Improved particle swarm optimisation for multi-objective optimal power flow considering the cost, loss, emission and voltage stability index

Niknam, Taher; Narimani, Mohammad Rasoul; Aghaei, Jamshid; Azizipanah‐Abarghooee, Rasoul

doi:10.1049/iet-gtd.2011.0851

Cited by 252 publications

(138 citation statements)

References 35 publications

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“…The PSO algorithm incorporated a non-uniform mutation rate, as used in the GA optimiser. Previous studies have shown that the addition of a mutation technique can help the search process of the PSO algorithm [29][30][31], since the mutation function prevents premature convergence, a problem to which PSO is particularly susceptible.…”

Section: Particle Swarm Optimisationmentioning

confidence: 99%

The Development and Application of an Evolutionary Algorithm for the Determination of Kinetic Parameters in Automotive Aftertreatment Models

Pedlow

McCullough

Goguet

et al. 2018

Emiss. Control Sci. Technol.

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Accurate mathematical models are an essential tool in the development of aftertreatment systems, as they can provide detailed information on the impact of design changes while simultaneously reducing development costs and the time between the initiation and completion of a catalytic process development. Identifying the set of kinetic parameters that achieves a perceived acceptable level of accuracy may require significant time and effort. Optimisation techniques can be used to speed up the tuning process; however, these techniques can require a large computation time, and may not produce a satisfactory answer. This invariably leads to questioning regarding the accuracy of such automated approaches. In this paper, the performance of a number of optimisation algorithms including a GA, nPSO and a hybrid algorithm has been explored with respect to their applicability to kinetic parameters optimisation in the context of aftertreatment modelling. The focus is therefore on the assessment of the optimisers' performance, rather than the characterisation of catalytic reactions. The different algorithms were applied to the optimisation of parameters in a number of mathematical functions and theoretical aftertreatment systems. The optimisation algorithms were tested on theoretical aftertreatment systems since these have known absolute solutions thereby allowing the optimisers' performance to be assessed in the absence of any other external source of inaccuracy such as model structure and experimental error. The results obtained demonstrate that such optimisation approaches facilitate the determination of kinetic parameters with suitable accuracy. The proposed hybrid optimisation algorithm achieved excellent performance in considerably shorter computation time than the GA or nPSO optimisers.

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Section: Particle Swarm Optimisationmentioning

confidence: 99%

The Development and Application of an Evolutionary Algorithm for the Determination of Kinetic Parameters in Automotive Aftertreatment Models

Pedlow

McCullough

Goguet

et al. 2018

Emiss. Control Sci. Technol.

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“…Furthermore, the fuel cost F ele1 and power loss F ele2 are also treated separately for the following two reasons. For one thing, the multi-objective optimization of optimal power flow has treated the fuel cost and power loss separately to optimize the power system comprehensively and obtain the compromise solutions, according to [13,29]. For the other, although the two objectives are both economic factors, they might represent the benefits of different companies.…”

Section: Optimal Coordinated Operation Of Iesmentioning

confidence: 99%

Many-objective optimization for coordinated operation of integrated electricity and gas network

Kou

Zheng

2017

J. Mod. Power Syst. Clean Energy

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This paper develops a many-objective optimization model, which contains objectives representing the interests of the electricity and gas networks, as well as the distributed district heating and cooling units, to coordinate the benefits of all parties participated in the integrated energy system (IES). In order to solve the many-objective optimization model efficiently, an improved objective reduction (IOR) approach is proposed, aiming at acquiring the smallest set of objectives. The IOR approach utilizes the Spearman's rank correlation coefficient to measure the relationship between objectives based on the Pareto-optimal front captured by the multi-objective group search optimizer with adaptive covariance and Lévy flights algorithm, and adopts various strategies to reduce the number of objectives gradually. Simulation studies are conducted on an IES consisting of a modified IEEE 30-bus electricity network and a 15-node gas network. The results show that the many-objective optimization problem is transformed into a bi-objective formulation by the IOR. Furthermore, our approach improves the overall quality of dispatch solutions and alleviates the decision making burden.

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“…The above features are provided by applying the multi-population cooperation and vector decomposing strategy that increase the search ability of the CMOABC by coevolving the smaller and simpler subpopulations according to the dynamically vector space decomposing strategy and the non-dominated sorting approach. Unlike the singleobjective optimization, two goals are emphasized in a multiobjective optimization: (1) convergence to the Pareto-optimal set and (2) maintaining the diversity in Pareto-optimal solution (Niknam et al, 2012). The approach of multi-populations optimizing in parallel integrated by the proposed method can meet the goals of the multi-objective optimization, which is suitable for multi-objective problems.…”

Section: Two-objective Casesmentioning

confidence: 99%

Cooperative artificial bee colony algorithm for multi-objective RFID network planning

Zhu

et al. 2014

Journal of Network and Computer Applications

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Improved particle swarm optimisation for multi-objective optimal power flow considering the cost, loss, emission and voltage stability index

Cited by 252 publications

References 35 publications

The Development and Application of an Evolutionary Algorithm for the Determination of Kinetic Parameters in Automotive Aftertreatment Models

The Development and Application of an Evolutionary Algorithm for the Determination of Kinetic Parameters in Automotive Aftertreatment Models

Many-objective optimization for coordinated operation of integrated electricity and gas network

Cooperative artificial bee colony algorithm for multi-objective RFID network planning

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