Maria Dolores Gil Montoya

A Pareto-based multi-objective evolutionary algorithm for automatic rule generation in network intrusion detection systems

Attacks against computer systems are becoming more complex, making it necessary to continually improve the security systems, such as intrusion detection systems which provide security for computer systems by distinguishing between hostile and non-hostile activity. Intrusion detection systems are usually classified into two main categories according to whether they are based on misuse (signature-based) detection or on anomaly detection. With the aim of minimizing the number of wrong decisions, a new Pareto-based multi-objective evolutionary algorithm is used to optimize the automatic rule generation of a signature-based intrusion detection system (IDS). This optimizer, included within a network IDS, has been evaluated using a benchmark dataset and real traffic of a Spanish university. The results obtained in this real application show the advantages of using this multi-objective approach.

A hybrid method for solving multi-objective global optimization problems

Real optimization problems often involve not one, but multiple objectives, usually in conflict. In single-objective optimization there exists a global optimum, while in the multi-objective case no optimal solution is clearly defined but rather a set of optimums, which constitute the so called Pareto-optimal front. Thus, the goal of multi-objective strategies is to generate a set of non-dominated solutions as an approximation to this front. However, most problems of this kind cannot be solved exactly because they have very large and highly complex search spaces. The objective of this work is to compare the performance of a new hybrid method here proposed, with several well-known multi-objective evolutionary algorithms (MOEA). The main attraction of these methods is the integration of selection and diversity maintenance. Since it is very difficult to describe exactly what a good approximation is in terms of a number of criteria, the performance is quantified with adequate metrics that evaluate the proximity to the global Pareto-front. In addition, this work is also one of the few empirical studies that solves three-objective optimization problems using the concept of global Pareto-optimality.

The load unbalancing problem for region growing image segmentation algorithms

This paper discusses and evaluates parallel implementations of a segmentation algorithm based on the Split-and-Merge approach. The solution has been conceived for a multiprocessor using the Single Program Multiple Data (SPMD) programming model and executions have been carried out on a Cray-T3E system. Our main goal is to describe our experiences in solving the region growing problem, which is representative of a class of non-uniform problems, characterized by a behavior that is data dependent. Since this problem exhibits unpredictable load fluctuations, it requires the use of load-balancing schemes to achieve efficient parallel solutions. We also propose and analyze several strategies for the selection of region identifiers and their influence on execution time and load distribution.

A Mixed Heuristic for Circuit Partitioning

As general-purpose parallel computers are increasingly being used to speed up different VLSI applications, the development of parallel algorithms for circuit testing, logic minimization and simulation, HDL-based synthesis, etc. is currently a field of increasing research activity. This paper describes a circuit partitioning algorithm which mixes Simulated Annealing (SA) and Tabu Search (TS) heuristics. The goal of such an algorithm is to obtain a balanced distribution of the target circuit among the processors of the multicomputer allowing a parallel CAD application for Test Pattern Generation to provide good efficiency. The results obtained indicate that the proposed algorithm outperforms both a pure Simulated Annealing and a Tabu Search. Moreover, the usefulness of the algorithm in providing a balanced workload distribution is demonstrated by the efficiency results obtained by a topological partitioning parallel test-pattern generator in which the proposed algorithm has been included. An extented algorithm that works with general graphs to compare our approach with other state of the art algorithms has been also included.

Annealing-based heuristics and genetic algorithms for circuit partitioning in parallel test generation

Abstract In this paper 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-heuristics, is also proposed and compared with pure tabu search and simulated annealing algorithms, and also with a genetic algorithm. The solutions obtained are compared and evaluated by including the hybrid partitioning algorithm in a parallel test generator which is used to determine the test patterns for the circuits of the frequently used ISCAS benchmark set.

A parallel multi-objective algorithm for two-dimensional bin packing with rotations and load balancing

Abstract Bin packing problems are NP-hard combinatorial optimization problems of fundamental importance in several fields, including computer science, engineering, economics, management, manufacturing, transportation, and logistics. In particular, the non-guillotine version of the single-objective two-dimensional bin packing problem with rotations is a highly complex scheduling problem that consists in packing a set of items into the minimum number of bins, where items can be rotated 90° and are characterized by having different heights and widths. Recently, some authors have proposed multi-objective formulations that also consider additional objectives, such as the balancing the bin load in order to increase its stability. The load imbalance minimization, which depends on the distribution of the items packed in them, is a critical point in many real applications. This paper analyzes how to solve two-dimensional bin packing problems with rotations and load balancing using parallel and multi-objective memetic algorithms that apply a set of search operators specifically designed to solve this problem. Results obtained using a set of test problems show the good performance of parallel and multi-objective memetic algorithms in comparison with other methods found in the literature.

Parallel VLSI test in a shared‐memory multiprocessor

SUMMARY This paper presents three parallel procedures implemented in a shared-memory multiprocessor to generate the patterns that allow the testing of digital circuits. The implementation of these procedures in a multiprocessor uses the system memory better than in a distributed-memory multicomputer, since it is not necessary to store the circuit structure in the local memory of each processor, besides other common structures. The parallel test generation procedures are based on a new sequential algorithm which mixes both the Boolean difference and digital spectral techniques. It is thus different from other methods proposed that deal with the parallelization of test generation algorithms that carry out an implicit enumeration of the input pattern space. The first procedure distributes the set of faults using a backtracking procedure starting from a primary output and allocating a similar number of lines to each processor. The second procedure distributes the set of faults among the processors taking into account the distance from each line to its nearest primary output; it then applies the algorithm to generate the test pattern with some modifications. The third procedure uses a circuit partitioning procedure which allows similar sized parts of the circuit to be assigned to each processor while communications between processors are minimized. The experimental results obtained when the procedures are applied to the usual benchmark circuits (the ISCAS set) show figures for speedup better than in a multicomputer, although fewer processors are used. Copyright

Ant colony optimization for water distribution network design: a comparative study

The optimal design of looped water distribution networks is a major environmental and economic problem with applications in urban, industrial and irrigation water supply. Traditionally, this complex problem has been solved by applying single-objective constrained formulations, where the goal is to minimize the network investment cost subject to pressure constraints. In order to solve this highly complex optimization problem some authors have therefore proposed using heuristic techniques for their solution. Ant Colony Optimization (ACO) is a metaheuristic that uses strategies inspired by real ants to solve optimization problems. This paper presents and evaluates the performance of a new ACO implementation specially designed to solve this problem, which results in two benchmark networks outperform those obtained by genetic algorithms and scatter search.

A parallel evolutionary algorithm for circuit partitioning

As general-purpose parallel computers are increasingly being used to speed up different VLSI applications, the development of parallel algorithms for circuit testing, logic minimization and simulation, HDL-based synthesis, etc. is currently a field of increasing research activity. In some of these applications the circuit partitioning problem occurs. That implies dividing a circuit into non-overlapping subcircuits while minimizing the number of cuts after the division and balancing the load associated to each one. Very effective heuristic algorithms have been developed in order to solve this problem, but it is unknown how good the partitions are since the problem is NP-complete. In these cases the use of parallel processing can be very useful. This paper describes a parallel evolutionary algorithm for circuit partitioning, where parallelism improves the solutions found by the corresponding sequential algorithm, which indeed is quite effective compared with other previously proposed procedures.

Analysis of OpenMP and MPI implementations of meta-heuristics for vehicle routing problems

Graphical abstractDisplay Omitted HighlightsWe parallelize a sequential meta-heuristic based on simulated annealing.The island and master-worker models, and OpenMP and MPI implementations are used.Vehicle routing problems with time windows are used as benchmarks.The performance of multithreading and multi-core processing is analyzed.The different implementation alternatives are statistically compared by using ANOVA. The parallelization of heuristic methods allows the researchers both to explore the solution space more extensively and to accelerate the search process. Nowadays, there is an increasing interest on developing parallel algorithms using standard software components that take advantage of modern microprocessors including several processing cores with local and shared cache memories. The aim of this paper is to show it is possible to parallelize algorithms included in computational software using standard software libraries in low-cost multi-core systems, instead of using expensive high-performance systems or supercomputers. In particular, it is analyzed the benefits provided by master-worker and island parallel models, implemented with MPI and OpenMP software libraries, to parallelize population-based meta-heuristics. The capacitated vehicle routing problem with hard time windows (VRPTW) has been used to evaluate the performance of these parallel strategies. The empirical results for a set of Solomons benchmarks show that the parallel approaches executed on a multi-core processor produce better solutions than the sequential algorithm with respect to both the quality of the solutions obtained and the runtime required to get them. Both MPI and OpenMP parallel implementations are able to obtain better or at least equal solutions (in terms of distance traveled) than the best known ones for the considered benchmark instances.