Antonio López Márquez

A hybrid meta-heuristic for multi-objective vehicle routing problems with time windows

The Capacitated Vehicle Routing Problem with Time Windows is an important combinatorial optimization problem consisting in the determination of the set of routes of minimum distance to deliver goods, using a fleet of identical vehicles with restricted capacity, so that vehicles must visit customers within a time frame. A large number of algorithms have been proposed to solve single-objective formulations of this problem, including meta-heuristic approaches, which provide high quality solutions in reasonable runtimes. Nevertheless, in recent years some authors have analyzed multi-objective variants that consider additional objectives to the distance travelled. This paper considers not only the minimum distance required to deliver goods, but also the workload imbalance in terms of the distances travelled by the used vehicles and their loads. Thus, MMOEASA, a Pareto-based hybrid algorithm that combines evolutionary computation and simulated annealing, is here proposed and analyzed for solving these multi-objective formulations of the VRPTW. The results obtained when solving a subset of Solomons benchmark problems show the good performance of this hybrid approach.

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.

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.

Parallelism on multicore processors using Parallel.FX

The Parallel.FX Task Parallel Library is the latest tool developed for multicore parallelism optimization using the .NET technology. It is a managed concurrency library that provides optimized managed code for multicore processors using a new thread pool that withstands cancellation, waiting and pool isolation, among many other features. The Task Parallel Library also uses dynamic work stealing techniques for superior scalability. This paper analyzes the performance improvement of using the Task Parallel Library of Parallel.FX when applying a Multi-Objective Evolutionary Algorithm to solve a timetabling problem. For comparative purposes, this algorithm has also been parallelized using threads. The results obtained show that both alternatives allow a reduction in the runtime necessary to solve this problem. However, parallelizing the code using the Task Parallel Library of Parallel.FX has the advantage of being easier and the code size is much smaller than directly programming threads.

A multi-objective evolutionary algorithm for network intrusion detection systems

Attacks against computer systems are becoming more complex, making it necessary to develop new security systems continually, such as Intrusion Detection Systems (IDS) which provide security for computer systems by distinguishing between hostile and non-hostile activity. With the aim of minimizing the number of wrong decisions of a misuse (signature-based) IDS, an optimization strategy for automatic rule generation is presented. This optimizer is a Pareto-based multi-objective evolutionary algorithm included within a network IDS, which has been evaluated using a benchmark dataset. The results obtained show the advantages of using this multi-objective approach.

A New Memetic Algorithm for the Two-Dimensional Bin-Packing Problem with Rotations

The two-dimensional bin-packing problem (2D-BPP) with rotations is an important optimization problem which has a large number of practical applications. It consists of the non-overlapping placement of a set of rectangular pieces in the lowest number of bins of a homogenous size, with the edges of these pieces always parallel to the sides of bins, and with free 90 degrees rotation. A large number of methods have been proposed to solve this problem, including heuristic and meta-heuristic approaches. This paper presents a new memetic algorithm to solve the 2D-BPP that incorporates some operators specially designed for this problem. The performance of this memetic algorithm is compared with two other heuristics previously proposed by other authors in ten classes of frequently used benchmark problems. It is observed that, in some cases, the method here proposed is able to equal or even outperform to the results of the other two heuristics in most test problems.

IMPROVING THE PERFORMANCE OF MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS USING THE ISLAND PARALLEL MODEL

Recently, the research interest in multi-objective optimization has increased remarkably. Most of the proposed methods use a population of solutions that are simultaneously improved trying to approximate them to the Pareto-optimal front. When the population size increases, the quality of the solutions tends to be better, but the runtime is higher. This paper presents how to apply parallel processing to enhance the convergence to the Pareto-optimal front, without increasing the runtime. In particular, we present an island-based parallelization of five multi-objective evolutionary algorithms: NSGAII, SPEA2, PESA, msPESA, and a new hybrid version we propose. Experimental results in some test problems denote that the quality of the solutions tends to improve when the number of islands increases.

Multi-Objective Evolutionary Algorithms Used in Greenhouse Planning for Recycling Biomass into Energy

Advanced parallel Multi-Objective Evolutionary Algorithms (MOEA) have been used in order to solve a wide array of problems, including the planning of greenhouse crops. This paper shows the application of MOEA using the Island Parallel Model to solve a problem involving greenhouse crop planning in order to maximize profits and the production of biomass while reducing economic risks. The interest in maximizing biomass waste lies in the possibility of recycling it into heat and energy.

Open source tool for energy saving and efficient system management

In order to improve power quality (PQ) techniques, efforts are made to develop smart sensors that can report near real-time data. Proprietary software and hardware on dedicated computers or servers processes these data and shows relevant information through tables or graphics. In this situation, interoperability, compatibility and scalability are not possible because of the lack of open protocols. This paper presents a new open source solution focused on optimization of power quality and monitoring for low voltage power systems. For that, an open source platform has been developed for computing, storing and managing all of the information generated from smart sensors. We apply the most up-to-date algorithms developed for PQ, event detection, and harmonic analysis or power metering. A plugin implementing the S-transform is being developed for the system. To obtain the best input values to this plugin we are developing optimization algorithms to detect the most of well-known disturbances. Our system makes use of cutting-edge web technologies such as HTML5, CSS3 and Javascript to provide user-friendly interaction and powerful capabilities for the analysis, measurement and monitoring of power systems.