A modified MOEA/D approach to the solution of multi-objective optimal power flow problem

Zhang, Jingrui; Tang, Qi‐Zhu; Li, Po; Deng, Daxiang; Chen, Yalin

doi:10.1016/j.asoc.2016.06.022

Cited by 117 publications

(54 citation statements)

References 57 publications

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“…For the purpose of careful analysis and in-depth comparison, a comparison of objectives along with BCSs obtained by MOFA-CPA, MOFA-PFA, MOEA/D [13], NSGA-II [13], MOACSA [30], MODE [30], MOHS [31], NSGA-II [31], MOABC/D [32], MOTLA/D [32], NS_CPSO [33], SWTC_NSPSO [33], MO-DEA [34], and ICA [35] for Case 1 is given in Table 6. As shown, the optimal BCS obtained by MOFA-CPA is 833.63 $/h and 5.0005 MW, which is neither too low like MODE, MOABC/D, MOTLA/D, and MO-DEA algorithms' loss, nor too high like MOFA-PFA, MOEA/D, MOACSA, NS_CPSO, SWTC_NSPSO, and ICA algorithms' cost, although they can get the lower loss.…”

Section: Analysis Of the Bcss For Cases 1-5mentioning

confidence: 99%

“…The CPA method successfully overcomes the drawback of the traditional PFA and ensures the obtained PFs fully satisfy the inequality constraints on dependent variables. Furthermore, a comparison of objectives along with BCSs obtained by MOFA-CPA, MOFA-PFA, MOEA/D [13], MOPSO [13], WA [36], and MOBA [36] for Case 4 is given in Table 7. From this table, we can notice that the MOFA-CPA can obtain better BCS in solving the tri-objective MOOPF problem, which clearly proves MOFA-CPA gives better BCS in terms of a compromise compared to other algorithms.…”

Section: Analysis Of the Bcss For Cases 1-5mentioning

confidence: 99%

“…Multi-objective evolutionary algorithms (MOEAs), multi-objective swarm optimization algorithms, and multi-objective meta-heuristic algorithms have been suggested and utilized to solve multi-objective problems (MOPs) effectively [12]. For example, in [13], the authors proposed a modified MOEA/D algorithm with repair strategy and penalty function for constraints processing to solve the MOOPF problem. A tri-objective differential evolution (DE) approach was carried out for the TOP problem contains all global optima of the MMOP in [14].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Solving the Multi-Objective Optimal Power Flow Problem Using the Multi-Objective Firefly Algorithm with a Constraints-Prior Pareto-Domination Approach

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Zhang

et al. 2018

Energies

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Known as a multi-objective, large-scale, and complicated optimization problem, the multi-objective optimal power flow (MOOPF) problem tends to be introduced with many constraints. In this paper, compared with the frequently-used penalty function-based method (PFA), a novel constraint processing approach named the constraints-prior Pareto-domination approach (CPA) is proposed for ensuring non-violation of various inequality constraints on dependent variables by introducing the Pareto-domination principle based on the sum of constraint violations. Moreover, for solving the constrained MOOPF problem, the multi-objective firefly algorithm with CPA (MOFA-CPA) is proposed and some optimization strategies, such as the crowding distance calculation and non-dominated sorting based on the presented CPA, are utilized to sustain well-distributed Pareto front (PF). Finally, in order to demonstrate the feasible and effective improvement of MOFA-CPA, a comparison study between MOFA-CPA and MOFA-PFA is performed on two test systems, including three bi-objective optimization cases and three tri-objective optimization cases. The simulation results demonstrate the capability of the MOFA-CPA for obtaining PF with good distribution and superiority of the proposed CPA for dealing with inequality constraints on dependent variables. In addition, some quality indicators are used to evaluate the convergence, distribution, and uniformity of the PFs found by the MOFA-CPA and MOFA-PFA.

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Section: Analysis Of the Bcss For Cases 1-5mentioning

confidence: 99%

Section: Analysis Of the Bcss For Cases 1-5mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Solving the Multi-Objective Optimal Power Flow Problem Using the Multi-Objective Firefly Algorithm with a Constraints-Prior Pareto-Domination Approach

Chen

Zhang

et al. 2018

Energies

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“…The performance of the proposed approach is validated through 12 well-known MOO benchmark test functions and a real-world engineering problem. For rigorous verification, the performance of the proposed approach is compared with those of four well-established MOO approaches, namely NSGA-II [4], TV-MOPSO [18], BMOPSO [30] and MOEA/D [31]. The performance comparison is based on five different MOO performance metrics.…”

Section: Introductionmentioning

confidence: 99%

A framework for multi-objective optimisation based on a new self-adaptive particle swarm optimisation algorithm

Tang

Zhu

Shin

et al. 2017

Information Sciences

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This paper develops a particle swarm optimisation (PSO) based framework for multi-objective optimisation (MOO). As a part of development, a new PSO method, named self-adaptive PSO (SAPSO), is first proposed. Since the convergence of SAPSO determines the quality of the obtained Pareto front, this paper analytically investigates the convergence of SAPSO and provides a parameter selection principle that guarantees the convergence. Leveraging the proposed SAPSO, this paper then designs a SAPSO-based MOO framework, named SAMOPSO. To gain a welldistributed Pareto front, we also design an external repository that keeps the non-dominated solutions. Next, a circular sorting method, which is integrated with the elitist-preserving approach, is designed to update the external repository in the developed MOO framework. The performance of the SAMOPSO framework is validated through 12 benchmark test functions and a real-word MOO problem. For rigorous validation, the performance of the proposed framework is compared with those of four well-known MOO algorithms. The simulation results confirm that the proposed SAMOPSO outperforms its contenders with respect to the quality of the Pareto front over the majority of the studied cases. The non-parametric comparison results reveal that the proposed method is significantly better than the four algorithms compared at the confidence level of 90% over the 12 test functions.

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“…Zheng and Liao [5] improved particle swarm algorithms, which could be applied to many other parameter identification and optimization problems. Zhang et al [6] presented a modified multiobjective evolutionary algorithm based on the decomposition approach to solve an optimal power flow problem with multiple and competing objectives. Lee et al [7] proposed a Web services-based MDO framework that enabled the synthesis of available disciplinary and cross-disciplinary resources for MDO via the Globus Toolkit.…”

Section: Related Workmentioning

confidence: 99%

A Systematic Optimization Design Method for Complex Mechatronic Products Design and Development

Jiang

Ding

Zhang

et al. 2018

Mathematical Problems in Engineering

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Designing a complex mechatronic product involves multiple design variables, objectives, constraints, and evaluation criteria as well as their nonlinearly coupled relationships. The design space can be very big consisting of many functional design parameters, structural design parameters, and behavioral design (or running performances) parameters. Given a big design space and inexplicit relations among them, how to design a product optimally in an optimization design process is a challenging research problem. In this paper, we propose a systematic optimization design method based on design space reduction and surrogate modelling techniques. This method firstly identifies key design parameters from a very big design space to reduce the design space, secondly uses the identified key design parameters to establish a system surrogate model based on data-driven modelling principles for optimization design, and thirdly utilizes the multiobjective optimization techniques to achieve an optimal design of a product in the reduced design space. This method has been tested with a high-speed train design. With comparison to others, the research results show that this method is practical and useful for optimally designing complex mechatronic products.

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A modified MOEA/D approach to the solution of multi-objective optimal power flow problem

Cited by 117 publications

References 57 publications

Solving the Multi-Objective Optimal Power Flow Problem Using the Multi-Objective Firefly Algorithm with a Constraints-Prior Pareto-Domination Approach

Solving the Multi-Objective Optimal Power Flow Problem Using the Multi-Objective Firefly Algorithm with a Constraints-Prior Pareto-Domination Approach

A framework for multi-objective optimisation based on a new self-adaptive particle swarm optimisation algorithm

A Systematic Optimization Design Method for Complex Mechatronic Products Design and Development

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