Development and validation of different hybridization strategies between GA and PSO

Gandelli, A.; Grimaccia, Francesco; Mussetta, Marco; Pirinoli, Paola; Zich, R.E.

doi:10.1109/cec.2007.4424823

Cited by 59 publications

(56 citation statements)

References 14 publications

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“…The splitting of the population is done according to a hybridization coefficient (hc) [30], which expresses the part of the population that is evolved in each iteration with GA. In the proposed GSO algorithm, hc = 0 means the procedure is a pure PSO (the whole population is evolved according to PSO operators), hc = POPSIZE means pure GA (the whole population is evolved according to GA operators), while 0 < hc < POPSIZE means that hc individuals from the population is evolved by GA, while the rest with PSO technique.…”

Section: Gso-based Technique For Test Paths Generationmentioning

confidence: 99%

“…The Genetical Swarm Optimization (GSO) [30] is a hybrid evolutionary technique that exploit in the most effective way the uniqueness and peculiarities of the PSO and GAs. This algorithm is essentially, as PSO and GA, a population-based heuristic search technique, which can be used to solve combinatorial optimization problems, modeled on the concepts of natural selection and evolution (GA), but also based on cultural and social rules derived from the analysis of the swarm intelligence and from the interaction among particles (PSO).…”

Section: Introductionmentioning

confidence: 99%

“…47.83 % * COVERED DEF-USE PATHS: 1,3,5,6,7,8,10,17,20,21,23 Path 3 ************* SELECTED ************* * Traversed Path: 5,6,7,8,9,10,11,12,13,18,19,20,21,22,23,24,29,30,19,31,32, * DEF-USE COVERAGE:…”

mentioning

confidence: 99%

“…111011101110111 e0,e1,e2,e4,e6,e8,e9,e10,e12,e13,e5,e14, (Path 11) 6. 110111110100001 e0,e1,e3,e4,e6,e7,e9,e5,e14, (Path 12) Path 10 ************* SELECTED ************* * Traversed Path: 5,6,7,8,9,10,14,15,16,17,18,19,20,21,25,26,27,28,29,30,19,20 91.30 % * COVERED DEF-USE PATHS: 12 ** hc= 5 ** ** Apply GA to 5 individuals from the current population * Parent 1 (Individual 1): 110111000110101 e0,e1,e3,e4,e10,e12,e9,e5,e14, * Parent 2 (Individual 6): 110111110100001 e0,e1,e3,e4,e6,e7,e9,e5,e14, * Parent 3 (Individual 5): 111011101110111 e0,e1,e2,e4,e6,e8,e9,e10,e12,e13,e5,e14, * Parent 4 (Individual 4): 111011111110011 e0,e1,e2,e4,e6,e7,e9,e10,e8,e13,e5,e14, * Parent 5 (Individual 3): 110111000110101 e0,e1,e3,e4,e10,e12,e9,e5,e14, * Selected Cases to be Parents of New Population are: * Parent 1 (Individual 4): 111011111110011 e0,e1,e2,e4,e6,e7,e9,e10,e8,e13,e5,e14, * Parent 2 ( Individual 2): 110111110100001 e0,e1,e3,e4,e6,e7,e9,e5,e14, * Parent 3 (Individual 4): 111011111110011 e0,e1,e2,e4,e6,e7,e9,e10,e8,e13,e5,e14, * Parent 4 (Individual 4): 111011111110011 e0,e1,e2,e4,e6,e7,e9,e10,e8,e13,e5,e14, * Parent 5 (Individual 3): 111011101110111 e0,e1,e2,e4,e6,e8,e9,e10,e12,e13,e5,e14, ** Crossover * Selected Parents: 1, 2…”

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confidence: 99%

“…110111001110001 e0,e1,e3,e4,e10,e8,e9,e5,e14, (Path 5) 6. 110111001010011 e0,e1,e3,e4,e10,e8,e13,e5,e14, (Path 6) Path 1 ************* SELECTED ************* * Traversed Path: 5,6,7,8,9,10,14,15,16,17,18,19,20,21,22,23,24,29,30,19,31,32, * DEF-USE COVERAGE:…”

mentioning

confidence: 99%

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Automatic Data Flow Test Paths Generation using the Genetical Swarm Optimization Technique

Girgis¹,

Ghiduk²,

Abd-elkawy³

2015

IJCA

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Path testing requires generating all paths through the program to be tested, and finding a set of program inputs that will execute every path. The number of possible paths in programs containing loops is infinite, and so it is very difficult, if not impossible, to test all of them. Path testing can be relaxed by selecting a subset of all executable paths that fulfill a certain path selection criterion and finding test data to cover it. The automatic generation of such test paths leads to more test coverage paths thus resulting in efficient and effective testing strategy. This paper presents a genetical swarm optimization (GSO) based technique, which effectively combines a genetic algorithm (GA) based technique and a particle swarm optimization (PSO) based technique, for automatic generation of a set of test paths that cover the all-uses criterion. Experiments have been carried out to evaluate the effectiveness of the proposed GSO approach in test paths generation compared to the GA and PSO approaches.

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Section: Gso-based Technique For Test Paths Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

mentioning

confidence: 99%

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Automatic Data Flow Test Paths Generation using the Genetical Swarm Optimization Technique

Girgis¹,

Ghiduk²,

Abd-elkawy³

2015

IJCA

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A multi‐objective optimization approach for beam pattern synthesis ofUAVvirtual rectangular antenna array

Mei,

Guo,

Kang

et al. 2024

Int J Numerical Modelling

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Virtual antenna array (VAA) formed by unmanned aerial vehicle (UAV) antenna units using collaborative beamforming (CB) technology plays an important role in the air communication system, and can be used in radar, military, disaster rescue and other places. However, there are still some issues with the beam pattern formed by this method, such as high sidelobe level (SLL), high cost and low efficiency. In this article, each UAV carries an omnidirectional antenna unit, and a large number of UAVs form a UAV virtual rectangular antenna array (UVRAA) to communicate with the ground base station (BS). We formulate an overhead minimization and efficient communication multi‐objective optimization problem (OMECMOP) which jointly optimize the excitation current weights of the UVRAA and reduce the number of UAVs in operation to improve the beam pattern, enhance the communication efficiency and decrease the overhead of UVRAA. In addition, we also propose an improved multi‐objective multi‐verse optimization algorithm based on the inverse decline curve type (ISDT‐MOMVO) which introduces a strategy optimization initialization solution with quasi‐opposition based learning (QBL) and a hybrid solution updating operators to solve the OMECMOP. The simulation results show that compared with other traditional swarm intelligence (SI) optimization algorithms the ISDT‐MOMVO algorithm produces better beam pattern and the thinning rate can reach 50%.

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Energy-Efficient Resource Allocation in Data Centers Using a Hybrid Evolutionary Algorithm

Reddy

Gangadharan

Rao

et al. 2020

Machine Learning for Intelligent Decision Science

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Development and validation of different hybridization strategies between GA and PSO

Cited by 59 publications

References 14 publications

Automatic Data Flow Test Paths Generation using the Genetical Swarm Optimization Technique

Automatic Data Flow Test Paths Generation using the Genetical Swarm Optimization Technique

A multi‐objective optimization approach for beam pattern synthesis ofUAVvirtual rectangular antenna array

Energy-Efficient Resource Allocation in Data Centers Using a Hybrid Evolutionary Algorithm

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