Multi-objective quantum-behaved particle swarm optimization algorithm with double-potential well and share-learning

Xu, Suhui; Mu, Xiaodong; Chai, Douglas; Zhao, Peng

doi:10.1016/j.ijleo.2016.02.049

Cited by 14 publications

(11 citation statements)

References 12 publications

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“…The calculation method of the crowding distance for each particle in external archive can be referred to literature [19]. The calculation method of sigma value [20] for each particle is shown as in (14), and the detail of guider particle selection by adopting sigma value method is shown in Figure 1. When the particle position is updated based on QPSO with double-well, the second guider particle should be selected.…”

Section: Two-stage Based Guider Particles Selection Methodmentioning

confidence: 99%

“…Based on the doublepotential well quantum model, a QPSO with double-well algorithm is proposed by Xu et al [14] to resolve the problem of population diversity decline due to the fast convergence of QPSO algorithm. In this algorithm, the motion of particles is simulated as in a double-well space.…”

Section: Qpso With Double-well Algorithmmentioning

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%

“…The multiobjective quantumbehaved particle swarm optimization algorithm with doublepotential well and share-learning (MOQPSO-DPS) [14], the MOQPSO-CD [17], and the multiobjective particle swarm optimization algorithm based on crowding distance sorting (MOPSO-CD) [23] are chosen as the comparative algorithms. Set the particle population to be 100, and set the iteration number to be 200.…”

Section: Verify the Performance Of Moqpso-d/s For Solving Multiobjectmentioning

confidence: 99%

“…Select the solution of one time from the 30 times of every algorithm for solving test functions ZDT1∼ZDT4 randomly, and the results are shown in Figure 4. Error ration (ER) [14] and spacing metric (SP) [24] are chosen to weigh the convergence of four algorithms for solving the test functions and the distribution uniformity of the Pareto optimal solution, respectively. And, the mean running time is chosen to weigh the operating rate.…”

Section: Verify the Performance Of Moqpso-d/s For Solving Multiobjectmentioning

confidence: 99%

See 4 more Smart Citations

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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Section: Two-stage Based Guider Particles Selection Methodmentioning

confidence: 99%

Section: Qpso With Double-well Algorithmmentioning

confidence: 99%

Section: Moqpso-d/s Algorithmmentioning

confidence: 99%

Section: Verify the Performance Of Moqpso-d/s For Solving Multiobjectmentioning

confidence: 99%

Section: Verify the Performance Of Moqpso-d/s For Solving Multiobjectmentioning

confidence: 99%

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

Qing-hua

Zhenhao

2017

Mathematical Problems in Engineering

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Multiobjective optimization of area‐to‐point heat conduction structure using binary quantum‐behaved PSO and Tchebycheff decomposition method

et al. 2020

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A multiobjective optimization of area‐to‐point heat conduction to minimize both mean temperature and temperature variance is conducted based on a decomposition‐based multiobjective binary quantum‐behaved particle swarm optimization (PSO) method (MOMBQPSO/D). The MOMBQPSO/D adopts the framework of the multiobjective evolutionary algorithm based on decomposition and modifies the binary quantum‐behaved PSO. In the first step of the MOMBQPSO/D, the multiobjective area‐to‐point problem is divided into a series of subproblems using Tchebycheff decomposition method. Next, all the subproblems are solved simultaneously using the modified binary quantum‐behaved PSO. Finally, a series of Pareto optimal solutions representing the conducting path structures are stepwise selected from the solutions to the subproblems. The features of the Pareto optimality‐based conducting paths and cooling performance are described. In addition, the effects of the conductive material quantity, optimization objective, heat sink location, and heat source distribution on the conducting path structure and cooling performance are discussed.

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Interval optimization of the day-ahead clearing schedule considering the real-time imbalance power with wind power integration

Jiang

Chen

Bu-ying

2018

Int Trans Electr Energ Syst

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Summary Large‐scale wind power penetration impacts the electricity market in many aspects, especially the day‐ahead clearing schedule, due to the uncertainty and low predictability of wind power. Based on forecast intervals of wind power and absorption of wind power with a preference, the day‐ahead clearing model of energy and reserve is built and contains 2 objective functions: the minimum clearing cost and real‐time imbalance power. The constraints for the clearing schedule and for meeting the uncertainty of wind power in forecast intervals are included. To supply market operators with upper and lower boundary information, the interval optimization model is converted into double‐layer nonlinear pessimistic and optimistic models. The improved multiobjective quantum particle swarm optimization (MOQPSO) with a “straight Pareto front” is developed to approximate the real, smooth, and uniform Pareto front. The day‐ahead clearing schedule and corresponding reserve capacity are analyzed by testing IEEE30‐bus system. The computational times of the improved MOQPSO and general MOQPSO are nearly the same, but the Pareto front is remarkably reformative. Furthermore, the upward/downward energy price in the real‐time market can considerably impact the day‐ahead clearing schedule.

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Multi-objective quantum-behaved particle swarm optimization algorithm with double-potential well and share-learning

Cited by 14 publications

References 12 publications

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

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

Multiobjective optimization of area‐to‐point heat conduction structure using binary quantum‐behaved PSO and Tchebycheff decomposition method

Interval optimization of the day-ahead clearing schedule considering the real-time imbalance power with wind power integration

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