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This paper describes a new multi-objective evolutionary programming (MOEP) method to solve the combined economic emission dispatch (CEED) and economic emission dispatch (EED) problems. The CEED is a bi-objective optimization problem that considers two objectives such as fuel cost and NOx emission. It is converted into a single objective optimization problem using weighted sum method. The EED is a...
This paper examines the Pareto front of a simple fossil fuel power plant using a common third-order model. This front is first examined analytically. Then the power plant model is transferred over to a steady-state model using a static neural network and the front is estimated using various geometric and heuristic approaches. This paper is the first of the two stages to eliminate the need for a human...
Differential evolution has seen growing popularity as an effective yet simple evolutionary algorithm. Its main feature is that the difference between population members is used for the mutation process instead of randomly generated values. Its ease of use and implementation make it a more attractive approach to evolutionary algorithms as it is simpler to explain the choice of parameters to fit a given...
This paper describes a new multi-objective evolutionary programming (MOEP) method to solve the combined economic emission dispatch (CEED) problem. CEED is a multi-objective optimization problem by considering the fuel cost and emission as the objectives. It is converted into single objective optimization problem using weighted sum method. Hence the MOEP is proposed by employing the non-dominated solution...
In this study the state-of-the-art extended particle swarm optimization (PSO) methods for solving multi-objective problems are represented. We emphasize in those, which use the co-evolution technique of the parallel vector evaluated PSO (VEPSO), analyzed and applied in a multi-objective problem of power systems. Specifically, reactive power control is formulated as a multi-objective optimization problem...
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