Example-based learning particle swarm optimization for continuous optimization

Huang, Han; Qin, Hu; Zhang, Hao; Lim, Andrew

doi:10.1016/j.ins.2010.10.018

Cited by 100 publications

(33 citation statements)

References 17 publications

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“…Although the tranditioal PSO has already been applied successfully in many application areas [14][15][16][17], it makes slow progress in keeping the balance between exploration and exploitation in dynamic environment is slow [18][19][20]. This is because that the tranditioal PSO cannot adapt to the changing environment and converge to an optimum in an early iteration [14].…”

Section: Particle Swarm and Local Stochastic Strategymentioning

confidence: 99%

“…Similar to EAs, particle swarm optimization (PSO), inspired by the simulation of social behaviors of biological population such as fishes and birds, is also an iterative and population-based optimization technique [13]. As it is easy-to-implement and has robust adaptability, PSO is potential way to frame the QoS optimization w. r. t. cooperation evolution [14][15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

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A Cooperative Evolution for QoS-driven IoT Service Composition

Jin

Chen

et al. 2013

Automatika

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Original scientific paperTo facilitate the automation process in the Internet of Things, the research issue of distinguishing prospective services out of many "similar" services, and identifying needed services w.r.t the criteria of Quality of Service (QoS), becomes very important. To address this aim, we propose heuristic optimization, as a robust and efficient approach for solving complex real world problems. Accordingly, this paper devises a cooperative evolution approach for service composition under the restrictions of QoS. A series of effective strategies are presented for this problem, which include an enhanced local best first strategy and a global best strategy that introduces perturbations. Simulation traces collected from real measurements are used for evaluating the proposed algorithms under different service composition scales that indicate that the proposed cooperative evolution approach conducts highly efficient search with stability and rapid convergence. The proposed algorithm also makes a well-designed trade-off between the population diversity and the selection pressure when the service compositions occur on a large scale.Key words: Cooperative evolution, IOT service composition, Quality of service Kooperativna evolucija za kvalitetno pružanje usluga u paradigmi Interneta stvari. Kako bi se automatizirali procesi u internetu stvati, nužno je rezlikovati bitne usluge u moru sličnih kao i identificirati potrebne usluge u pogledu kvalitete usluge (QoS). Kako bi doskočili ovome problemu prdlaže se heuristička optimizacija kao robustan i efikasan način rješavajne kompleksnih problema. Nadalje, učlanku je predložen postupak kooperativne evolucije za slaganje usluga uz ograničenja u pogledu kvalutete usluge. Predstavljen je niz efektivnih strategija za spomenuti problem uključujući strategije najboljeg prvog i najboljeg globalnog koje unose perturbacije u polazni problem. Simulacijski rezultati kao i stvarni podatci su korišteni u svrhu evaluacije prodloženog algoritma kako bi se osigurala efikasna pretraga uz stabilnost i brzu konvergenciju. Predloženi algoritam takoer vodi računa o odnosu izmeu različitosti populacije i selekcijskog pritiska kada je potrebno osigurati slaganje usluga na velikoj skali.Ključne riječi: kooperativna evolucija, Internet stvari pružanje usluge, kvaliteta usluge

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Section: Particle Swarm and Local Stochastic Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Cooperative Evolution for QoS-driven IoT Service Composition

Jin

Chen

et al. 2013

Automatika

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“…Ant Colony Optimization (ACO) [2], Particle Swarm Optimization (PSO) [3], Bee Colony Optimization (BCO) [4] and Bees Algorithm (BA) [1] are the most familiar approaches belonging to swarm-based group of meta-heuristics. Many recent variants of these algorithms were used to solve continuous optimization problems by researchers [5][6][7][8][9][10][11][12][13][14][15]. In this study, we attempt to solve continuous optimization problems via a multiple colony bees algorithm, each colony inspired by real honey bees and represents a hive, in which different bees live and constitute a population.…”

Section: Introductionmentioning

confidence: 99%

Multiple colony bees algorithm for continuous spaces

Akpınar

Baykasoğlu

2014

Applied Soft Computing

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“…They include ant colony optimization (Socha & Dorigo, 2008), artificial bee colony algorithm (Akay & Karaboga, 2010;Kang et al, 2011), evolution strategies (ES) (Beyer, 2001), differential evolution (Das & Suganthan, 2011;Dasgupta et al, 2009;Kukkonen & Lampinen, 2004;Mezura-Montes et al, 2010;Noman & Iba, 2005;Rönkkönen et al, 2005;Storn & Price, 1997;Zhang et al, 2008), particle swarm optimization (Chen et al, 2007;Huang et al, 2010;Juang et al, 2011;Krohling & Coelho, 2006;l. Sun et al, 2011), and so on.…”

Section: Introductionmentioning

confidence: 99%