Evolutionary Algorithms for Mobile Ad Hoc Networks

Dorronsoro, Bernab; Ruiz, Patricia; Danoy, Grégoire; Pign, Yoann; Bouvry, Pascal

doi:10.1002/9781118833209

Cited by 33 publications

(18 citation statements)

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“…The performance of the CC versions of both NSGA-II and SPEA2 was shown to be highly competitive with respect to the original algorithms in a number of combinatorial and continuous problems, both on theoretical benchmarks and realworld problems [6], [7], [8], [18]. …”

Section: Evaluated Algorithmsmentioning

confidence: 99%

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A parallel cooperative coevolutionary SMPSO algorithm for multi-objective optimization

Atashpendar

Dorronsoro

Danoy

et al. 2016

2016 International Conference on High Performance Computing &Amp; Simulation (HPCS)

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Abstract-This paper presents a new parallel multi-objective cooperative coevolutionary variant of the Speed-constrained Multi-objective Particle Swarm Optimization (SMPSO) algorithm. SMPSO adopts a strategy for limiting the velocity of the particles that prevents them from having erratic movements. This characteristic provides the algorithm with a high degree of reliability. The proposed approach, called CCSMPSO, is based on a new design and implementation of SMPSO in a cooperative coevolutionary (CC) framework. In such an architecture, the population is split into several subpopulations, which are in turn in charge of optimizing a subset of the global solution by using the original multi-objective algorithm. We compare our work with two different state-of-the-art multi-objective CC metaheuristics, namely CCNSGA-II and CCSPEA2, as well as the original SMPSO in order to demonstrate its effectiveness. Our experiments indicate that our proposed solution, CCSMPSO, offers significant computational speedups, a higher convergence speed and better and comparable results in terms of solution quality, when compared to the other two CC algorithms and SMPSO, respectively. Three different criteria are used for making the comparisons, namely the quality of the resulting approximation sets, average computational time and the convergence speed to the Pareto front.

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Section: Evaluated Algorithmsmentioning

confidence: 99%

“…For a more thorough analysis of the superiority of CCMOEAs and their super-linear speedup with respect to their sequential counterparts in continuous and combinatorial problems, the reader is encouraged to refer to [6], [7], [8], [18].…”

Section: ) Computational Speedupmentioning

confidence: 99%

A parallel cooperative coevolutionary SMPSO algorithm for multi-objective optimization

Atashpendar

Dorronsoro

Danoy

et al. 2016

2016 International Conference on High Performance Computing &Amp; Simulation (HPCS)

Self Cite

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“…For example, RahmatSamii et al [9] used PSO for antenna design optimization. Dorronsoro et al [10] used evolutionary algorithm to model and solve minimization problems. Grimaccia et al [11] used social network optimization to design generators for vehicle energy harvesting.…”

Section: Evolutionary Algorithmsmentioning

confidence: 99%

Combining Extended Imperialist Competitive Algorithm with a Genetic Algorithm to Solve the Distributed Integration of Process Planning and Scheduling Problem

Zhang

et al. 2017

Mathematical Problems in Engineering

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Distributed integration of process planning and scheduling (DIPPS) extends traditional integrated process planning and scheduling (IPPS) by considering the distributed features of manufacturing. In this study, we first establish a mathematical model which contains all constraints for the DIPPS problem. Then, the imperialist competitive algorithm (ICA) is extended to effectively solve the DIPPS problem by improving country structure, assimilation strategy, and adding resistance procedure. Next, the genetic algorithm (GA) is adapted to maintain the robustness of the plan and schedule after machine breakdown. Finally, we perform a two-stage experiment to prove the effectiveness and efficiency of extended ICA and GA in solving DIPPS problem with machine breakdown.

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“…For an extensive survey on optimization algorithms solving problems in ad hoc networks, please refer to [41].…”

Section: Centralized and Decentralized Systemsmentioning

confidence: 99%

“…Evolutionary algorithms can be applied for solving different problems in ad hoc networks in many ways. We classify the optimization process in terms of the execution mode, information needed, and platform used [41].…”

Section: Application Of Evolutionary Algorithms To Problems Inmentioning

confidence: 99%

A Survey on the Application of Evolutionary Algorithms for Mobile Multihop Ad Hoc Network Optimization Problems

Reina

Ruiz

Ciobanu

et al. 2016

International Journal of Distributed Sensor Networks

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Evolutionary algorithms are metaheuristic algorithms that provide quasioptimal solutions in a reasonable time. They have been applied to many optimization problems in a high number of scientific areas. In this survey paper, we focus on the application of evolutionary algorithms to solve optimization problems related to a type of complex network like mobile multihop ad hoc networks. Since its origin, mobile multihop ad hoc network has evolved causing new types of multihop networks to appear such as vehicular ad hoc networks and delay tolerant networks, leading to the solution of new issues and optimization problems. In this survey, we review the main work presented for each type of mobile multihop ad hoc network and we also present some innovative ideas and open challenges to guide further research in this topic.

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Evolutionary Algorithms for Mobile Ad Hoc Networks

Cited by 33 publications

References 0 publications

A parallel cooperative coevolutionary SMPSO algorithm for multi-objective optimization

A parallel cooperative coevolutionary SMPSO algorithm for multi-objective optimization

Combining Extended Imperialist Competitive Algorithm with a Genetic Algorithm to Solve the Distributed Integration of Process Planning and Scheduling Problem

A Survey on the Application of Evolutionary Algorithms for Mobile Multihop Ad Hoc Network Optimization Problems

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