A new hybrid cuckoo search and firefly optimization

Elkhechafi, Mariam; Hachimi, Hanaâ; Elkettani, Youssfi

doi:10.1515/mcma-2018-0003

Cited by 19 publications

(6 citation statements)

References 12 publications

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“…A hybrid of bat algorithm and FA has been proposed by Warangal et al in [20]. A hybrid of cuckoo search of FA has been proposed by Elkhechafi et al in [21]. A hybrid of FA and ant colony optimization has been proposed by Layeb and Benayad in [22].…”

Section: Other Hybrid Firefly Algorithmsmentioning

confidence: 99%

“…and its associated is better than set current as 1.4.3 For each particle Calculate particle velocity according equation 20Update particle position according equation (21) and with application of equations (18) and (19) If particle position update result in allocation of more than one channel, randomly select only one channel End While maximum iterations has not been reached. Step 3  3.1 Calculate the fitness value of each firefly using equation 1and rank the fireflies according to their fitness values.…”

Section: If Currentmentioning

confidence: 99%

“…plays the role of balancing local search and global search. With the new current velocity, the position of the particle is then updated according to the equation (21): (18) and (19) will be used to convert the values obtained during particle position update to binary values. If particle position update result in allocation of more than one channel to SU, one channel will be randomly.…”

Section: Spectrum Allocation Using Binary Hybrid Firefly and Particlementioning

confidence: 99%

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Comparison of Hybrid Firefly Algorithms for Binary and Continuous Optimization Problems in a TV White Space Network

Ronoh

Kamucha

2020

Wseas Transactions on Communications

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TV white spaces (TVWS) can be used by Secondary Users (SUs) through Dynamic Spectrum Access (DSA) as long as they do not cause harmful interference to Primary Users (PUs). Due to spectrum scarcity, there is increasing demand for DSA. When there is a high density of SUs in a TVWS network such as cellular access to TVWS, problem of interference among SUs will arise. Possibility of harmful interference to PUs may also arise. Power and spectrum allocation optimization is therefore necessary to reduce the level of interference among SUs and to protect PUs against harmful interference. In this paper different hybrid firefly algorithms with particle swarm optimization and genetic algorithm for optimization of spectrum allocation in a TVWS network as a discrete optimization problem and that of power allocation as a continuous optimization problem are compared. Simulation was done using Matlab. Simulation results show that hybrid firefly algorithm with genetic algorithm outperforms other hybrid firefly algorithms for spectrum allocation. On the other hand, hybrid firefly algorithm with genetic algorithm and particle swarm optimization outperforms all other algorithms for power allocation as continuous optimization problem

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Section: Other Hybrid Firefly Algorithmsmentioning

confidence: 99%

Section: If Currentmentioning

confidence: 99%

Section: Spectrum Allocation Using Binary Hybrid Firefly and Particlementioning

confidence: 99%

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Comparison of Hybrid Firefly Algorithms for Binary and Continuous Optimization Problems in a TV White Space Network

Ronoh

Kamucha

2020

Wseas Transactions on Communications

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“…A hybrid of bat algorithm and FA has been proposed by Warangal et alin [20]. A hybrid of cuckoo search of FA has been proposed by Elkhechafi et al in [21]. A hybrid of FA and ant colony optimization has been proposed by Layeb and Benayad in [22].…”

Section: Other Hybrid Firefly Algorithmsmentioning

confidence: 99%

Comparison of Hybrid Firefly Algorithms for Power Allocation in a TV White Space Network

Kennedy¹,

Kamucha²,

Tonny³

2019

IJCA

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TV white spaces (TVWS) can be used by Secondary Users (SUs) through Dynamic Spectrum Access (DSA) as long as they do not cause harmful interference to Primary Users (PUs). Due spectrum scarcity, there is increasing demand for DSA. When there is a high density of SUs in a TVWS network such as cellular access to TVWS, problem of interference among SUs will arise. Possibility of harmful interference to PUs may also arise. Optimization of power allocation is therefore necessary to reduce the level of interference among SUs and to protect PUs against harmful interference. Performance of different hybrid firefly algorithm with particle swarm optimization and genetic algorithm for optimization of power allocation in a TVWS network are compared. Simulation was done using Matlab. Simulation results show that hybrid of firefly algorithm, particle swarm optimization and genetic algorithm outperform other hybrid firefly algorithms. Hybrid of firefly algorithm, particle swarm optimization and genetic algorithm achieves best throughput, sum power as well as objective function value.

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“…LSHA and POHA took advantage of the merits of ACS and IIWD, and can effectively handle CVRP, where in most cases, LSHA outperformed other three algorithms in the literature. Besides, CS has been hybrided with improved shuffled frog leaping algorithm [39], global harmony search algorithm [40], simulated annealing algorithm [41], FA [42] and so on.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Ant Colony and Cuckoo Search Algorithm for Route Optimization of Heating Engineering

et al. 2018

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The development of remote sensing and intelligent algorithms create an opportunity to include ad hoc technology in the heating route design area. In this paper, classification maps and heating route planning regulations are introduced to create the fitness function. Modifications of ant colony optimization and the cuckoo search algorithm, as well as a hybridization of the two algorithms, are proposed to solve the specific Zhuozhou–Fangshan heating route design. Compared to the fitness function value of the manual route (234.300), the best route selected by modified ant colony optimization (ACO) was 232.343, and the elapsed time for one solution was approximately 1.93 ms. Meanwhile, the best route selected by modified Cuckoo Search (CS) was 244.247, and the elapsed time for one solution was approximately 0.794 ms. The modified ant colony optimization algorithm can find the route with smaller fitness function value, while the modified cuckoo search algorithm can find the route overlapped to the manual selected route better. The modified cuckoo search algorithm runs more quickly but easily sticks into the premature convergence. Additionally, the best route selected by the hybrid ant colony and cuckoo search algorithm is the same as the modified ant colony optimization algorithm (232.343), but with higher efficiency and better stability.

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A new hybrid cuckoo search and firefly optimization

Cited by 19 publications

References 12 publications

Comparison of Hybrid Firefly Algorithms for Binary and Continuous Optimization Problems in a TV White Space Network

Comparison of Hybrid Firefly Algorithms for Binary and Continuous Optimization Problems in a TV White Space Network

Comparison of Hybrid Firefly Algorithms for Power Allocation in a TV White Space Network

A Hybrid Ant Colony and Cuckoo Search Algorithm for Route Optimization of Heating Engineering

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