Using local search strategies to improve the performance of NSGA-II for the Multi-Criteria Minimum Spanning Tree problem

Parraga-Alava, Jorge; Dorn, Márcio; Inostroza-Ponta, Mario

doi:10.1109/cec.2017.7969432

Cited by 3 publications

(3 citation statements)

References 28 publications

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“…We refer to Ehrgott [Ehrgott, 2005] for an extensive discussion of the problem and the different algorithmic approaches to it. Being NP-complete, many heuristic approaches have been developed [Arroyo et al, 2008], including many based on evolutionary algorithms [Knowles and Corne, 2000;Knowles and Corne, 2001;Bossek and Grimme, 2017;Parraga-Alava et al, 2017;Majumder et al, 2020].…”

Section: Previous Workmentioning

confidence: 99%

The First Proven Performance Guarantees for the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) on a Combinatorial Optimization Problem

Cerf,

Doerr,

Hebras

et al. 2023

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence

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The Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is one of the most prominent algorithms to solve multi-objective optimization problems. Recently, the first mathematical runtime guarantees have been obtained for this algorithm, however only for synthetic benchmark problems. In this work, we give the first proven performance guarantees for a classic optimization problem, the NP-complete bi-objective minimum spanning tree problem. More specifically, we show that the NSGA-II with population size N >= 4((n-1) wmax + 1) computes all extremal points of the Pareto front in an expected number of O(m^2 n wmax log(n wmax)) iterations, where n is the number of vertices, m the number of edges, and wmax is the maximum edge weight in the problem instance. This result confirms, via mathematical means, the good performance of the NSGA-II observed empirically. It also shows that mathematical analyses of this algorithm are not only possible for synthetic benchmark problems, but also for more complex combinatorial optimization problems. As a side result, we also obtain a new analysis of the performance of the global SEMO algorithm on the bi-objective minimum spanning tree problem, which improves the previous best result by a factor of |F|, the number of extremal points of the Pareto front, a set that can be as large as n wmax. The main reason for this improvement is our observation that both multi-objective evolutionary algorithms find the different extremal points in parallel rather than sequentially, as assumed in the previous proofs.

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Section: Previous Workmentioning

confidence: 99%

The First Proven Performance Guarantees for the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) on a Combinatorial Optimization Problem

Cerf,

Doerr,

Hebras

et al. 2023

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence

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“…The performance of NSGA-II has been investigated with the use of three different local search techniques in [143]: 1) PLS, 2) Path-Relinking (PR) [78], which starts from a high-quality solution generating a path in the neighbourhood space that leads toward another solution, and 3) Tabu search [77]. It has been shown in [143] that all memetic MOEAs can clearly outperform NSGA-II, and PLS is more effective than PR and Tabu search. However, PLS has a restrictive nature and a strict acceptance criterion.…”

Section: Memetic Algorithms (Mas)mentioning

confidence: 99%

“…Experiments were conducted to compare the performance of LPLS-NSGA-II and PLS-based NSGA-II (PLS-NSGA-II) [143]. Additionally, a relaxed version of LPLS was implemented (i.e., Relaxed-LPLS-NSGA-II).…”

Section: Algorithm 9 Adaptive Memetic Algorithm For Distributed Dwscmentioning

confidence: 99%

Distributed Data-intensive Web Service Composition

Sadeghiram¹

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<p>Service-oriented architecture (SOA) encourages the creation of modular applications involving Web services as the reusable components. Data-intensive Web services have emerged to manipulate and deal with the massive data emerged from technological advances and their various applications. Distributed Data-intensive Web Service Composition (DWSC) is a core of SOA, which includes the selection of data-intensive Web services from diverse locations on the network and composes them to accomplish a complicated task. As a fundamental challenge for service developers, service compositions must fulfil functional requirements and optimise Quality of Service (QoS), simultaneously. The QoS of a distributed DWSC is not only impacted by the QoS of component services and how the compositions are generated, but also by the locations of services and data transformation between services. However, existing works often neglect the impact of locations and data on service composition. The distributed DWSC has not been sufficiently studied in the literature. In this thesis, we first define the single-objective distributed DWSC that includes communication (e.g. bandwidth), Web service (execution time) and data (data cost) attributes. To this aim, we consider bandwidth information of communication links obtained using the location information of services. Based on the problem formulation, we then address the distributed DWSC problem by developing EC-based approaches. Those EC-based approaches are designed to incorporate domain-knowledge for effectively solving the distributed DWSC problem. Afterwards, we study the multi-objective distributed DWSC to satisfy different QoS requirements. In particular, the QoS-constrained distributed DWSC problem and user preferences are considered. For finding trade-off solutions for those problems, new Multi-objective Evolutionary Algorithms (MOEAs) are proposed based on the current Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Furthermore, a new problem formulation for the dynamic distributed DWSC (D2−DWSC) problem with bandwidth fluctuations is proposed. An EC-based approach is developed to solve the D2-DWSC. Finally, extensive empirical evaluations are conducted that demonstrate the high performance of our proposed methods in finding composite services with good QoS.</p>

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Minimum Shared-Link-Count Forwarding for Alleviating Congestion in Wireless Sensor Networks

Thomas

Mathew²

2018

Engineering Vibration, Communication and Information Processing

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Using local search strategies to improve the performance of NSGA-II for the Multi-Criteria Minimum Spanning Tree problem

Cited by 3 publications

References 28 publications

The First Proven Performance Guarantees for the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) on a Combinatorial Optimization Problem

The First Proven Performance Guarantees for the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) on a Combinatorial Optimization Problem

Distributed Data-intensive Web Service Composition

Minimum Shared-Link-Count Forwarding for Alleviating Congestion in Wireless Sensor Networks

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