Multiobjective algorithms to optimize broadcasting parameters in mobile Ad-hoc networks

Perez-Perez, R.; Luque, C.; Cervantes, Alejandro; Isasi, Pedro

doi:10.1109/cec.2007.4424873

Cited by 8 publications

(3 citation statements)

References 18 publications

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“…Communications Different methods are proposed throughout the literature that used ES for computer networks. Pérez-Pérez et al [265] used Evolution Strategy with NSGAII (ESN) to approximate the Pareto frontier of the mobile adhoc network (MANETs). Krulikovska et al [266] used ES for the routing of multipoint connections.…”

Section: B Evolution Strategy Applicationsmentioning

confidence: 99%

Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey

Majid¹

2021

Preprint

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<div>Deep Reinforcement Learning (DRL) has the potential to surpass human-level control in sequential decision-making problems. Evolution Strategies (ESs) have different characteristics than DRL, yet they are promoted as a scalable alternative. </div><div>To get insights into their strengths and weaknesses, in this paper, we put the two approaches side by side. After presenting the fundamental concepts and algorithms for each of the two approaches, they are compared from the perspectives of scalability, exploration, adaptation to dynamic environments, and multi-agent learning. Then, the paper discusses hybrid algorithms, combining aspects of both DRL and ESs, and how they attempt to capitalize on the benefits of both techniques. Lastly, both approaches are compared based on the set of applications they support, showing their potential for tackling real-world problems.</div><div>This paper aims to present an overview of how DRL and ESs can be used, either independently or in unison, to solve specific learning tasks. It is intended to guide researchers to select which method suits them best and provides a bird's eye view of the overall literature in the field. Further, we also provide application scenarios and open challenges. </div>

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Section: B Evolution Strategy Applicationsmentioning

confidence: 99%

Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey

Majid¹

2021

Preprint

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“…Different methods are proposed throughout the literature that used ESs for communication. Pérez-Pérez et al [245] used ESs with NSGAII (ESN) to approximate the Pareto frontier of the mobile adhoc network (MANETs). Krulikovska et al [246] used ESs for the routing of multipoint connections.…”

Section: B Evolution Strategy Applicationsmentioning

confidence: 99%

Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey

Majid¹,

Saaybi²,

Rietbergen³

et al. 2021

Preprint

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<div>Deep Reinforcement Learning (DRL) and Evolution Strategies (ESs) have surpassed human-level control in many sequential decision-making problems, yet many open challenges still exist.</div><div>To get insights into the strengths and weaknesses of DRL versus ESs, an analysis of their respective capabilities and limitations is provided. </div><div>After presenting their fundamental concepts and algorithms, a comparison is provided on key aspects such as scalability, exploration, adaptation to dynamic environments, and multi-agent learning. </div><div>Then, the benefits of hybrid algorithms that combine concepts from DRL and ESs are highlighted. </div><div>Finally, to have an indication about how they compare in real-world applications, a survey of the literature for the set of applications they support is provided.</div>

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“…The evolutionary multi-objective algorithms combined in this work are MOPSO (Multi-Objective Particle Swarm Optimization) and ESN (Evolution Stratety with NSGAII) algorithms [9]. The first one is an adapted version of Particle Swarm Optimization to multi-objective optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Algorithms Hybridization to Optimize Broadcasting Parameters in Mobile Ad-Hoc Networks

García

Luque

Cervantes

et al. 2009

Lecture Notes in Computer Science

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Abstract. The aim os this paper is to study the hybridization of two multi-objective algorithms in the context of a real problem, the MANETs problem. The algorithms studied are Particle Swarm Optimization (MOPSO) and a new multiobjective algorithm based in the combination of NSGA-II with Evolution Strategies (ESN). This work analyzes the improvement produced by hybridization over the Pareto's fronts compared with the non-hybridized algorithms. The purpose of this work is to validate how hybridization of two evolutionary algorithms of different families may help to solve certain problems together in the context of MANETs problem. The hybridization used for this work consists on a sequential execution of the two algorithms and using the final population of the first algorithm as initial population of the second one.

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Multiobjective algorithms to optimize broadcasting parameters in mobile Ad-hoc networks

Cited by 8 publications

References 18 publications

Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey

Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey

Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey

Multiobjective Algorithms Hybridization to Optimize Broadcasting Parameters in Mobile Ad-Hoc Networks

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