Modified ABC Algorithm for Generator Maintenance Scheduling

Anandhakumar, R.; Subramanian, S.; Ganesan, S.

doi:10.7763/ijcee.2011.v3.425

Cited by 8 publications

(7 citation statements)

References 16 publications

(25 reference statements)

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“…Various constraints are considered and should be satisfied by the schedule. An appropriate maintenance schedule should minimize the operating and maintenance cost of the power generating system or improve the reliability of the system [4]. The maintenance scheduling problem is very important as it affects other decisions regarding the operation of generating plants such as unit commitment and unit dispatch.…”

Section: A Maintenance Schedulingmentioning

confidence: 99%

A Tri-Objective Model for Generator Maintenance Scheduling

Moyo

Nwulu²,

Ekpenyong³

et al. 2021

IEEE Access

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The complexity of power systems is increasing as new generating units are added to power systems in order to supply power to the growing economies. This has resulted in further research into the generator maintenance scheduling (GMS) problem which seeks to ensure optimal preventive maintenance scheduling that is effective and reliable. This research is focused on developing a generator maintenance schedule using a tri-objective model. The GMS tri-objective model is solved using two solution methodologies. The first is an exact solution method using mathematical modelling software, Advanced Interactive Multidimensional Modelling System (AIMMS). The second solution method is a recently developed metaheuristic algorithm called Exchange Market Algorithm (EMA). Results show that the tri-objective model finds a trade-off solution of the individual solution methods. The metaheuristic algorithm gives a better solution for larger optimization problems.

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Section: A Maintenance Schedulingmentioning

confidence: 99%

A Tri-Objective Model for Generator Maintenance Scheduling

Moyo

Nwulu²,

Ekpenyong³

et al. 2021

IEEE Access

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“…The improved following bees in the discrete ABC algorithm introduce Tabu table ) (2,5) and side (1,4) in this two cities respectively, and it will get neighborhood solution i X which is meaningless. In order to avoid the bees repeated searching within the same neighborhood and improve the production ability of algorithm, for Tabu table i tabu in example 2, it just needs to record (2,4) two points, namely record the second city of first side and first cit of second side.…”

Section: Improved Following Beesmentioning

confidence: 99%

“…ABC algorithm [5] is based on the self-organization of the swarm simulation model with the advantages of less setting parameters, strong robustness, it has received extensive attention of scholars both at home and abroad, and been applied in many fields.…”

Section: Introductionmentioning

confidence: 99%

A New Method Used for Traveling salesman problem Based on Discrete Artificial Bee Colony Algorithm

Meng¹,

Yin²,

Hu³

2016

TELKOMNIKA

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We propose a new method based on discrete Artificial Bee Colony algorithm (DABC) IntroductionTraveling salesman problem (it is abbreviated in TSP) [1] is an important combinational optimization problem in the area of mathematics. It belongs to Non-Deterministic Polynomial (NP) problem [2]. Although there are some precise algorithms which can be used to solve the problem, the principle of precise algorithms is complex, and it can produce "combination explosion problem" along with the increase number of city, therefore, the domestic and foreign scholars have been trying to seek a highly efficient and stable algorithm for solving this complex problem.Artificial bees colony algorithm (ABC) [3] is one of heuristic search algorithms based on swarm intelligence, which was proposed by Karaboga et al. [4] in 2005. ABC algorithm [5] is based on the self-organization of the swarm simulation model with the advantages of less setting parameters, strong robustness, it has received extensive attention of scholars both at home and abroad, and been applied in many fields.Nowadays, many researchers have studied the new algorithm about Artificial bees colony for TSP. Sharma P et al. [6] showed that bees speculative modified over time and based on the best solution found by the bee itself and the swarms of bees were dynamically divided into smaller groups and search process was performed by an independent smaller group of bees. Hong-Tao et al. [7] proposed a discrete artificial bee colony algorithm. And he introduced a tabu list and a repulsion operator.The above methods almost are used for solving continuous domain optimization problems. However, ABC algorithm is relatively few used in the aspect of discrete domain application. To improve the performance of TSP solution, we propose improved discrete Artificial Bee Colony algorithm through redefining leading bees, following bees and scouts which better coordinates and balances the exploration and mining process of ABC algorithm. To facilitate the description, this paper also gives some definitions. We present a new discrete Artificial Bee Colony algorithm for traveling salesman problem. This new scheme takes balance of space exploration and the local refinement into consideration. Finally, we conduct some experiments to verify its performance. The results show that the new algorithm has a good effect on solving TSP question.

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“…In [18] Sonmez uses the ABC technique to solve the multiobjective problem of scheduling generators to fulfil the demand of energy. In [19], Anandhakumar et al used the ABC capabilities to solve the problem of diesel engine maintenance.…”

Section: Introductionmentioning

confidence: 99%

Journal Bearing Optimization Using Nonsorted Genetic Algorithm and Artificial Bee Colony Algorithm

Gorasso

Wang

2014

Advances in Mechanical Engineering

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In this work, a journal bearing optimization process has been developed and is divided into two stages. Each one has a set of decision variables and custom objectives aggregating performances with a weighting strategy. The performance functions used are an artificial neural network, trained with Reynolds equation solutions, and a CFD simulation of the bearings carried out with commercial software. The results show the capabilities of the algorithm to design and optimize journal bearings by reducing both power loss and mass flow with respect to ones designed with traditional methods, as well as by minimizing the maximum and average temperature.

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Modified ABC Algorithm for Generator Maintenance Scheduling

Cited by 8 publications

References 16 publications

A Tri-Objective Model for Generator Maintenance Scheduling

A Tri-Objective Model for Generator Maintenance Scheduling

A New Method Used for Traveling salesman problem Based on Discrete Artificial Bee Colony Algorithm

Journal Bearing Optimization Using Nonsorted Genetic Algorithm and Artificial Bee Colony Algorithm

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