Gara Miranda

Using multi-objective evolutionary algorithms for single-objective optimization

In recent decades, several multi-objective evolutionary algorithms have been successfully applied to a wide variety of multi-objective optimization problems. Along the way, several new concepts, paradigms and methods have emerged. Additionally, some authors have claimed that the application of multi-objective approaches might be useful even in single-objective optimization. Thus, several guidelines for solving single-objective optimization problems using multi-objective methods have been proposed. This paper offers a survey of the main methods that allow the use of multi-objective schemes for single-objective optimization. In addition, several open topics and some possible paths of future work in this area are identified.

A Parallel Plugin-Based Framework for Multi-objective Optimization

This work presents a parallel framework for the solution of multi-objective optimization problems. The framework implements some of the best known multi-objective evolutionary algorithms. The framework architecture makes usage of configuration files to provide a more extensive and simple customization environment than other similar tools. A wide variety of configuration options can be specified to adapt the software behaviour to many different parallel models, including a new adaptive model which dynamically grants more computational resources to the most promising algorithms. The plugin-based architecture of the framework minimizes the final user effort required to incorporate their own problems and evolutionary algorithms, and facilitates the tool maintenance. The flexibility of the approach has been tested by configuring a standard homogeneous island-based model and a self-adaptive model. The computational results obtained for problems with different granularity demonstrate the efficiency of the provided parallel implementation.

Benchmarking a Wide Spectrum of Metaheuristic Techniques for the Radio Network Design Problem

The radio network design (RND) is an NP-hard optimization problem which consists of the maximization of the coverage of a given area while minimizing the base station deployment. Solving RND problems efficiently is relevant to many fields of application and has a direct impact in the engineering, telecommunication, scientific, and industrial areas. Numerous works can be found in the literature dealing with the RND problem, although they all suffer from the same shortfall: a noncomparable efficiency. Therefore, the aim of this paper is twofold: first, to offer a reliable RND comparison base reference in order to cover a wide algorithmic spectrum, and, second, to offer a comprehensible insight into accurate comparisons of efficiency, reliability, and swiftness of the different techniques applied to solve the RND problem. In order to achieve the first aim we propose a canonical RND problem formulation driven by two main directives: technology independence and a normalized comparison criterion. Following this, we have included an exhaustive behavior comparison between 14 different techniques. Finally, this paper indicates algorithmic trends and different patterns that can be observed through this analysis.

Metaheuristics for solving a real-world frequency assignment problem in GSM networks

The Frequency Assignment Problem (FAP) is one of the key issues in the design of GSM networks (Global System for Mobile communications), and will remain important in the foreseeable future. There are many versions of FAP, most of them benchmarking-like problems. We use a formulation of FAP, developed in published work, that focuses on aspects which are relevant for real-world GSM networks. In this paper, we have designed, adapted, and evaluated several types of metaheuristic for different time ranges. After a detailed statistical study, results indicate that these metaheuristics are very appropriate for this FAP. New interference results have been obtained, that significantly improve those published in previous research.

Optimization algorithms for large-scale real-world instances of the frequency assignment problem

Nowadays, mobile communications are experiencing a strong growth, being more and more indispensable. One of the key issues in the design of mobile networks is the frequency assignment problem (FAP). This problem is crucial at present and will remain important in the foreseeable future. Real-world instances of FAP typically involve very large networks, which can be handled only by heuristic methods. In the present work, we are interested in optimizing frequency assignments for problems described in a mathematical formalism that incorporates actual interference information, measured directly on the field, as is done in current GSM networks. To achieve this goal, a range of metaheuristics have been designed, adapted, and rigourously compared on two actual GSM networks modeled according to the latter formalism. To generate quickly and reliably high-quality solutions, all metaheuristics combine their global search capabilities with a local-search method specially tailored for this domain. The experiments and statistical tests show that in general, all metaheuristics are able to improve upon results published in previous studies, but two of the metaheuristics emerge as the best performers: a population-based algorithm (Scatter Search) and a trajectory based (1+1) Evolutionary Algorithm. Finally, the analysis of the frequency plans obtained offers insight about how the interference cost is reduced in the optimal plans.

Using multi-objective evolutionary algorithms for single-objective constrained and unconstrained optimization

In recent decades, several multi-objective evolutionary algorithms have been successfully applied to a wide variety of multi-objective optimization problems. Along the way, several new concepts, paradigms and methods have emerged. Additionally, some authors have claimed that the application of multi-objective approaches might be useful even in single-objective optimization. Thus, several guidelines for solving single-objective optimization problems using multi-objective methods have been proposed. This paper offers an updated survey of the main methods that allow the use of multi-objective schemes for single-objective optimization. In addition, several open topics and some possible paths of future work in this area are identified.

Optimisation of a multi-objective two-dimensional strip packing problem based on evolutionary algorithms

This paper considers a real-world two-dimensional strip packing problem involving specific machinery constraints and actual cutting production industry requirements. To adapt the problem to a wider range of machinery characteristics, the design objective considers the minimisation of material length and the total number of cuts for guillotinable-type patterns. The number of cuts required for the cutting process is crucial for the life of the industrial machines and is an important aspect in determining the cost and efficiency of the cutting operation. In this paper we propose the application of evolutionary algorithms to address the multi-objective problem, for which numerous approaches to its single-objective formulation exist, but for which multi-objective approaches are almost non-existent. The multi-objective evolutionary algorithms applied provide a set of solutions offering a range of trade-offs between the two objectives from which clients can choose according to their needs. By considering both the length and number of cuts, they derive solutions with wastage levels similar to most previous approximations which just seek to optimise the overall length.

Improving the diversity preservation of multi-objective approaches used for single-objective optimization

The maintenance of a proper diversity is an important issue for the correct behavior of Evolutionary Algorithms (EAs). The loss of diversity might lead to stagnation in suboptimal regions, producing the effect known as “premature convergence”. Several methods to avoid premature convergence have been previously proposed. Among them, the use of Multi-objective Evolutionary Algorithms (MOEAs) is a promising approach. Several ways of using MOEAs for single-objective optimization problems have been devised. The use of an additional objective based on calculating the diversity that each individual introduces in the population has been successfully applied by several researchers. Several ways of measuring the diversity have also been tested. In this work, the main weaknesses of some of the previously presented approaches are analyzed. Considering such drawbacks, a new scheme whose aim is to maintain a better diversity than previous approaches is proposed. The proposed approach is empirically validated using a set of well-known single-objective benchmark problems. Our preliminary results indicate that the proposed approach provides several advantages in terms of premature convergence avoidance. An analysis of the convergence in the average-case is also carried out. Such an analysis reveals that the better ability of our proposed approach to deal with premature convergence produces a reduction in the convergence speed in the average-case for several of the benchmark problems adopted.

A memetic algorithm and a parallel hyperheuristic island-based model for a 2D packing problem

This work presents several approaches used to deal with the 2D packing problem proposed in the GECCO 2008 contest session. A memetic algorithm, together with the specifically designed local search and variation operators, are presented. A novel parallel model was used to parallelize the approach. The model is a hybrid algorithm which combines a parallel island-based scheme with a hyperheuristic approach. An adaptive behavior is added to the island-based model by applying the hyperheuristic procedure. The main operation of the island-based model is kept, but the configurations of the memetic algorithms executed on each island are dynamically mapped. The model grants more computational resources to those configurations that show a more promising behavior. For this purpose a specific criterion was designed in order to select the configurations with better success expectations. Computational results obtained for the contest problem demonstrate the validity of the proposed model. The best reported solutions for the problem contest instance have been achieved by using the here presented approaches.

Parallel Hypervolume-Guided Hyperheuristic for Adapting the Multi-objective Evolutionary Island Model

This work presents a new parallel model for the solution of multi-objective optimization problems. The model is based on the cooperation of a set of evolutionary algorithms. The main aim is to raise the level of generality at which most current evolutionary algorithms operate. This way, a wider range of problems can be tackled since the strengths of one algorithm can compensate for the weaknesses of another. The proposed model is a hybrid algorithm that combines a parallel island-based scheme with a hyperheuristic approach. The hyperheuristic is guided by the measurement of the hypervolume achieved by different optimization methods. The model grants more computational resources to those schemes that show a more promising behaviour. The computational results obtained for some tests available in the literature demonstrate the validity of the proposed model.