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

Chen, Gonggui; Yi, Xingting; Zhang, Zhizhong; Lei, Hangtian

doi:10.3390/en11123438

Cited by 26 publications

(18 citation statements)

References 40 publications

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“…The objective functions and system constraints of MOOPF mathematical model are summarized as follows [9], [25].…”

Section: Mathematical Model Of Moopf Problemsmentioning

confidence: 99%

“…The non-linear weight coefficient ωc, which can enhance the population diversity, is introduced to improve the speed model of SBA algorithm. The novel updated formulas of v and ωc are defined as (25) and (26).…”

Section: ) Improvement Of Speed Updating Mannermentioning

confidence: 99%

“…The results clearly show that the NHBA algorithm has better exploration ability than the common MOPSO and NSGA-III algorithms in finding more competitive BC solutions when utilizing the same Constrain-prior Pareto-dominance method (CPM). The detailed description of CPM can be found in literatures [24], [25].…”

Section: Introductionmentioning

confidence: 99%

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Applications of Novel Hybrid Bat Algorithm With Constrained Pareto Fuzzy Dominant Rule on Multi-Objective Optimal Power Flow Problems

et al. 2019

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To overcome the premature-convergence of standard bat algorithm in solving the multiobjective optimal power flow (MOOPF) problems, a novel hybrid bat algorithm (NHBA) is proposed in this paper. The suggested NHBA algorithm modifies the local search manner by a monotone random filling model based on extreme (MRFME) and improves the population-diversity by mutation and crossover mechanisms. To obtain the uniformly distributed Pareto optimal set (POS) with zero constraint-violation, an innovative non-dominated sorting method combined with the constrained Pareto fuzzy dominant (CPFD) strategy is put forward in this paper. To verify the superiority of the proposed NHBA-CPFD algorithm, which is federated by the NHBA algorithm and the CPFD strategy, ten MOOPF simulation cases considering the basic fuel cost, the fuel cost with value-point loadings, the total emission, and the active power loss are studied on the IEEE 30-node, IEEE 57-node, and IEEE 118-node systems. In contrast to the NHBA, MOPSO, and NSGA-III algorithms which adopt the constrain-prior Pareto-dominance method (CPM), numerous results validate the NHBA-CPFD algorithm that can achieve more superior compromise solutions and preferable Pareto fronts (PFs) even in the large-scale systems. Furthermore, two performance metrics of generational distance (GD) and hyper-volume (HV) also demonstrate that the NHBA-CPFD algorithm has great advantages to obtain the feasible POS with evenly distribution and favorable-diversity. INDEX TERMS Novel hybrid bat algorithm, multi-objective optimal power flow problem, constrained Pareto fuzzy dominant strategy, monotone random filling model based on extreme, performance metrics.

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“…The objective functions and system constraints of MOOPF mathematical model are summarized as follows [9], [25].…”

Section: Mathematical Model Of Moopf Problemsmentioning

confidence: 99%

Section: ) Improvement Of Speed Updating Mannermentioning

confidence: 99%

See 1 more Smart Citation

Applications of Novel Hybrid Bat Algorithm With Constrained Pareto Fuzzy Dominant Rule on Multi-Objective Optimal Power Flow Problems

et al. 2019

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“…The triangular probability distribution equality in Eq. (19) was used to decide on which frogs need to be selected in memetic evolution phase.…”

Section: Shuffled Frog Leaping Algorithm (Sfla)mentioning

confidence: 99%

“…We have already mentioned that several techniques are discussed about the matter of ORPD solution. However, the multi-objective solution techniques [19][20] are frequently observed in recent years besides single-objective methods [21]. Numbered study point out the NSGA-II procedures for solving ORPD.…”

Section: Introductionmentioning

confidence: 99%

New Metaheuristic Algorithms for Reactive Power Optimization

2019

Teh. vjesn.

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Optimal reactive power dispatch (ORPD) is significant regarding operating the practice safely and efficiently. The ORPD is beneficial to recover the voltage profile, diminish the losses and increase the voltage stability. The ORPD is a complicated optimization issue in which the total active power loss is reduced by detecting the powersystem control variables, like generator voltages, tap ratios of tap-changer transformers, and requited reactive power, ideally. This study offers new approaches based on Shuffled Frog Leaping Algorithm (SFLA) and Tree Seed Algorithm (TSA) to solve the best ORPD. The results of the approaches are offered set against the current results studied in the literature. The recommended algorithms were tested by IEEE-30 and IEEE-118 bus systems to discover the optimal reactive power control variables. It was observed that the obtained results are more successful than the other algorithms.

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Multi-objective coyote optimization algorithm based on hybrid elite framework and Meta-Lamarckian learning strategy for optimal power flow problem

Zhu,

Wang,

Zhang

et al. 2024

Artif Intell Rev

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A multi-objective coyote optimization algorithm based on hybrid elite framework and Meta-Lamarckian learning strategy (MOCOA-ML) was proposed to solve the optimal power flow (OPF) problem. MOCOA-ML adds external archives with grid mechanism on the basis of elite non-dominated sorting. It can guarantee the diversity of the population while obtaining the Pareto solution set. When selecting elite coyotes, there is a greater probability to select the elite in sparse areas, which is conducive to the development of sparse areas. In addition, combined with Meta-Lamarckian learning strategy, based on four crossover operators (horizontal crossover operator, longitudinal crossover operator, elite crossover operator and direct crossover operator), the local search method is adaptively selected for optimization, and its convergence performance is improved. First, the simulation is carried out in 20 test functions, and compared with MODA, MOPSO, MOJAYA, NSGA-II, MOEA/D, MOAOS and MOTEO. The experimental results showed that MOCOA-ML achieved the best inverted generational distance value and the best hypervolume value in 11 and 13 test functions, respectively. Then, MOCOA-ML is used to solve the optimal power flow problem. Taking the fuel cost, power loss and total emissions as objective functions, the tests of two-objective and three-objective bechmark problems are carried out on IEEE 30-bus system and IEEE 57-bus system. The results are compared with MOPSO, MOGWO and MSSA algorithms. The experimental results of OPF demonstrate that MOCOA-ML can find competitive solutions and ranks first in six cases. It also shows that the proposed method has obtained a satisfactory uniform Pareto front.

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

Cited by 26 publications

References 40 publications

Applications of Novel Hybrid Bat Algorithm With Constrained Pareto Fuzzy Dominant Rule on Multi-Objective Optimal Power Flow Problems

Applications of Novel Hybrid Bat Algorithm With Constrained Pareto Fuzzy Dominant Rule on Multi-Objective Optimal Power Flow Problems

New Metaheuristic Algorithms for Reactive Power Optimization

Multi-objective coyote optimization algorithm based on hybrid elite framework and Meta-Lamarckian learning strategy for optimal power flow problem

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