Biogeography Based Optimization Approach for Solving Optimal Power Flow Problem

Herbadji, Ouafa; Slimani, Linda; Bouktir, T.

doi:10.14257/ijhit.2013.6.5.17

Cited by 8 publications

(10 citation statements)

References 9 publications

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“…The results of this comparison, which are given in Table , indicate that the proposed approach is superior to those reported in the literature for the same test system. Stochastic fractal search has achieved 0.229% and 0.045% less emission level reduction than those obtained using of BBO and ABC, respectively. Case: Minimization of both generating cost and emission level …”

Section: Resultsmentioning

confidence: 87%

“…The optimal setting values of control parameter of the emission index minimization are given in Table . The results attained from the proposed SFS algorithm have been compared with other 2 methods in the literature . The results of this comparison, which are given in Table , indicate that the proposed approach is superior to those reported in the literature for the same test system.…”

Section: Resultsmentioning

confidence: 94%

“…The results attained from the proposed SFS algorithm have been compared with other 2 methods in the literature. 64,65 The results of this comparison, which are given in Table 19, indicate that the proposed approach is superior to those reported in the literature for the same test system. Stochastic fractal search has achieved 0.229% and 0.045% less emission level reduction than those obtained using of BBO 64 and ABC, 65 respectively.…”

Section: Transmission-constrained Edmentioning

confidence: 83%

“…The third scenario uses the standard IEEE 30‐bus power system. For this system, results obtained by SFS will be compared with the results obtained using GSA, BBO, modified differential evolution (MDE), improved GA, evolving ant direction differential evolution (EADDE), evolving ant direction hybrid differential evolution (EADHDE), gradient method, BBO, ABC, improved PSO, and nondominated sorting methodology with PSO (NDSPSO) . The following 3 case studies will be investigated under this scenario: Minimization of the total generation operating cost only, Minimization of the total emission level only, and Minimization of both the total generation operating cost and the total emission level. …”

Section: Resultsmentioning

confidence: 99%

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Environmental-economic dispatch using stochastic fractal search algorithm

Alomoush

Oweis²

2018

Int Trans Electr Energ Syst

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Summary Stochastic fractal search (SFS) is one of the latest metaheuristic global optimization algorithms, which has been introduced in 2015. It is a very promising algorithm and outperforms many of existing well‐known metaheuristic algorithms. This paper uses SFS algorithm to solve the highly nonlinear environmental‐economic dispatch problem in power systems operations considering generator physical constraints, gas emission level, and transmission line losses and limits. To verify the effectiveness of using this algorithm, it has been applied in this paper to different commonly used test systems. The results are compared with previous results obtained by 8 different methods such as genetic algorithm–based methods, particle swarm optimization–based methods, differential evolution–based methods, and gravitational search algorithm. Results will reveal that the optimal SFS‐based optimal solution can reduce the total system production cost and will also show that the optimal solutions obtained using the SFS algorithm perform better than many of the commonly used global optimization techniques, where the proposed algorithm has achieved a nearer global optimal solution compared to the other approaches with a short time.

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Section: Resultsmentioning

confidence: 87%

Section: Resultsmentioning

confidence: 94%

Section: Transmission-constrained Edmentioning

confidence: 83%

Section: Resultsmentioning

confidence: 99%

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Environmental-economic dispatch using stochastic fractal search algorithm

Alomoush

Oweis²

2018

Int Trans Electr Energ Syst

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“…However, from the literature, it can be concluded that, these techniques could not solve the complex objective functions which are not differentiable, mostly with complicated constraints. To solve these, many evolutionary algorithms have been proposed and implemented in the field of power system to solve different OPF problems, such as genetic algorithm (GA) [8][9][10], particle swarm optimization (PSO) [11][12][13], artificial bee colony (ABC) algorithm [14][15], harmony search algorithm (HSA) [16][17]26], differential evolution (DE) [18][19], biogeography based optimization (BBO) [20][21][22][23], bacterial swarm algorithm (BSA) [24], modified shuffle frog leaping algorithm (MSLFA) [25], gravitational search algorithm (GSA) [27][28] etc. ABC method was employed by Adaryani et al [14] to solve the OPF problem considering both equality and inequality constraints in an electric power system.…”

Section: Introductionmentioning

confidence: 99%

Multi objective optimal power flow using NSMOGSA

Bhowmik

Chakraborty

Babu

2014

2014 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2014]

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Application of non dominated sorting multi objective gravitational search algorithm (NSMOGSA) for solution of different optimal power flow (OPF) problem is presented in this article. NSMOGSA is an extension of the gravitational search algorithm (GSA), where the gravitational acceleration is updated using non-dominated sorting concept. A mixture of sign and reordering mutations are also proposed in to reduce the chance of trapping in local optima. The proposed method is employed for optimal adjustments of the power system control variables which involve both continuous and discrete variables of the OPF problem. To show the effectiveness of the proposed algorithm, it has been tested on a standard IEEE 30bus system with two different individual objectives that reflect total emission cost minimization and fuel cost minimization. The performance of NSMOGSA is compared with the results found by other meta-heuristic techniques reported in the recent literature to demonstrate the tangible superiority of the proposed method.Keywords-fuel cost minimization; multi objective optimal power flow; NSMOGSA; total emission cost minimization.I.

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Design of non-uniform concentric circular antenna arrays with optimal sidelobe level reduction using biogeography-based optimization

Dib

Sharaqa

2014

Int. j. microw. wirel. technol.

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Design of non-uniform concentric circular antenna arrays with optimal sidelobe level reduction using biogeography-based optimization nihad dib 1 and ashraf sharaqa 2This paper presents the design of non-uniform concentric circular antenna arrays (CCAAs) of isotropic radiators with optimum sidelobe level (SLL) reduction. The biogeography-based optimization (BBO) method is used to determine an optimum set of excitation amplitudes that provide a radiation pattern with optimum SLL reduction with the constraint of a fixed major lobe beamwidth. The BBO method represents a new global evolutionary algorithm for optimization problems in electromagnetics. It is shown that the BBO results provide an SLL reduction that is comparable to that obtained using wellknown algorithms, such as the particle swarm optimization (PSO), genetic algorithm (GA), and evolutionary programming (EP). Moreover, BBO results are compared with those obtained using the Matlab function Fmincon which uses a sequential quadratic programming (SQP) method. The comparison shows that the design of non-uniformly excited CCAAs using the SQP method provides a SLL reduction that is better than that obtained using global stochastic optimization methods, indicating that global optimization techniques might not really be needed in this problem.

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Biogeography Based Optimization Approach for Solving Optimal Power Flow Problem

Abstract: This paper presents the use of a novel evolutionary algorithm called Biogeography-based optimization (BBO)

Cited by 8 publications

References 9 publications

Environmental-economic dispatch using stochastic fractal search algorithm

Environmental-economic dispatch using stochastic fractal search algorithm

Multi objective optimal power flow using NSMOGSA

Design of non-uniform concentric circular antenna arrays with optimal sidelobe level reduction using biogeography-based optimization

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