Firefly algorithm for multi-objective RFID network planning problem

Tuba, Milan; Bačanin, Nebojša; Alihodžić, Adis

doi:10.1109/telfor.2014.7034365

Cited by 10 publications

(4 citation statements)

References 27 publications

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“…At the same time, variables x 1 , x 2 , x 3 and x 4 denote the numbers of teeth of the gears A, B, C and D, respectively. Those variables take values in the interval [12,60].…”

Section: P5 Gear Train Design Problemmentioning

confidence: 99%

“…Since a magic formula does not exist, which works for all types of problems [4], in this paper, several swarm intelligence algorithms [5] have been adopted for solving nonlinear engineering problems. Some of the most popular swarm intelligence optimization techniques are artificial bee colony(ABC) [6][7][8] [9], firefly algorithm (FA) [10][1] [11] [12], cuckoo search (CS) [13] [14], bat algorithm (BA) [15][16] [17][18] [19], flower pollination algorithm [20], and etc. In this article, we have combined the bat algorithm as a representative of swarm intelligent multiagent algorithm with one agent simulated annealing method to produce as much as possible suboptimal solutions.…”

Section: Introductionmentioning

confidence: 99%

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An Effective Integrated Metaheuristic Algorithm For Solving Engineering Problems

Alihodžić

Delalić

Gusic

2020

Proceedings of the 2020 Federated Conference on Computer Science and Information Systems

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To tackle a specific class of engineering problems, in this paper, we propose an effectively integrated bat algorithm with simulated annealing for solving constrained optimization problems. Our proposed method (I-BASA) involves simulated annealing, Gaussian distribution, and a new mutation operator into the simple Bat algorithm to accelerate the search performance as well as to additionally improve the diversification of the whole space. The proposed method performs balancing between the grave exploitation of the Bat algorithm and global exploration of the Simulated annealing. The standard engineering benchmark problems from the literature were considered in the competition between our integrated method and the latest swarm intelligence algorithms in the area of design optimization. The simulations results show that I-BASA produces high-quality solutions as well as a low number of function evaluations.

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“…At the same time, variables x 1 , x 2 , x 3 and x 4 denote the numbers of teeth of the gears A, B, C and D, respectively. Those variables take values in the interval [12,60].…”

Section: P5 Gear Train Design Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Effective Integrated Metaheuristic Algorithm For Solving Engineering Problems

Alihodžić

Delalić

Gusic

2020

Proceedings of the 2020 Federated Conference on Computer Science and Information Systems

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“…Besides all these methods and approaches, there are exist also more studies as M. Tuba et al, formulated the problem by considering four objectives: Coverage, interferences, number of readers, and transmitted power. In [11] they presented an implementation of the firefly algorithm with a mono-objective approach and they show that it is efficient than cooperative multi-objective artificial bee colony algorithm (CMOABC), multi-objective artificial bee colony(MOABC), and non-dominated sorting genetic algorithm II (NSGA-II), and in [12] Tuba et al, solve the RNP problem using Hierarchical and Multi-objective approach. Also in [13] authors used Fireworks Algorithm to solve the problem and they obtained that this algorithm is efficient than GPSO and VNPSO.…”

Section: Introductionmentioning

confidence: 99%

Robust multi-objective optimization for solving the RFID network planning problem

Lamin¹,

Raghib²,

Majd³

2021

Math. Model. Comput.

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Radio-frequency identification (RFID) is a new technology used for identifying and tracking objects or people by radio-frequency waves to facilitate automated traceability and data collection. The RFID system consists of an electronic tag attached to an object, readers, and a middleware. In the latest real applications based on the RFID technology, the deployment of readers has become a central issue for RFID network planning by means of optimizing several objectives such as the coverage of tags, the number of readers, and the readers/tags interferences. In practice, the system is affected by uncertainty and uncontrollable environmental parameters. Therefore, the optimal solutions to the RFID network planning problem can be significantly reduced with uncontrollable variations in some parameters, such as the reader's transmitted power. In this work, we propose a robust multi-objective optimization approach to solve the deployment of RFID readers. In this way, we achieve robust optimal solutions that are insensitive to uncertainties in the optimization parameters.

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“…Firefly algorithm (FA) introduced by Yang [15] models flashing behavior of fireflies. FA exhibits outstanding performance when dealing with different kinds of problems [16], [17], [18], [19], [20]. Seeker optimization algorithm (SOA) emulates human search process.…”

Section: Introductionmentioning

confidence: 99%

Hybridized bat algorithm for multi-objective radio frequency identification (RFID) network planning

Tuba

Bačanin

2015

2015 IEEE Congress on Evolutionary Computation (CEC)

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This paper introduces implementation of hybridized bat algorithm for multi-objective radio frequency identification network planning problem. Multi-objective RFID problem is a well known hard optimization problem that can be solved by using swarm intelligence algorithms. Bat algorithm is a recent mataheuristic, proved to be very successful for tackling such tasks. In our implementation, we hybridized bat algorithm with the artificial bee colony algorithm and adapted it for solving radio frequency identification network planning problem. In the experimental section, we have first shown, by using standard bound-constrained benchmark functions, that our hybridization is justified and that it improves results compared to standard bat algorithm, as well as to other state-of-the-art algorithms. After that, we examined performance of our proposed approach on illustrative RFID network planning problem and compared it with other results from the literature where our proposed algorithm proved to be more successful.Keywords-RFID network planning; bat algorithm swarm intelligence; nature inspired algorithms; hybrid algorithms

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Firefly algorithm for multi-objective RFID network planning problem

Cited by 10 publications

References 27 publications

An Effective Integrated Metaheuristic Algorithm For Solving Engineering Problems

An Effective Integrated Metaheuristic Algorithm For Solving Engineering Problems

Robust multi-objective optimization for solving the RFID network planning problem

Hybridized bat algorithm for multi-objective radio frequency identification (RFID) network planning

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