Solving the economic dispatch problem with a modified quantum-behaved particle swarm optimization method

Sun, Jun; Fang, Wei; Wang, Daojun; Xu, Wenbo

doi:10.1016/j.enconman.2009.07.015

Cited by 70 publications

(37 citation statements)

References 18 publications

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“…QPSO outperforms PSO in global search ability and is a promising optimizer for complex problems [35][36][37]. QPSO demonstrates its superiority in solving ED problems comparing to other population-based optimization algorithms [38,39]. Although various QPSO approaches have been successful in solving ED problems as reported in literature, they still lack the efficient mechanism to treat the constraints effectively [39].…”

Section: Introductionmentioning

confidence: 99%

“…QPSO demonstrates its superiority in solving ED problems comparing to other population-based optimization algorithms [38,39]. Although various QPSO approaches have been successful in solving ED problems as reported in literature, they still lack the efficient mechanism to treat the constraints effectively [39]. The most commonly used method to handle constraints in ED problem with QPSO is the use of penalty functions [39,40].…”

Section: Introductionmentioning

confidence: 99%

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An Elitist Transposon Quantum‐Based Particle Swarm Optimization Algorithm for Economic Dispatch Problems

Yang

2018

Complexity

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Population-based optimization algorithms are useful tools in solving engineering problems. This paper presents an elitist transposon quantum-based particle swarm algorithm to solve economic dispatch (ED) problems. It is a complex and highly nonlinear constrained optimization problem. The proposed approach, double elitist breeding quantum-based particle swarm optimization (DEB-QPSO), makes use of two elitist breeding strategies to promote the diversity of the swarm so as to enhance the global search ability and an improved efficient heuristic handling technique to manage the equality and inequality constraints of ED problems. Investigating on 15-unit, 40-unit, and 140-unit widely used test systems, through performance comparison, the proposed DEB-QPSO algorithm is able to obtain higher-quality solutions efficiently and stably superior than the other the state-of-the-art algorithms.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Elitist Transposon Quantum‐Based Particle Swarm Optimization Algorithm for Economic Dispatch Problems

Yang

2018

Complexity

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“…Literature [5] presented an improved quantum particle swarm optimization algorithm to solve the problem of production planning optimization, convergence instability problems often occur but when solving the optimization. Literature [6] presented an improved quantum particle swarm algorithm, and has a faster convergence speed and robustness. This paper takes a machine tool plant bed production planning as the research object, established a production plan model, and applies the improved simulated annealing algorithm to solve, so as to effectively improve the efficiency of system simulation.…”

Section: Introductionmentioning

confidence: 99%

Research of Production Scheduling Based on Theory of Constraints

Zhang¹,

Du²

2015

Proceedings of the 2015 International Conference on Electrical, Automation and Mechanical Engineering

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“…Recently, quantum-behaved PSO approaches, inspired by the fundamental theory of particle swarm and features of quantum 0885-8950/$26.00 © 2010 IEEE mechanics, have been developed [33]- [35]. This paper proposes a quantum-inspired BPSO (QBPSO) which is based on the concept and principles of quantum computing such as a quantum bit and superposition of states to enhance the performance of the conventional BPSO.…”

Section: Introductionmentioning

confidence: 99%

A New Quantum-Inspired Binary PSO: Application to Unit Commitment Problems for Power Systems

Jeong

Park

Jang

et al. 2010

IEEE Trans. Power Syst.

214

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Abstract-This paper proposes a new binary particle swarm optimization (BPSO) approach inspired by quantum computing, namely quantum-inspired BPSO (QBPSO). Although BPSO-based approaches have been successfully applied to the combinatorial optimization problems in various fields, the BPSO algorithm has some drawbacks such as premature convergence when handling heavily constrained problems. The proposed QBPSO combines the conventional BPSO with the concept and principles of quantum computing such as a quantum bit and superposition of states. The QBPSO adopts a Q-bit individual for the probabilistic representation, which replaces the velocity update procedure in the particle swarm optimization. To improve the search capability of the quantum computing, this paper also proposes a new rotation gate, that is, a coordinate rotation gate for updating Q-bit individuals combined with a dynamic rotation angle for determining the magnitude of rotation angle. The proposed QBPSO is applied to unit commitment (UC) problems for power systems which are composed of up to 100-units with 24-h demand horizon. Index Terms-Binary particle swarm optimization, combinatorial optimization, constraint treatment technique, quantum computing, quantum evolutionary algorithm, unit commitment.

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Solving the economic dispatch problem with a modified quantum-behaved particle swarm optimization method

Cited by 70 publications

References 18 publications

An Elitist Transposon Quantum‐Based Particle Swarm Optimization Algorithm for Economic Dispatch Problems

An Elitist Transposon Quantum‐Based Particle Swarm Optimization Algorithm for Economic Dispatch Problems

Research of Production Scheduling Based on Theory of Constraints

A New Quantum-Inspired Binary PSO: Application to Unit Commitment Problems for Power Systems

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