FUZZY-BASED REAL-CODED GENETIC ALGORITHM FOR OPTIMIZING NON-CONVEX ENVIRONMENTAL ECONOMIC LOSS DISPATCH

A non-convex Environmental Economic Loss Dispatch (NCEELD) is a constrained multi-objective optimization problem that has been solved for assigning generation cost to all the generators of the power network with equality and inequality constraints. The objectives considered for simultaneous optimization are emission, economic load and network loss dispatch. The valve-point loading, prohibiting operating zones and ramp rate limit issues have also been taken into consideration in the generator fuel cost. The tri-objective problem is transformed into a single objective function via the price penalty factor. The NCEELD problem is simultaneously optimized using a fuzzy-based real-coded genetic algorithm (GA). The proposed technique determines the best solution from a Pareto optimal solution set based on the highest rank. The efficacy of the projected method has been demonstrated on the IEEE 30-bus network with three and six generating units. The attained results are compared to existing results and found superior in terms of finding the best-compromise solution over other existing methods such as GA, particle swarm optimization, flower pollination algorithm, biogeography-based optimization and differential evolution. The statistical analysis has also been carried out for convex multi-objective problem

Dynamic Economic Dispatch Considering Emission Using Multi-Objective Flower Pollination Algorithm

This paper presents dynamic economic dispatch considering emission constraint using multi-objective flower pollination algorithm (MOFPA) method. Minimizing the operating cost in economic dispatch is no longer permitted to be the only criterion for dispatching the electric power due to environmental and health consideration. Besides, dynamic constraints such as output power ramp rates have to be considered to avoid excessive fatigue in plant structure, which leads to the necessity of solving this problem using improved economic dispatch called dynamic economic emission dispatch (DEED). In this paper, fuel cost and NOx emission functions are considered as a single-objective optimization problem and both of them can be formulated by using multi-objective optimization. This multi-objective optimization function will be solved using Flower Pollination Algorithm (FPA). This algorithm is a new nature-inspired algorithm, based on the characteristics of flowering plants. Based on the literature survey, the cost function is taken as a quadratic function and solved for economic and emission dispatch problem. The IEEE 30-bus system with 6- generation units is presented as a plant to illustrate the application of the proposed problem.

eulerForce: Force-directed Layout for Euler Diagrams

Euler diagrams use closed curves to represent sets and their relationships. They facilitate set analysis, as humans tend to perceive distinct regions when closed curves are drawn on a plane. However, current automatic methods often produce diagrams with irregular, non-smooth curves that are not easily distinguishable. Other methods restrict the shape of the curve to for instance a circle, but such methods cannot draw an Euler diagram with exactly the required curve intersections for any set relations. In this paper, we present eulerForce, as the first method to adopt a force-directed approach to improve the layout and the curves of Euler diagrams generated by current methods. The layouts are improved in quick time. Our evaluation of eulerForce indicates the benefits of a force-directed approach to generate comprehensible Euler diagrams for any set relations in relatively fast time

An improved optimization technique for estimation of solar photovoltaic parameters

The nonlinear current vs voltage (I-V) characteristics of solar PV make its modelling difficult. Optimization techniques are the best tool for identifying the parameters of nonlinear models. Even though, there are different optimization techniques used for parameter estimation of solar PV, still the best optimized results are not achieved to date. In this paper, Wind Driven Optimization (WDO) technique is proposed as the new method for identifying the parameters of solar PV. The accuracy and convergence time of the proposed method is compared with results of Pattern Search (PS), Genetic Algorithm (GA), and Simulated Annealing (SA) for single diode and double diode models of solar PV. Furthermore, for performance validation, the parameters obtained through WDO are compared with hybrid Bee Pollinator Flower Pollination Algorithm (BPFPA), Flower Pollination Algorithm (FPA), Generalized Oppositional Teaching Learning Based Optimization (GOTLBO), Artificial Bee Swarm Optimization (ABSO), and Harmony Search (HS). The obtained results clearly reveal that WDO algorithm can provide accurate optimized values with less number of iterations at different environmental conditions. Therefore, the WDO can be recommended as the best optimization algorithm for parameter estimation of solar PV

Structured Learning of Tree Potentials in CRF for Image Segmentation

We propose a new approach to image segmentation, which exploits the advantages of both conditional random fields (CRFs) and decision trees. In the literature, the potential functions of CRFs are mostly defined as a linear combination of some pre-defined parametric models, and then methods like structured support vector machines (SSVMs) are applied to learn those linear coefficients. We instead formulate the unary and pairwise potentials as nonparametric forests---ensembles of decision trees, and learn the ensemble parameters and the trees in a unified optimization problem within the large-margin framework. In this fashion, we easily achieve nonlinear learning of potential functions on both unary and pairwise terms in CRFs. Moreover, we learn class-wise decision trees for each object that appears in the image. Due to the rich structure and flexibility of decision trees, our approach is powerful in modelling complex data likelihoods and label relationships. The resulting optimization problem is very challenging because it can have exponentially many variables and constraints. We show that this challenging optimization can be efficiently solved by combining a modified column generation and cutting-planes techniques. Experimental results on both binary (Graz-02, Weizmann horse, Oxford flower) and multi-class (MSRC-21, PASCAL VOC 2012) segmentation datasets demonstrate the power of the learned nonlinear nonparametric potentials.Comment: 10 pages. Appearing in IEEE Transactions on Neural Networks and Learning System