Quantum-Behaved Bat Algorithm for Solving the Economic Load Dispatch Problem Considering a Valve-Point Effect

2021 ◽  
pp. 93-110
Author(s):  
Pandian Vasant ◽  
Fahad Parvez Mahdi ◽  
Jose Antonio Marmolejo-Saucedo ◽  
Igor Litvinchev ◽  
Roman Rodriguez Aguilar ◽  
...  
Keyword(s):  
Nonlinear Optimization ◽  
Optimization Problems ◽  
Bat Algorithm ◽  
Evolutionary Programming ◽  
Economic Load Dispatch ◽  
Solution Quality ◽  
Swarm Optimization ◽  
Real World Problem ◽  
Valve Point Effect ◽  
Power Generation Systems

Quantum computing-inspired metaheuristic algorithms have emerged as a powerful computational tool to solve nonlinear optimization problems. In this paper, a quantum-behaved bat algorithm (QBA) is implemented to solve a nonlinear economic load dispatch (ELD) problem. The objective of ELD is to find an optimal combination of power generating units in order to minimize total fuel cost of the system, while satisfying all other constraints. To make the system more applicable to the real-world problem, a valve-point effect is considered here with the ELD problem. QBA is applied in 3-unit, 10-unit, and 40-unit power generation systems for different load demands. The obtained result is then presented and compared with some well-known methods from the literature such as different versions of evolutionary programming (EP) and particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), simulated annealing (SA) and hybrid ABC_PSO. The comparison of results shows that QBA performs better than the above-mentioned methods in terms of solution quality, convergence characteristics and computational efficiency. Thus, QBA proves to be an effective and a robust technique to solve such nonlinear optimization problem.

Quantum-Behaved Bat Algorithm for Solving the Economic Load Dispatch Problem Considering a Valve-Point Effect

2020 ◽  
Vol 11 (3) ◽  
pp. 41-57
Author(s):  
Pandian Vasant ◽  
Fahad Parvez Mahdi ◽  
Jose Antonio Marmolejo-Saucedo ◽  
Igor Litvinchev ◽  
Roman Rodriguez Aguilar ◽  
...  
Keyword(s):  
Nonlinear Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Bat Algorithm ◽  
Economic Load Dispatch ◽  
Solution Quality ◽  
Real World Problem ◽  
Valve Point Effect ◽  
Power Generation Systems ◽  
Better Than

Quantum computing-inspired metaheuristic algorithms have emerged as a powerful computational tool to solve nonlinear optimization problems. In this paper, a quantum-behaved bat algorithm (QBA) is implemented to solve a nonlinear economic load dispatch (ELD) problem. The objective of ELD is to find an optimal combination of power generating units in order to minimize total fuel cost of the system, while satisfying all other constraints. To make the system more applicable to the real-world problem, a valve-point effect is considered here with the ELD problem. QBA is applied in 3-unit, 10-unit, and 40-unit power generation systems for different load demands. The obtained result is then presented and compared with some well-known methods from the literature such as different versions of evolutionary programming (EP) and particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), simulated annealing (SA) and hybrid ABC_PSO. The comparison of results shows that QBA performs better than the above-mentioned methods in terms of solution quality, convergence characteristics and computational efficiency. Thus, QBA proves to be an effective and a robust technique to solve such nonlinear optimization problem.

Economic Load Dispatch With Multiple Fuel Options and Valve Point Effect Using Cuckoo Search Algorithm With Different Distributions

2020 ◽  
Vol 9 (3) ◽  
pp. 24-38
Author(s):  
Cuong Dinh Tran ◽  
Tam Thanh Dao ◽  
Ve Song Vo
Keyword(s):  
Random Walk ◽  
Heuristic Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Cauchy Distribution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Natural Phenomenon ◽  
Economic Load Dispatch ◽  
Valve Point Effect

The cuckoo search algorithm (CSA), a new meta-heuristic algorithm based on natural phenomenon of the cuckoo species and Lévy flights random walk has been widely and successfully applied to several optimization problems so far. In the article, two modified versions of CSA, where new solutions are generated using two distributions including Gaussian and Cauchy distributions in addition to imposing bound by best solutions mechanisms are proposed for solving economic load dispatch (ELD) problems with multiple fuel options. The advantages of CSA with Gaussian distribution (CSA-Gauss) and CSA with Cauchy distribution (CSA-Cauchy) over CSA with Lévy distribution and other meta-heuristic are fewer parameters. The proposed CSA methods are tested on two systems with several load cases and obtained results are compared to other methods. The result comparisons have shown that the proposed methods are highly effective for solving ELD problem with multiple fuel options and/nor valve point effect.

scholarly journals Improved Barebones Particle Swarm Optimization with Neighborhood Search and Its Application on Ship Design

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Jingzheng Yao ◽  
Duanfeng Han
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Ship Design ◽  
Neighborhood Search ◽  
Search Process ◽  
Exploration And Exploitation ◽  
Swarm Optimization ◽  
Real World Problem ◽  
Complex Optimization

Barebones particle swarm optimization (BPSO) is a new PSO variant, which has shown a good performance on many optimization problems. However, similar to the standard PSO, BPSO also suffers from premature convergence when solving complex optimization problems. In order to improve the performance of BPSO, this paper proposes a new BPSO variant called BPSO with neighborhood search (NSBPSO) to achieve a tradeoff between exploration and exploitation during the search process. Experiments are conducted on twelve benchmark functions and a real-world problem of ship design. Simulation results demonstrate that our approach outperforms the standard PSO, BPSO, and six other improved PSO algorithms.

Comparison of Selected Swarm Intelligence Algorithms in Student Courses Recommendation Application

2014 ◽  
Vol 24 (01) ◽  
pp. 91-109 ◽  
Author(s):  
Janusz Sobecki
Keyword(s):  
Particle Swarm Optimization ◽  
Swarm Intelligence ◽  
Optimization Problems ◽  
Bat Algorithm ◽  
Problem Space ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Grade Prediction ◽  
Bee Colony Optimization ◽  
Swarm Intelligence Algorithm

In this paper a comparison of a few swarm intelligence algorithms applied in recommendation of student courses is presented. Swarm intelligence algorithms are nowadays successfully used in many areas, especially in optimization problems. To apply each swarm intelligence algorithm in recommender systems a special representation of the problem space is necessary. Here we present the comparison of efficiency of grade prediction of several evolutionary algorithms, such as: Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Intelligent Weed Optimization (IWO), Bee Colony Optimization (BCO) and Bat Algorithm (BA).

scholarly journals Modified Particle Swarm Optimisation for Economic Load Dispatch Problem

2019 ◽  
Vol 8 (2S3) ◽  
pp. 172-177
Keyword(s):  
Particle Swarm ◽  
Solution Space ◽  
Bat Algorithm ◽  
Steady Increase ◽  
Cost Curve ◽  
Economic Load Dispatch ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Cost Parameters ◽  
The Cost

For every Power System the concept of Economic Load Dispatch has and continues to be an area of immense importance and concern. With the steady increase in demand over the years it has become all the more mandatory to reduce the cost of power generation and at the same time meet all the power requirements of the concerned area. There are a number of techniques to solve the ever existing problem of Economic Load Dispatch such as Bat Algorithm, Firefly Method, NewtonRaphson Method, Lambda Iterative Method and so on and so forth. Each of these heuristic approaches require the cost curve to be piece wise linear. In this paper, Modified Particle Swarm Optimization has been proposed to find the most economical and ideal solution to the load dispatch problem. This is done by focussing on the population size of the solution space more than on other cost parameters. The simulation for the system is performed on MATLAB software

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