Journal ArticleDOI
Environmental/economic power dispatch problem using multi-objective differential evolution algorithm
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TLDR
The results demonstrate the capability of the proposed MODE approach to generate well-distributed Pareto optimal non-dominated solutions of multi-objective EED problem and confirms its potential for solving other power systems multi- objective optimization problems.About:
This article is published in Electric Power Systems Research.The article was published on 2010-09-01. It has received 219 citations till now. The article focuses on the topics: Optimization problem & Multi-objective optimization.read more
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An annual load forecasting model based on support vector regression with differential evolution algorithm
TL;DR: The effectiveness of this model has been proved by the final simulation which shows that the proposed model outperforms the SVR model with default parameters, back propagation artificial neural network (BPNN) and regression forecasting models in the annual load forecasting.
Journal ArticleDOI
A survey on multi-objective evolutionary algorithms for the solution of the environmental/economic dispatch problems
Boyang Qu,Boyang Qu,Yongsheng Zhu,Yongsheng Zhu,Yuechao Jiao,M. Y. Wu,Ponnuthurai Nagaratnam Suganthan,Jing Liang,Jing Liang +8 more
TL;DR: The state-of-the-art of research related to the development of efficient multi-objective evolutionary algorithms (MOEAs) to solve environmental/economic dispatch (EED) problems is surveyed.
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A modified teaching–learning based optimization for multi-objective optimal power flow problem
TL;DR: In this paper, a modified teaching-learning based optimization algorithm is analyzed to solve the multi-objective optimal power flow problem considering the total fuel cost and total emission of the units.
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Artificial bee colony algorithm with dynamic population size to combined economic and emission dispatch problem
TL;DR: A new algorithm namely Artificial Bee Colony with Dynamic Population size (ABCDP) is introduced which is using similar mechanisms defined in IABC-LS without using many parameters to be tuned, to prove the efficiency and robustness of algorithm in power dispatch.
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An improved bacterial foraging algorithm for combined static/dynamic environmental economic dispatch
TL;DR: An improved bacterial foraging algorithm (IBFA) is proposed in which a parameter automation strategy and crossover operation is used in micro BFA to improve computational efficiency and is found to be better.
References
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Journal ArticleDOI
A fast and elitist multiobjective genetic algorithm: NSGA-II
TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
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Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces
Rainer Storn,Kenneth Price +1 more
TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
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Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach
Eckart Zitzler,Lothar Thiele +1 more
TL;DR: The proof-of-principle results obtained on two artificial problems as well as a larger problem, the synthesis of a digital hardware-software multiprocessor system, suggest that SPEA can be very effective in sampling from along the entire Pareto-optimal front and distributing the generated solutions over the tradeoff surface.
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Performance assessment of multiobjective optimizers: an analysis and review
TL;DR: This study provides a rigorous analysis of the limitations underlying this type of quality assessment in multiobjective evolutionary algorithms and develops a mathematical framework which allows one to classify and discuss existing techniques.
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Handling multiple objectives with particle swarm optimization
TL;DR: An approach in which Pareto dominance is incorporated into particle swarm optimization (PSO) in order to allow this heuristic to handle problems with several objective functions and indicates that the approach is highly competitive and that can be considered a viable alternative to solve multiobjective optimization problems.