulations of minimum weight. We present an experimental study for a set of instances for MWT problem. We create these instances since no reference to benchmarks for this problem were found in the literature. We assess through the experimental evaluation the applicability of the ACO metaheuristic for MWT problem.Facultad de Informátic

Timely Near-Optimal Path Generation for an Unmanned Aerial System in a Highly Constrained Environment

A current challenge in path planning is the ability to efficiently calculate a near-optimum path solution through a highly-constrained environment in near-real time. In addition, computing performance on a small unmanned aerial vehicle is typically limited due to size and weight restrictions. The proposed method determines a solution quickly by first mapping a highly constrained three-dimensional environment to a two-dimensional weighted node surface in which the weighting accounts for both the terrain gradient and the vehicle\u27s performance. The 2D surface is then discretized into triangles which are sized based upon the vehicle maneuverability and terrain gradient. The shortest feasible path between the nodes of the two-dimensional triangulated surface is determined using an A* algorithm. An optimal path is then chosen through the unconstrained corridor to yield a quick near-optimal path solution in three-dimensional space. This technique requires prior knowledge of the terrain map and vehicle performance. The cost to traverse each segment of the map is independent of the starting position on the map and can be pre-calculated once the goal position is known. The proposed method allows for a rapid path solution from any start position to a goal position while satisfying all constraints. It was shown that employing the methodology herein resulted in near-optimal solutions in less than a couple seconds for the scenarios tested. The future work section proposes methods for improving the algorithms efficiency even further

Multicriteria inventory classification using a genetic algorithm

Cataloged from PDF version of article.One of the application areas of genetic algorithms is parameter optimization. This paper addresses the problem of optimizing a set of parameters that represent the weights of criteria, where the sum of all weights is 1. A chromosome represents the values of the weights, possibly along with some cut-off points. A new crossover operation, called continuous uniform crossover, is proposed, such that it produces valid chromosomes given that the parent chromosomes are valid. The new crossover technique is applied to the problem of multicriteria inventory classification. The results are compared with the classical inventory classification technique using the Analytical Hierarchy Process. @ 1998 Elsevier Science B.V

Finding Wombling Boundaries in LHC Data with Voronoi and Delaunay Tessellations

We address the problem of finding a wombling boundary in point data generated by a general Poisson point process, a specific example of which is an LHC event sample distributed in the phase space of a final state signature, with the wombling boundary created by some new physics. We discuss the use of Voronoi and Delaunay tessellations of the point data for estimating the local gradients and investigate methods for sharpening the boundaries by reducing the statistical noise. The outcome from traditional wombling algorithms is a set of boundary cell candidates with relatively large gradients, whose spatial properties must then be scrutinized in order to construct the boundary and evaluate its significance. Here we propose an alternative approach where we simultaneously form and evaluate the significance of all possible boundaries in terms of the total gradient flux. We illustrate our method with several toy examples of both straight and curved boundaries with varying amounts of signal present in the data.Comment: 54 pages. New figure 13 and appendix A added. Conclusions unchanged. Matches published versio

Solving the planar p-median problem by variable neighborhood and concentric searches

Two new approaches for the solution of the p-median problem in the plane are proposed. One is a Variable Neighborhood Search (VNS) and the other one is a concentric search. Both approaches are enhanced by a front-end procedure for finding good starting solutions and a decomposition heuristic acting as a post optimization procedure. Computational results confirm the effectiveness of the proposed algorithms