A multi-objective approach to indoor wireless heterogeneous networks planning based on biogeography-based optimization

Goudos, Sotirios K.; David, Paul A.; Liu, Ning; Martens, Luc; Joseph, Wout

doi:10.1016/j.comnet.2015.08.037

Cited by 23 publications

(10 citation statements)

References 47 publications

(64 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Different examples are given. They are: the case of heterogenous networks consisting of WiFi access points [123]; multi objective node deployment to ensure reliable and efficient real time performance [124][125] and lifetime maximisation [126]; optimum allocation of spectrum in wireless networks [127] and minimisation of the number of links in WSNs [128].…”

Section: Quality Of Service Improvementmentioning

confidence: 99%

Differential Evolution in Wireless Communications: A Review

Okagbue¹,

Adamu

Anake

2019

Int. J. Onl. Eng.

View full text Add to dashboard Cite

<p class="0abstract">Differential Evolution (DE) is an evolutionary computational method inspired by the biological processes of evolution and mutation. DE has been applied in numerous scientific fields. The paper presents a literature review of DE and its application in wireless communication. The detailed history, characteristics, strengths, variants and weaknesses of DE were presented. Seven broad areas were identified as different domains of application of DE in wireless communications. It was observed that coverage area maximisation and energy consumption minimisation are the two major areas where DE is applied. Others areas are quality of service, updating mechanism where candidate positions learn from a large diversified search region, security and related field applications. Problems in wireless communications are often modelled as multiobjective optimisation which can easily be tackled by the use of DE or hybrid of DE with other algorithms. Different research areas can be explored and DE will continue to be utilized in this context.</p>

show abstract

Section: Quality Of Service Improvementmentioning

confidence: 99%

Differential Evolution in Wireless Communications: A Review

Okagbue¹,

Adamu

Anake

2019

Int. J. Onl. Eng.

View full text Add to dashboard Cite

show abstract

“…A abordagem foi destinada a redes Wi-Fi outdoor, sem obstáculos. Já Goudos et al (2015) empregaram um algoritmo de otimização multiobjetivo baseado em biogeografia para projetar uma rede híbrida, 802.11 e LTE (Long Term Evolution). Os objetivos foram maximizar a cobertura da rede e minimizar a potência de transmissão dos APs.…”

Section: Trabalhos Relacionadosunclassified

Planejamento Multiobjetivo de Redes Locais sem Fio Utilizando Algoritmos Híbridos

Lima

Takahashi

Vieira

et al. 2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

View full text Add to dashboard Cite

This work presents a novel approach based on hybrid optimization algorithms for planning Wireless Local Area Networks (WLANs). Such an approach is split into two main parts: network structure design and channel assignment enhancement. In the project phase, the quantity, positions, and channels of the access points are planned to take into account their maximum capacities, client demand attendance, and minimum required coverage. Different tests were performed in two scenarios, one using real data from a large-scale WLAN. The results were compared to three approaches and they show that the proposed approach provides solutions that reduce costs and improve the throughput of WLANs. Resumo: Este trabalho apresenta uma nova abordagem baseada em algoritmos de otimização híbridos para o planejamento de redes locais sem fio (WLANs). Tal abordagem divide-se em duas partes: projeto estrutural da rede e aprimoramento do esquema de atribuição de canais. Na fase de projeto, a quantidade, as posições e os canais dos pontos de acesso são planejados tendo em conta suas capacidades, as demandas de tráfego dos usuários e a cobertura mínima necessária. Diferentes testes foram executados em dois cenários, sendo um deles utilizando dados reais de uma WLAN de grande porte. Os resultados foram comparados com três métodos e demonstram que a abordagem propostaé capaz de fornecer soluções que reduzem os custos e melhoram a vazão das WLANs.

show abstract

“…An efficient multi-objective optimization algorithm using the differential evolution (DE) algorithm is proposed to solve multi-objective optimal power flow (MO-OPF) problems [37]. BBO has also been modified to solve multi-objective optimization problems (MOPs) [38][39][40][41][42][43][44][45], such as, multi-objective biogeography-based optimization based on predator-prey approach [38], indoor wireless heterogeneous networks planning [39], automated warehouse scheduling [40], and community detection in social networks with node attributes [41]. Work in the literature [42] is focused on numerical comparisons of migration models for multi-objective biogeography-based optimization.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybridizing Adaptive Biogeography-Based Optimization with Differential Evolution for Multi-Objective Optimization Problems

2017

View full text Add to dashboard Cite

In order to improve the performance of optimization, we apply a hybridization of adaptive biogeography-based optimization (BBO) algorithm and differential evolution (DE) to multi-objective optimization problems (MOPs). A model of multi-objective evolutionary algorithms (MOEAs) is established, in which the habitat suitability index (HSI) is redefined, based on the Pareto dominance relation, and density information among the habitat individuals. Then, we design a new algorithm, in which the modification probability and mutation probability are changed, according to the relation between the cost of fitness function of randomly selected habitats of last generation, and average cost of fitness function of all habitats of last generation. The mutation operators based on DE algorithm, are modified, and the migration operators based on number of iterations, are improved to achieve better convergence performance. Numerical experiments on different ZDT and DTLZ benchmark functions are performed, and the results demonstrate that the proposed MABBO algorithm has better performance on the convergence and the distribution properties comparing to the other MOEAs, and can solve more complex multi-objective optimization problems efficiently. distribution of solutions [6]. Second, there are several good results based on the hybridization of BBO with differential evolution (DE) [8,9]. Last, blend combination operators have been widely and successfully used in other population-based optimization algorithms [10].The original contributions of this paper include the following. Firstly, the model of applying BBO for multi-objective evolutionary algorithm was established, in which the habitat suitability index is redefined, based on the Pareto dominance relation and density information among the habitat individuals. Motivated by the work in [11,12], we hybridize adaptive BBO with DE to design a new algorithm for multi-objective optimization problems. In this algorithm, the mutation operator based on DE algorithm was modified, and the migration operator based on the number of iterations was re-designed to achieve better performance. Furthermore, the modification probability and mutation probability are adaptively changed, according to the relation between the cost of fitness function of randomly selected habitats of last generation, and average cost of fitness function of all habitats of last generation. Experiments performed on ZDT and DTLZ benchmark functions show that the obtained Pareto solution set can approximate to the Pareto optimal front, and has good diversity and uniform distribution. The results also demonstrate that the proposed MABBO algorithm has better performance on the convergence and the distribution.The paper is organized as follows. Section 2 describes the related work about BBO algorithm. The basic theories of multi-objective optimization are presented in Section 3.The MABBO algorithm procedure for multi-objective optimization problems are proposed in Section 4. Section 5 shows the simulation and experiment results. ...

show abstract

A multi-objective approach to indoor wireless heterogeneous networks planning based on biogeography-based optimization

Cited by 23 publications

References 47 publications

Differential Evolution in Wireless Communications: A Review

Differential Evolution in Wireless Communications: A Review

Planejamento Multiobjetivo de Redes Locais sem Fio Utilizando Algoritmos Híbridos

Hybridizing Adaptive Biogeography-Based Optimization with Differential Evolution for Multi-Objective Optimization Problems

Contact Info

Product

Resources

About