Adaptive biogeography based predator–prey optimization technique for optimal power flow

Christy, A. Ananthi; Raj, P. Ajay-D-Vimal

doi:10.1016/j.ijepes.2014.04.054

Cited by 54 publications

(10 citation statements)

References 41 publications

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“…Additionally, the results are compared with other methods, and the comparison is shown in Table 9. According to the value of the weighted sum, IKHA is better than KHA, particle swarm optimization and gravitational search algorithm (PSOGSA) [29], The proposed KHA [39], adaptive biogeography based predator-prey optimization (ABPPO) [40], MSA [22], LTLBO [23], Gbest guided artificial bee colony algorithm (GABC) [24] and ICBO [12] Looking at the two goals separately, the results of IKHA are lower than those of KHA and the proposed KHA For the results of the other methods in Table 8, such as the PSOGSA [29], only one of the two goals is better than IKHA. As the individual evaluation criteria are different, the optimal solution is different.…”

Section: Case 5: Minimization Of Quadratic Cost and Voltage Magnitudementioning

confidence: 99%

“…It is also shows 7.3266% increase in the quadratic fuel cost and 49.661% reduction in the transmission real power losses compared with case 1. The simulation results are compared with other methods in Table 10, and the value of the weighted sum is better than KHA, MSA [22], modified differential evolution (MDE) [22], PSOGSA [29] and ABPPO [40]. The analysis of current case is similar to Case 5 because that the different criteria make different choices.…”

Section: Case 6: Minimization Of Quadratic Cost and Transmission Realmentioning

confidence: 99%

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Improved Krill Herd Algorithm with Novel Constraint Handling Method for Solving Optimal Power Flow Problems

Chen

Zhang

2018

Energies

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Abstract:As one of the most important tools used in operation and planning of power systems, the optimal power flow (OPF) problem considering the economy and security is large-scale, complex and hard to solve. In this paper, an improved krill herd algorithm (IKHA) has been proposed. In IKHA, the onlooker search mechanism is introduced to reduce the probability of falling into local optimum; and the parameter values of the proposed algorithm including inertia weight and step-length scale factor are varied according to the iteration of evolutionary process, which improves the exploration and exploitation capabilities. Moreover, a novel constraint handling method is proposed to guide the individual to the feasible space and ensure that the optimal solution satisfies the security constraints. Then, IKHA is combined with the novel constraint handling method to solve the multi-constrained OPF problem, and its performance is tested on the IEEE 30 bus, IEEE 57 bus and IEEE 118 bus systems for 10 different simulation cases containing linear and non-linear objective functions. The simulation results demonstrate that the proposed method can solve the OPF problem successfully and obtain better solutions compared with other methods reported in the recent literatures, which prove the feasibility and effectiveness of the improvements in this work.

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Section: Case 5: Minimization Of Quadratic Cost and Voltage Magnitudementioning

confidence: 99%

Section: Case 6: Minimization Of Quadratic Cost and Transmission Realmentioning

confidence: 99%

Improved Krill Herd Algorithm with Novel Constraint Handling Method for Solving Optimal Power Flow Problems

Chen

Zhang

2018

Energies

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“…Newly developed search-based optimization algorithms are applied for OPF problems, like particle swarm optimization (PSO) method sit [ 2 ], differential evolutionary technique sit [ 20 ] [ 21 ]), improved colliding bodies optimization method sit [ 22 ], improved PSO algorithm sit [ 23 ], biogeography-based optimization technique sit [ 24 ], imperialist competitive method sit [ 25 ], grey wolf optimizer sit [ 26 ], hybrid algorithm of PSO and GSA algorithm sit [ 27 ], differential search technique sit [ 28 ], gravitational search method (GSM) sit [ 29 ] [ 30 ] [ 31 ], multi-phase search optimization technique sit [ 32 ] [ 33 ], fuzzy-based hybrid PSO algorithm sit [ 34 ], chaotic self-adaptive differential harmony search method sit [ 35 ], black-hole-based optimization technique sit [ 36 ], harmony search technique sit [ 37 ], artificial bee colony method (4), Jaya optimization technique sit [ 38 ], teaching-learning-optimization algorithm sit [ 39 ], biogeography-based optimization (BBO) sit [ 40 ], differential evolution (DE) sit [ 41 ], artificial bee colony (ABC) algorithm sit [ 42 ], distributed algorithm (DA) sit [ 43 ], and the Firefly algorithm (FA) sit [ 44 ]. An analysis of a non-deterministic algorithm, which is applied to solve OPF, is mentioned in sit [ 45 ].…”

Section: Introductionmentioning

confidence: 99%

Optimal power flow using hybrid firefly and particle swarm optimization algorithm

et al. 2020

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In this paper, a novel, effective meta-heuristic, population-based Hybrid Firefly Particle Swarm Optimization (HFPSO) algorithm is applied to solve different non-linear and convex optimal power flow (OPF) problems. The HFPSO algorithm is a hybridization of the Firefly Optimization (FFO) and the Particle Swarm Optimization (PSO) technique, to enhance the exploration, exploitation strategies, and to speed up the convergence rate. In this work, five objective functions of OPF problems are studied to prove the strength of the proposed method: total generation cost minimization, voltage profile improvement, voltage stability enhancement, the transmission lines active power loss reductions, and the transmission lines reactive power loss reductions. The particular fitness function is chosen as a single objective based on control parameters. The proposed HFPSO technique is coded using MATLAB software and its effectiveness is tested on the standard IEEE 30-bus test system. The obtained results of the proposed algorithm are compared to simulated results of the original Particle Swarm Optimization (PSO) method and the present state-of-the-art optimization techniques. The comparison of optimum solutions reveals that the recommended method can generate optimum, feasible, global solutions with fast convergence and can also deal with the challenges and complexities of various OPF problems.

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“…BBO is an evolutionary algorithm that was inspired by the migration of species between habitats. Specifically, it was inspired by biogeography, which describes (i) the speciation and migration of species between isolated habitats and (ii) the extinction of species [29]. In recent years, the BBO algorithm has been widely used in a myriad of fields, such as to solve the engineering optimization problem.…”

Section: Introductionmentioning

confidence: 99%

Relationship between Clinicopathologic Variables in Breast Cancer Overall Survival Using Biogeography-Based Optimization Algorithm

Chuang

Chen

Moi

et al. 2019

BioMed Research International

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Breast cancer is the most common cancer among women and is considered a major public health concern worldwide. Biogeography-based optimization (BBO) is a novel metaheuristic algorithm. This study analyzed the relationship between the clinicopathologic variables of breast cancer using Cox proportional hazard (PH) regression on the basis of the BBO algorithm. The dataset is prospectively maintained by the Division of Breast Surgery at Kaohsiung Medical University Hospital. A total of 1896 patients with breast cancer were included and tracked from 2005 to 2017. Fifteen general breast cancer clinicopathologic variables were collected. We used the BBO algorithm to select the clinicopathologic variables that could potentially contribute to predicting breast cancer prognosis. Subsequently, Cox PH regression analysis was used to demonstrate the association between overall survival and the selected clinicopathologic variables. C-statistics were used to test predictive accuracy and the concordance of various survival models. The BBO-selected clinicopathologic variables model obtained the highest C-statistic value (80%) for predicting the overall survival of patients with breast cancer. The selected clinicopathologic variables included tumor size (hazard ratio [HR] 2.372, p = 0.006), lymph node metastasis (HR 1.301, p = 0.038), lymphovascular invasion (HR 1.606, p = 0.096), perineural invasion (HR 1.546, p = 0.168), dermal invasion (HR 1.548, p = 0.028), total mastectomy (HR 1.633, p = 0.092), without hormone therapy (HR 2.178, p = 0.003), and without chemotherapy (HR 1.234, p = 0.491). This number was the minimum number of discriminators required for optimal discrimination in the breast cancer overall survival model with acceptable prediction ability. Therefore, on the basis of the clinicopathologic variables, the survival prediction model in this study could contribute to breast cancer follow-up and management.

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Adaptive biogeography based predator–prey optimization technique for optimal power flow

Cited by 54 publications

References 41 publications

Improved Krill Herd Algorithm with Novel Constraint Handling Method for Solving Optimal Power Flow Problems

Improved Krill Herd Algorithm with Novel Constraint Handling Method for Solving Optimal Power Flow Problems

Optimal power flow using hybrid firefly and particle swarm optimization algorithm

Relationship between Clinicopathologic Variables in Breast Cancer Overall Survival Using Biogeography-Based Optimization Algorithm

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