Implementing Metaheuristic Optimization Algorithms with JECoLi

Evangelista, Pedro; Maia, Paulo; Rocha, Miguel

doi:10.1109/isda.2009.161

Cited by 12 publications

(8 citation statements)

References 16 publications

(14 reference statements)

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“…The experimental simulations were run on a publicly available optimization framework, NetOpt [13], previously developed by the authors, in which the optimization meta-heuristic algorithms are provided by a Java-based library, JEColi [6]. An OSPF routing simulator is used to accommodate the traffic demands onto the networks topology arcs, and therefore enabling the application of the congestion evaluation function Φ * .…”

Section: Experimental Frameworkmentioning

confidence: 99%

Comparison of Single and Multi-objective Evolutionary Algorithms for Robust Link-State Routing

Pereira

Sousa

Cortez

et al. 2015

Lecture Notes in Computer Science

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Traffic Engineering (TE) approaches are increasingly important in network management to allow an optimized configuration and resource allocation. In link-state routing, the task of setting appropriate weights to the links is both an important and a challenging optimization task. A number of different approaches has been put forward towards this aim, including the successful use of Evolutionary Algorithms (EAs). In this context, this work addresses the evaluation of three distinct EAs, a single and two multi-objective EAs, in two tasks related to weight setting optimization towards optimal intra-domain routing, knowing the network topology and aggregated traffic demands and seeking to minimize network congestion. In both tasks, the optimization considers scenarios where there is a dynamic alteration in the state of the system, in the first considering changes in the traffic demand matrices and in the latter considering the possibility of link failures. The methods will, thus, need to simultaneously optimize for both conditions, the normal and the altered one, following a preventive TE approach towards robust configurations. Since this can be formulated as a bi-objective function, the use of multi-objective EAs, such as SPEA2 and NSGA-II, came naturally, being those compared to a single-objective EA. The results show a remarkable behavior of NSGA-II in all proposed tasks scaling well for harder instances, and thus presenting itself as the most promising option for TE in these scenarios.

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Section: Experimental Frameworkmentioning

confidence: 99%

Comparison of Single and Multi-objective Evolutionary Algorithms for Robust Link-State Routing

Pereira

Sousa

Cortez

et al. 2015

Lecture Notes in Computer Science

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“…A module containing specific optimization routines was created for feed optimization and related tasks. This module uses JECoLi (Evangelista, Maia, & Rocha, 2009) that contains generic optimization routines based on metaheuristic search algorithms. Some adaptations had to be made in order to use these algorithms to support feed optimizations (Rocha et al, 2004;Mendes, Rocha, Ferreira, & Rocha, 2006).…”

Section: Optferm An Optimization Supporting Tool For Fermentation Enmentioning

confidence: 99%

Optimization of fed-batch fermentation processes with bio-inspired algorithms

Rocha

Mendes

Rocha

et al. 2014

Expert Systems with Applications

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The optimization of the feeding trajectories in fed-batch fermentation processes is a complex problem that has gained attention given its significant economical impact. A number of bio-inspired algorithms have approached this task with considerable success, but systematic and statistically significant comparisons of the different alternatives are still lacking. In this paper, the performance of different metaheuristics, such as Evolutionary Algorithms (EAs), Differential Evolution (DE) and Particle Swarm Optimization (PSO) is compared, resorting to several case studies taken from literature and conducting a thorough statistical validation of the results. DE obtains the best overall performance, showing a consistent ability to find good solutions and presenting a good convergence speed, with the DE/rand variants being the ones with the best performance. A freely available computational application, OptFerm, is described that provides an interface allowing users to apply the proposed methods to their own models and data.

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“…Regarding the EA engine, we adopted a general EA, as implemented in the JECoLi Java library. 25 First, there is an initial population with P individuals. New solutions are bred through the use of random respectful recombination 26 and random mutation operators:…”

Section: Evolutionary Feature Selectionmentioning

confidence: 99%

Email Spam Detection: A Symbiotic Feature Selection Approach Fostered by Evolutionary Computation

Sousa

Cortez

Vaz

et al. 2013

Int. J. Info. Tech. Dec. Mak.

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The electronic mail (email) is nowadays an essential communication service being widely used by most Internet users. One of the main problems affecting this service is the proliferation of unsolicited messages (usually denoted by spam) which, despite the efforts made by the research community, still remains as an inherent problem affecting this Internet service. In this perspective, this work proposes and explores the concept of a novel symbiotic feature selection approach allowing the exchange of relevant features among distinct collaborating users, in order to improve the behavior of anti-spam filters. For such purpose, several Evolutionary Algorithms (EA) are explored as optimization engines able to enhance feature selection strategies within the anti-spam area. The proposed mechanisms are tested using a realistic incremental retraining evaluation procedure and resorting to a novel corpus based on the well-known Enron datasets mixed with recent spam data. The obtained results show that the proposed symbiotic approach is competitive also having the advantage of preserving end-users privacy.

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Implementing Metaheuristic Optimization Algorithms with JECoLi

Cited by 12 publications

References 16 publications

Comparison of Single and Multi-objective Evolutionary Algorithms for Robust Link-State Routing

Comparison of Single and Multi-objective Evolutionary Algorithms for Robust Link-State Routing

Optimization of fed-batch fermentation processes with bio-inspired algorithms

Email Spam Detection: A Symbiotic Feature Selection Approach Fostered by Evolutionary Computation

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