Mosquitoes-behaviour based (MOX) evolutionary algorithm in static transmission network expansion planning

Rathore, Chandrakant; Roy, Ranjit; Raj, Shubham; Sinha, Ankit Kumar

doi:10.1109/iceets.2013.6533524

Cited by 5 publications

(4 citation statements)

References 16 publications

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“…Without considering losses, and not allowing generation redispatch (generation scenario G1 [47]), according to [50], [51] and other authors, the optimal solution is composed of fifteen circuits connected on nine branches (#1-5=1, #3-24=1, #6-10=1, #7-8=2, #14-16=1, #15-24=1, #16-17=2, #16-19=1, #17-18=2) and costs US$ 390 million. The BATp optimizer, with 𝑟 𝑖𝑗 = 0, has also found this solution.…”

Section: V11 Lossless Optimal Solutionsmentioning

confidence: 99%

Modified Bat Algorithm for transmission network expansion planning considering active power losses

Barros¹,

Barros²

2023

ITEGAM

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This paper presents a metaheuristic algorithm, called BATp optimizer, to solve the combinatorial problem of static transmission networks expansion planning (STNEP) considering the effect of active power losses in the circuits. The optimizer is composed of two modules. One module generates candidate solutions, using the modified Bat Algorithm (BA), and the other that makes solutions with over costs or infeasibilities competitive. The modification made to the original BA consists in the inclusion of a local search intensification operator that acts on the elements of the current global optimal solution to improve the convergence rate and hinder stagnation in a suboptimal solution. The number of elements modified in the current global optimal solution is defined as a function of the number of buses and branches in the analyzed system. The size of the initial population is also defined as a function of the number of buses and branches. The active power losses are represented in the equality constraints of the mixed-integer nonlinear programming (MINLP) problem. The performance evaluation of the transmission network of the analyzed system is done by a linear power flow. The performance of the BATp optimizer was tested in three systems well known in the literature: the IEEE 24-bus and the South Brazilian -SB 46-bus. In each of the analyzed systems, situations were simulated with and without the possibility of generation redispatch. The BATp optimizer was able to find good results compared to those published in the literature, with relatively low computational effort.

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Section: V11 Lossless Optimal Solutionsmentioning

confidence: 99%

Modified Bat Algorithm for transmission network expansion planning considering active power losses

Barros¹,

Barros²

2023

ITEGAM

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“…There are various types of swarm based algorithms, i.e. Particle Swarm Optimization (PSO) [34], Monarch butterfly optimization [35], Cat Swarm Optimization (CSO) [36], Ant Colony Algorithm (ACO) [37], Group search optimizer [38], Bat Algorithm (BA) [39], Artificial Bee Colony Algorithm (ABCA) [16], Shuffled Frog Leaping Algorithm (SFLA) [40], Firefly Algorithm (FFA) [41], Grey wolf optimization (GWO) [42], Krill Herd (KH) [43], Monkey Algorithm [44], Intelligent Water Drops, Chaos Theory (CT) [45] and Mosquitoes Mating Strategy [46]. League championship algorithm [47] is considered a sport based metaheuristic technique.…”

Section: Metaheuristic Techniquesmentioning

confidence: 99%

Optimization-Based Evolutionary Data Mining Techniques for Structural Health Monitoring

Gordan¹,

Ismail²,

Razak³

et al. 2020

J. Civ. Eng. Constr.

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In recent years, data mining technology has been employed to solve various Structural Health Monitoring (SHM) problems as a comprehensive strategy because of its computational capability. Optimization is one the most important functions in Data mining. In an engineering optimization problem, it is not easy to find an exact solution. In this regard, evolutionary techniques have been applied as a part of procedure of achieving the exact solution. Therefore, various metaheuristic algorithms have been developed to solve a variety of engineering optimization problems in SHM. This study presents the most applicable as well as effective evolutionary techniques used in structural damage identification. To this end, a brief overview of metaheuristic techniques is discussed in this paper. Then the most applicable optimization-based algorithms in structural damage identification are presented, i.e. Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Imperialist Competitive Algorithm (ICA) and Ant Colony Optimization (ACO). Some related examples are also detailed in order to indicate the efficiency of these algorithms.

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“…The mosquitoes-behaviour-based (MOX) algorithm is inspired by characteristic behaviour in their reproduction and early development stages of mosquitoes have been applied to solve TNEP problem (Rathore et al, 2013b).…”

Section: Other Algorithmsmentioning

confidence: 99%

“…In this work, researcher compares the result of this strategy from PSO and GA for TNEP problem. (1995a, 1995b) approach has been used to solve TNEP da Silva et al (1999) IGA An improved GA has been designed to solve TNEP de Silva et al (2005) SGA Specialised GA has been presented to solve security constraint TNEP Correa et al (2015) GA NSGA-II crowded distance features and the Chu-Beasley GA has been used to solve security constraint TNEP Sensarma et al (2002) PSO Risk constrained TNEP has been solved by PSO Kavitha and Swarup (2006) LPSO Linear programming-based PSO is used to solve TNEP Barreto et al (2013) PSO TNEP has been solved by different variations of PSO like GPSO, LPSO, and QPSO also the results are compared Sisodia et al (2016) GA + PSO Hybrid tool of GA and PSO has been used to solve TNEP Sum-Im et al 2009, DE Multi-stage TNEP has been Rezende et al (2009) solved using DE Georgilakis (2010b) IDE Improve DE algorithm has been used to solve TNEP problem in deregulated environment Orfanos et al (2011) EDE Enhanced DE algorithm has been developed to solve TNEP Rathore et al (2013a) ABC ABC algorithm has been used to solve STNEP Rathore and Roy (2016) ABC TNEP problem with incorporating of wind power and plugin-electrical vehicles has been solved using ABC algorithm Fuchs and Gjengedal (2011) ACO TNEP problem with integration of wind power has been solved using ACO algorithm Verma et al (2010) HS Security constrained TNEP problem has been solved using HS algorithm Sharma et al (2017) FSMO TNEP problem has been solved using Fibonacci series inspired SMO algorithm Khandelwal et al (2017a) GWO TNEP problem has been solved using modified GWO algorithm Rathore et al (2013b) MOX TNEP problem has been solved using modified MOX algorithm 4 Significant works for TNEP…”

Section: Other Algorithmsmentioning

confidence: 99%

Transmission network expansion planning using state-of-art nature inspired algorithms: a survey

Khandelwal

Bhargava

Sharma

et al. 2019

IJSI

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Transmission network expansion planning (TNEP) problem has been continuously solved for many years still the cost effective, reliable, and optimise solution is always desirable. The TNEP has been solved by various conventional and non conventional strategies. The strategy to find the solution of TNEP by classical mathematical optimisation techniques is tedious, slow and inefficient. In recent years, nature inspired algorithms (NIAs) have proven their importance to provide the solutions of the TNEP problem over classical mathematical optimisation techniques. This paper presents a review on the key contributions of the state-of-art NIAs to solve the TNEP problem. Further, the TNEP system specific significant works presented in the literature are summarised for easy understanding of the readers. The readers can get a brief description of the considered NIAs algorithms which has been applied to solve various systems of TNEP problem and they can also identify the significant NIA which is being applied for specific TNEP system.

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Mosquitoes-behaviour based (MOX) evolutionary algorithm in static transmission network expansion planning

Cited by 5 publications

References 16 publications

Modified Bat Algorithm for transmission network expansion planning considering active power losses

Modified Bat Algorithm for transmission network expansion planning considering active power losses

Optimization-Based Evolutionary Data Mining Techniques for Structural Health Monitoring

Transmission network expansion planning using state-of-art nature inspired algorithms: a survey

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