Quantum behaved Particle Swarm Optimization (QPSO) for multi-objective design optimization of composite structures

Omkar, S. N.; Khandelwal, Rahul; Ananth, T. V. S.; Naik, G. Narayana; Gopalakrishnan, S.

doi:10.1016/j.eswa.2009.03.006

Cited by 173 publications

(97 citation statements)

References 30 publications

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“…The IPSO technique is suggested that deals with an inequality constraint treatment mechanism called as dynamic search-space squeezing strategy to accelerate the optimization process and simultaneously. Omkar et al proposed a novel multi-objective optimization algorithm developed on the basis of the Quantum behaved Particle Swarm Optimization (QPSO) paradigm [10]. Niknam et al proposed a hybrid evolutionary programming based clustering algorithm, called PSO-SA, by combining particle swarm optimization (PSO) and simulated annealing (SA) [11].…”

Section: Related Workmentioning

confidence: 99%

An Improved PSO Algorithm Based on SA and Quantum Theory and Its Application

Tan¹,

Dong²,

Li³

et al. 2015

IJDTA

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Section: Related Workmentioning

confidence: 99%

An Improved PSO Algorithm Based on SA and Quantum Theory and Its Application

Tan¹,

Dong²,

Li³

et al. 2015

IJDTA

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“…Calculate the fitness of every particle (y) = ( 1 (y), 2 (y)) according to (16) and (23); let pbest = y, (pbest) = (y), where pbest denotes the individual best position of y.…”

Section: Solvingmentioning

confidence: 99%

“…And the multiobjective quantum-behaved particle swarm optimization algorithm (MOQPSO) [14][15][16][17][18] is put forward. However, the requirement for solving a multiobjective optimization problem is different from solving a single objective optimization problem.…”

Section: Moqpso-d/s Algorithmmentioning

confidence: 99%

MOQPSO‐D/S for Air and Missile Defense WTA Problem under Uncertainty

Qing-hua

Zhenhao

2017

Mathematical Problems in Engineering

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Aiming at the shortcomings of single objective optimization for solving weapon target assignment (WTA) and the existing multiobjective optimization based WTA method having problems being applied in air and missile defense combat under uncertainty, a fuzzy multiobjective programming based WTA method was proposed to enhance the adaptability of WTA decision to the changes of battlefield situation. Firstly, a multiobjective quantum-behaved particle swarm optimization with double/single-well (MOQPSO-D/S) algorithm was proposed by adopting the double/single-well based position update method, the hybrid random mutation method, and the two-stage based guider particles selection method. Secondly, a fuzzy multiobjective programming WTA model was constructed with consideration of air and missile defense combat's characteristics. And, the uncertain WTA model was equivalently clarified based on the necessity degree principle of uncertainty theory. Thirdly, with particles encoding and illegal particles adjusting, the MOQPSO-D/S algorithm was adopted to solve the fuzzy multiobjective programming based WTA model. Finally, example simulation was conducted, and the result shows that the WTA model constructed is rational and MOQPSO-D/S algorithm is efficient.

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“…Compared with GA and other similar evolutionary techniques, PSO has some attractive characteristics and in many cases proved to be more effective (Hassan, Cohanim, Weck & Venter, 2005 [2]. This paper firstly establishes maximum entropy OD matrix calculation model.…”

Section: Introductionmentioning

confidence: 99%

“…A host of such biologically inspired evolutionary techniques have been developed namely Genetic Algorithm (GA) solving such optimization problems. All of these algorithms with their stochastic means are well equipped to handle such problems [2].…”

Section: Introductionmentioning

confidence: 99%