An analysis of the equilibrium of migration models for biogeography-based optimization

Ma, Haiping

doi:10.1016/j.ins.2010.05.035

Cited by 231 publications

(134 citation statements)

References 31 publications

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“…Another search engine employed very frequently is PSO with several variations. The distinct characteristic of both methods is their solution generation scheme, where the solutions are not discarded after each generation but rather changed by the virtue of another variable (Ma, 2010).…”

Section: Resultsmentioning

confidence: 99%

Constrained Optimization with Evolutionary Algorithms: A Comprehensive Review

Kuşakçı

Can

2012

scjournal

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Abstract-( Global optimization is an essential part of any kind of system. Various algorithms have been proposed that try to imitate the learning and problem solving abilities of the nature up to certain level. The main idea of all nature-inspired algorithms is to generate an interconnected network of individuals, a population. Although most of unconstrained optimization problems can be easily handled with Evolutionary Algorithms (EA), constrained optimization problems (COPs) are very complex. In this paper, a comprehensive literature review will be presented which summarizes the constraint handling techniques for COPs.)

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Section: Resultsmentioning

confidence: 99%

Constrained Optimization with Evolutionary Algorithms: A Comprehensive Review

Kuşakçı

Can

2012

scjournal

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“…The migration operator provides BBO with a good exploitation ability. Due to these differences, BBO can outperform other algorithms [9], [10], [11]. Note that if other algorithms like PSO (Particle Swarm Optimization) and DE (Differential Evolution) are constrained to discrete space then the next generation will not necessarily be discrete [11].…”

Section: B Modified Oppositional Biogeography-based Optimization (Momentioning

confidence: 99%

Optimizing wireless access networks towards power consumption: Influence of the optimization algorithm

Deruyck

David

Martens

et al. 2016

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

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Abstract-Nowadays, wireless access networks are already amongst the top power consumers in the ICT (Information and Communication Technology) sector. As it expected that these networks will further expand in the future due to the extreme growth in mobile devices and the high bit rate demand of the applications running on these devices, it is important to consider power consumption as a key parameter in the network design phase. In this paper, two optimization algorithms are proposed: a capacity-based heuristic which aims to reduce power consumption by responding to the instantaneous bit rate demand by the user and an evolutionary opposition-based learning algorithm focusing on the joint-optimization of power consumption and geometrical coverage. Applying both algorithms on a realistic suburban case in Ghent, Belgium, show that both algorithms are able to design an LTE-A network consuming only 24% and 29%, respectively, of the power consumed by the reference scenario which is representative for today's networks. The evolutionary algorithm outperforms the capacity-based algorithm by obtaining a 5% lower power consumption, while the capacity-based heuristic has a 2 to 3% higher coverage. Future research in joint-optimization algorithms of energy and network performance is definitely needed.

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“…The migration operator provides BBO with a good exploitation ability. These differences can make BBO outperform other algorithms [39,58,59]. It must be pointed out that if PSO or ABC are constrained to discrete space then the next generation will not necessarily be discrete [59] .…”

Section: / C Npf /mentioning

confidence: 99%

“…The BBO mechanism is simple, like that of PSO and ABC. Therefore, for most problems, the computational cost of BBO and other EAs will be the same since it will be dominated by objective function evaluation [58]. More details about the BBO algorithm can be found in [39,58,59].…”

Section: / C Npf /mentioning

confidence: 99%

A novel generalized oppositional biogeography-based optimization algorithm: application to peak to average power ratio reduction in OFDM systems

Goudos

2016

Open Mathematics

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A major drawback of orthogonal frequency division multiplexing (OFDM) signals is the high value of peak to average power ratio (PAPR). Partial transmit sequences (PTS) is a popular PAPR reduction method with good PAPR reduction performance, but its search complexity is high. In this paper, in order to reduce PTS search complexity we propose a new technique based on biogeography-based optimization (BBO). More specifically, we present a new Generalized Oppositional Biogeography Based Optimization (GOBBO) algorithm which is enhanced with Oppositional Based Learning (OBL) techniques. We apply both the original BBO and the new Generalized Oppositional BBO (GOBBO) to the PTS problem. The GOBBO-PTS method is compared with other PTS schemes for PAPR reduction found in the literature. The simulation results show that GOBBO and BBO are in general highly efficient in producing significant PAPR reduction and reducing the PTS search complexity.

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An analysis of the equilibrium of migration models for biogeography-based optimization

Cited by 231 publications

References 31 publications

Constrained Optimization with Evolutionary Algorithms: A Comprehensive Review

Constrained Optimization with Evolutionary Algorithms: A Comprehensive Review

Optimizing wireless access networks towards power consumption: Influence of the optimization algorithm

A novel generalized oppositional biogeography-based optimization algorithm: application to peak to average power ratio reduction in OFDM systems

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