Modified bio‐inspired optimisation algorithm with a centroid decision making approach for solving a multi‐objective optimal power flow problem

Barocio, E.; Regalado, Jose A.; Cuevas, Erick; Uribe, Felipe; Zuniga, Pavel; Torres, Pedro

doi:10.1049/iet-gtd.2016.1135

Cited by 42 publications

(20 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

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

confidence: 99%

“…Table 5 compares the best compromise solutions (BCSs) found in the Pareto front solutions related in Cases 1, 2, 3, 4, and 5, respectively. The method used to select the BCS is the fuzzy membership method described in [15]. Table 5 shows that the proposed MOFA-CPA obtains all the minimum values of these three optimal objectives computed for the BCS in Case 4, and the BCS obtained by MOFA-CPA of 878.13 $/h cost, 3.9232 MW loss, and 0.2161 ton/h emission dominates the BCS obtained by MOFA-CPA of 879.91 $/h cost, 4.2179 MW loss, and 0.2165 ton/h emission.…”

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

confidence: 99%

“…A tri-objective differential evolution (DE) approach was carried out for the TOP problem contains all global optima of the MMOP in [14]. In [15], Emilio Barocio implemented the modified flower pollination algorithm (MFPA) to calculate the PFs under different objective combinations of the MOOPF problem, using added penalty functions to handle the constraints. The authors used the non-dominated sorting multi-objective gravitational search algorithm (NSMOGSA) to solve the MOOPF problem, its applicability to large systems remains to be tested due to the effectiveness of the proposed algorithm is only tested on a standard IEEE 30-bus power system [16].…”

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

Chen

Zhang

et al. 2018

Energies

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…(1) Swarm-based algorithms such as particle swarm optimization (PSO) [25], glowworm swarm optimization [26], artificial bee colony (ABC) [27], grasshopper optimization [28], and the grey wolf optimizer [29], [30]. Also, the modified shuffle frog leaping algorithm [31], moth-flame algorithm [32], flower pollination algorithm [33], and stud krill herd algorithm [34].…”

Section: B Background Outlookmentioning

confidence: 99%

Optimal Power Flow Solution With an Embedded Center-Node Unified Power Flow Controller Using an Adaptive Grasshopper Optimization Algorithm

et al. 2020

View full text Add to dashboard Cite

This paper proposes an adaptive grasshopper optimization algorithm (AGOA) for solving the optimal power flow (OPF) problem with the optimal incorporation of a center-node unified power flow controller (C-UPFC). The C-UPFC which is an advanced flexible AC transmission system (FACTS) device is inserted in series with a transmission line (TL) at its midpoint for providing the power flow control together with independent voltage control. The proposed AGOA is based on applying the Levy flight distribution and spiral path orientation of search agents to the traditional grasshopper optimization algorithm (GOA) to diminish the stagnation problem of the basic GOA at local optima and enhance its searching ability. Therefore, this AGOA technique is implemented for optimal sizing and siting of the C-UPFC on standard IEEE 30-bus and 57-bus systems as well as 26-bus system, and then compared with other well-known techniques to verify its effectiveness. To assess the installation of the C-UPFC in a power system, the optimal capacities and locations of the C-UPFC are determined for different objective functions, such as the fuel cost, fuel cost with a valve point loading effect (VPLE), piecewise cost and emission. Simulation results reveal that the proposed algorithm is more efficient and superior for OPF solution compared with the other algorithms reported in the literature. Furthermore, the optimal integration of the C-UPFC in the power system is considerably minimizing the power loss and improving the voltage profile.

show abstract

“…Multi objective optimization concerns optimization problems with multiple objectives. Barocio et al [17] and Qingqi et al [18] have proven the superiority of bio-inspired algorithms in solving multi-objective optimization. The fitness of multi objective optimization is calculated as follows:…”

Section: Update Roach Location (Xi)mentioning

confidence: 99%

Development of Discrete-Cockroach Algorithm (DCA) for Feature Selection Optimization

Hendrawan¹,

Rachmawati²,

Fauzy³

2018

EECSI

View full text Add to dashboard Cite

One of the recently proposed algorithms in the field of bio-inspired algorithm is the Hungry Roach Infestation Optimization (HRIO) algorithm. Haven has developed optimization algorithms HRIO that is inspired by recent discoveries in the social behavior of cockroaches. Result showed that HRIO was effective at finding the global optima of a suite of test functions. However, there is no researcher who has observed HRIO for solving discrete feature selection problems. Therefore, we try to develop a discrete-cockroach algorithm (DCA) as the modification of HRIO for optimizing discrete feature selection problem. We test the algorithm to solve feature selection problem in machine vision for predicting water stress in plant using single and multiobjectives optimization. Two objective functions are minimizing prediction error and minimizing the number of feature-subset. The results showed DCA has better performance compared to the existed bio-inspired optimization algorithms such as genetic algorithms (GA) and discreteparticle swarm optimization (discrete-PSO). The performance showed significant difference between DCA and other methods.

show abstract

Modified bio‐inspired optimisation algorithm with a centroid decision making approach for solving a multi‐objective optimal power flow problem

Cited by 42 publications

References 38 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

Optimal Power Flow Solution With an Embedded Center-Node Unified Power Flow Controller Using an Adaptive Grasshopper Optimization Algorithm

Development of Discrete-Cockroach Algorithm (DCA) for Feature Selection Optimization

Contact Info

Product

Resources

About