scholarly journals Hybrid ABC-BAT for Solving Short-Term Hydrothermal Scheduling Problems

Energies ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 551 ◽  
Author(s):  
Smarajit Ghosh ◽  
Manvir Kaur ◽  
Suman Bhullar ◽  
Vinod Karar
Keyword(s):  
Hybrid Algorithm ◽  
Artificial Bee Colony ◽  
Optimal Allocation ◽  
Bat Algorithm ◽  
Scheduling Problems ◽  
Short Term ◽  
Hydrothermal Scheduling ◽  
Hybrid Techniques ◽  
Bee Colony ◽  
The Cost

The main objective of short-term hydrothermal scheduling is the optimal allocation of the hydro and thermal generating units, so that the total cost of thermal plants can be minimized. The time of operation of the functioning units are considered to be 24 h. To achieve this objective, the hybrid algorithm combination of Artificial Bee Colony (ABC) and the BAT algorithm were applied. The swarming behavior of the algorithm searches the food source for which the objective function of the cost is to be considered; here, we have used two search algorithms, one to optimize the cost function, and another to improve the performance of the system. In the present work, the optimum scheduling of hydro and thermal units is proposed, where these units are acting as a relay unit. The short term hydrothermal scheduling problem was tested in a Chilean system, and confirmed by comparison with other hybrid techniques such as Artificial Bee Colony–Quantum Evolutionary and Artificial Bee Colony–Particle Swarm Optimization. The efficiency of the proposed hybrid algorithm is established by comparing it to these two hybrid algorithms.

scholarly journals A Hybrid Artificial Bee Colony Algorithm for Flexible Job Shop Scheduling Problems

2011 ◽  
Vol 6 (2) ◽  
pp. 286 ◽  
Author(s):  
Jun-qing Li ◽  
Sheng-xian Xie ◽  
Quan-ke Pan ◽  
Song Wang
Keyword(s):  
Hybrid Algorithm ◽  
Artificial Bee Colony ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Food Sources ◽  
Flexible Job Shop ◽  
Scheduling Problems ◽  
Flexible Job Shop Scheduling ◽  
Shop Scheduling ◽  
Bee Colony

<p>In this paper, we propose a hybrid Pareto-based artificial bee colony (HABC) algorithm for solving the multi-objective flexible job shop scheduling problem. In the hybrid algorithm, each food sources is represented by two vectors, i.e., the machine assignment vector and the operation scheduling vector. The artificial bee is divided into three groups, namely, employed bees, onlookers, and scouts bees. Furthermore, an external Pareto archive set is introduced to record non-dominated solutions found so far. To balance the exploration and exploitation capability of the algorithm, the scout bees in the hybrid algorithm are divided into two parts. The scout bees in one part perform randomly search in the predefined region while each scout bee in another part randomly select one non-dominated solution from the Pareto archive set. Experimental results on the well-known benchmark instances and comparisons with other recently published algorithms show the efficiency and effectiveness of the proposed algorithm.</p>

scholarly journals Combined Economic and Emission Dispatch Solution using Artificial Bee Colony Algorithm with Fuzzy Approach

2018 ◽  
Vol 7 (3.15) ◽  
pp. 46
Author(s):  
M N. Abdullah ◽  
G Y. Sim ◽  
A Azmi ◽  
S H. Shamsudin
Keyword(s):  
Power Generation ◽  
Power System ◽  
Artificial Bee Colony ◽  
Compromise Solution ◽  
Fuzzy Approach ◽  
Abc Algorithm ◽  
Unit System ◽  
Bee Colony ◽  
The Cost ◽  
Emission Dispatch

The cost and emission minimization in power system operation become important issue in power dispatch due to increase of environmental pollution and fossil fuel price. Therefore, combined economic and emission dispatch (CEED) must be considered in generation scheduling in order to provide balanced solution for optimal cost and emissions level of power generation. In this paper, an Artificial Bee Colony (ABC) algorithm with Fuzzy best compromise solution is proposed to determine the optimal cost and emission level by converting the multi-objective (cost and emission) into single objective problem using weighted sum method approach. The best compromise solution among Pareto front solution was determined by fuzzy approach. The effectiveness of ABC algorithm has been validated in terms of the best solution, convergence behaviour and consistency for power system benchmark such as IEEE 30-bus 6-unit system and 10-unit system. The comparison study shows that ABC algorithm capable to obtain a better performance of minimizing the cost and emission level in power generation.  

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