Genetic Algorithm and Rough Sets Based Hybrid Approach for Economic Environmental Dispatch of Power Systems
Mohamed A. Hussein, Ahmed A. EL-Sawy, EL-Sayed M. Zaki, A. A. Mousa
Journal of Advances in Mathematics and Computer Science · pp. 2978–2999 · Published 7 Aug 2014
10.9734/BJMCS/2014/11526Abstract
In this paper we present a new optimization algorithm for Economic environmental dispatch EED of power systems. The purpose of EED problem is to compute the optimal generation for individual units of the power system by minimizing the fuel cost and emission levels simultaneously, subject to various equality and inequality constraints. The proposed algorithm is population based an evolutionary algorithm which operates in two phases: in the first one, genetic algorithm is implemented as search engine in order to generate approximate true Pareto front. This algorithm based on concept of co-evolution and repair algorithm for handling nonlinear constraints. Also it maintains a finite-sized archive of non-dominated solutions which gets iteratively updated in the presence of new solutions based on the concept of ε -dominance. Then, in the second phase, rough sets theory is adopted as local search engine in order to improve the spread of the solutions found so far. Optimization using multiobjective evolutionary algorithms yields not a single optimal solution. However, for practical applications, we need to select one solution which will satisfy the different goals to some extent. TOPSIS method has the ability to identify the best alternative from a finite set of alternatives. The proposed approach is carried out on the standard IEEE 30-bus 6-generator test system. The results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto-optimal nondominated solutions of the multiobjective EED problem. Also the comparison with the exiting well-known algorithms demonstrates the superiority of the proposed approach and confirms its potential to solve the multiobjective EED problem.
References (26)
- 1 Multi-Objective Optimization Using Evolutionary Algorithms
- 2 Multiple Attribute Decision Making: Methods and Applications
- 3 An Overview of Evolutionary Algorithms in Multiobjective Optimization [DOI]
- 4 Multiple Attribute Decision Making: Methods and Applications
- 5 Comparison of weights in TOPSIS models [DOI]
- 6 An updated survey of GA-based multiobjective optimization techniques [DOI]
- 7 Environmental/economic power dispatch using multiobjective evolutionary algorithms [DOI]
- 8 Multi-objective genetic algorithm and its applications to flowshop scheduling [DOI]
- 9 Economic load dispatch multiobjective optimization procedures using linear programming techniques [DOI]
- 10 Stochastic economic emission load dispatch [DOI]
- 11 A novel multiobjective evolutionary algorithm for environmental/economic power dispatch [DOI]
- 12 A niched Pareto genetic algorithm for multiobjective environmental/economic dispatch [DOI]
- 13 Emission constrained dynamic dispatch [DOI]
- 14 New multi-objective stochastic search technique for economic load dispatch [DOI]
- 15 Economic-Enviromental Power Dispatch [DOI]
- 16 Archiving with Guaranteed Convergence and Diversity in Multi-Objective Optimization
- 17 Assessing the Influence of Power Pools on Emission Constrained Economic Dispatch [DOI]
- 18 An effective genetic algorithm approach to multiobjective resource allocation problems (MORAPs) [DOI]
- 19 An ɛ-dominance-based multiobjective genetic algorithm for economic emission load dispatch optimization problem [DOI]
- 20 Using genetic algorithm and TOPSIS technique for multiobjective reactive power compensation [DOI]
- 21 IT-CEMOP: An iterative co-evolutionary algorithm for multiobjective optimization problem with nonlinear constraints [DOI]
- 22 Epsilon-dominance based multiobjective genetic algorithm for economic emission load dispatch optimization problem
- 23 Environmental marginal cost evaluation by non-inferiority surface (power systems) [DOI]
- 24 Enhanced Particle Swarm Optimization Based Local Search for Reactive Power Compensation Problem [DOI]
- 25 Optimization methodology based on Quantum computing applied to Fuzzy practical unit commitment problem
- 26 Hybrid Ant Optimization System for Multiobjective Optimal Power Flow Problem under Fuzziness [DOI]
Cited by 13
Abdallah M. Elsayed, Mohammed M. Mishref, Sobhy M. Farrag · International Transactions on Electrical Energy Systems · 2018
Tamer Fetouh, Mohamed S. Zaky · IET Generation, Transmission & Distribution · 2017
A. A. Mousa, M. A. El-Shorbagy, M. A. Farag · IEEE Access · 2020
M. A. El-Shorbagy, M. A. Farag, A. A. Mousa · Advances in Intelligent Systems and Computing · 2019
M. A. El-Shorbagy, A. A. Mousa · Complexity · 2021
M.A. El-Shorbagy, A.A. Mousa, S.M. Nasr · Chaos, Solitons & Fractals · 2016
Showing 6 of 13 known citations — external sources report more than can currently be individually listed.
Related research
- A Combined Genetic Algorithms-local Search Engine (GAs–LCE) in Constrained Nonlinear Programming — shares topic coverage
- mcga: R Implementation of the Machine-coded Genetic Algorithms — shares topic coverage
- Studying the effect of Eliminating Repeated Individuals from the Population in a Genetic Algorithm: Solution Perspectives for the Travelling Salesman Problem — shares topic coverage
- Enhanced Supply Chain Algorithm for ERP Systems Using ACO, Genetic, and Floyd-Warshall Algorithms — shares topic coverage
- Optimization Method for the Internal Distribution Network of a Photovoltaic Plant Using Genetic Algorithm — shares topic coverage
Article metrics
Real usage data collected on this platform.
25
Page views
0
PDF downloads
4
Outbound clicks
13
Citations
Views over time
Views by country
Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".
Traffic sources
Referring site, by host.
Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.