Pareto Differential Evolution Research Articles

Memetic Elitist Pareto Differential Evolution algorithm based Radial Basis Function Networks for classification problems

This paper presents a new multi-objective evolutionary hybrid algorithm for the design of Radial Basis Function Networks (RBFNs) for classification problems. The algorithm, MEPDEN, Memetic Elitist Pareto evolutionary approach based on the Non-dominated Sorting Differential Evolution (NSDE) multi-objective evolutionary algorithm which has been adapted to design RBFNs, where the NSDE algorithm is augmented with a local search that uses the Back-propagation algorithm. The MEPDEN is tested on two-class and multiclass pattern classification problems. The results obtained in terms of Mean Square Error (MSE), number of hidden nodes, accuracy (ACC), sensitivity (SEN), specificity (SPE) and Area Under the receiver operating characteristics Curve (AUC), show that the proposed approach is able to produce higher prediction accuracies with much simpler network structures. The accuracy and complexity of the network obtained by the proposed algorithm are compared with Memetic Eilitist Pareto Non-dominated Sorting Genetic Algorithm based RBFN (MEPGAN) through statistical tests. This study showed that MEPDEN obtains RBFNs with an appropriate balance between accuracy and simplicity, outperforming the other method considered.

Economic environmental dispatch using multi-objective differential evolution

Economic environmental dispatch (EED) is an important optimization task in fossil fuel fired power plant operation for allocating generation among the committed units such that fuel cost and emission level are optimized simultaneously while satisfying all operational constraints. It is a highly constrained multiobjective optimization problem involving conflicting objectives with both equality and inequality constraints. In this paper, multi-objective differential evolution has been proposed to solve EED problem. Numerical results of three test systems demonstrate the capabilities of the proposed approach. Results obtained from the proposed approach have been compared to those obtained from pareto differential evolution, nondominated sorting genetic algorithm-II and strength pareto evolutionary algorithm 2.

Memetic pareto differential evolutionary artificial neural networks to determine growth multi-classes in predictive microbiology

The main objective of this research is to automatically design Artificial Neural Network models with sigmoid basis units for multiclassification tasks in predictive microbiology. The classifiers obtained achieve a double objective: a high classification level in the dataset and high classification levels for each class. The Memetic Pareto Differential Evolution Neural Network chosen to learn the structure and weights of the Neural Networks is a Differential Evolutionary approach based on the Pareto Differential Evolution multiobjective evolutionary algorithm. The Pareto Differential Evolution algorithm is augmented with a local search using the improved Resilient Backpropagation with backtracking–iRprop + algorithm. To analyze the robustness of this methodology, it has been applied to two complex classification problems in predictive microbiology (Staphylococcus aureus and Shigella flexneri). The results obtained show that the generalization ability and the classification rate in each class can be more efficiently improved within this multiobjective algorithm.

Multi-objective optimal power flow with FACTS devices

This paper presents multi-objective differential evolution to optimize cost of generation, emission and active power transmission loss of flexible ac transmission systems (FACTS) device-equipped power systems. In the proposed approach, optimal power flow problem is formulated as a multi-objective optimization problem. FACTS devices considered include thyristor controlled series capacitor (TCSC) and thyristor controlled phase shifter (TCPS). The proposed approach has been examined and tested on the modified IEEE 30-bus and 57-bus test systems. The results obtained from the proposed approach have been compared with those obtained from nondominated sorting genetic algorithm-II, strength pareto evolutionary algorithm 2 and pareto differential evolution.

An Algorithm Based on Differential Evolution for Multi-Objective Problems

Abstract: This paper presents a new multi-objective evolu-tionary algorithm based on differential evolution. The pro-posed approach adopts a secondary population in order to re-tain the nondominated solutions found during the evolution-ary process. Additionally, the approach also incorporates theconcept of † -dominance to get a good distribution of the solu-tions retained. The main goal of this work was to keep thefast convergence exhibited by Differential Evolution in globaloptimization when extending this heuristic to multi-objectiveoptimization. We adopted standard test functions and perfor-mance measures reported in the specialized literature to vali-date our proposal. Our results are compared with respect toanother multi-objective evolutionary algorithm based on differ-ential evolution (Pareto Differential Evolution) and with respectto two approaches that are representative of the state-of-the-artin the area: the NSGA-II and † -MOEA. I. Introduction Most real world problems involve the simultaneous opti-mization of two or more (often conflicting) objectives. Thesolution of such problems (called “multi-objective”) is differ-ent from that of a single-objective optimization problem. Themain difference is that multi-objective optimization problemsnormally have not one but a set of solutions which are allequally good.In the past, a wide variety of evolutionary algorithms (EA’s)have been used to solve multi-objective optimization prob-lems [5]. However, from the several types of EAs available,few researchers have attempted to extend Differential Evo-lution (DE) [25] to solve multi-objective optimization prob-lems. DE has been very successful in the solution of a va-riety of continuous (single-objective) optimization problemsin which it has shown a great robustness and a very fast con-vergence. These are precisely the characteristics of DE thatmake it attractive to extend it to solve multi-objective opti-mization problems.In this paper, we propose a new multi-objective evolution-ary algorithm based on differential evolution. Our approachuses an external archive in which the relaxed form of Paretodominance known as

THE PARETO DIFFERENTIAL EVOLUTION ALGORITHM

The use of evolutionary algorithms (EAs) to solve problems with multiple objectives (known as Vector Optimization Problems (VOPs)) has attracted much attention recently. Being population based approaches, EAs offer a means to find a group of pareto-optimal solutions in a single run. Differential Evolution (DE) is an EA that was developed to handle optimization problems over continuous domains. The objective of this paper is to introduce a novel Pareto Differential Evolution (PDE) algorithm to solve VOPs. The solutions provided by the proposed algorithm for five standard test problems, is competitive to nine known evolutionary multiobjective algorithms for solving VOPs.

An evolutionary artificial neural networks approach for breast cancer diagnosis

This paper presents an evolutionary artificial neural network (EANN) approach based on the pareto-differential evolution (PDE) algorithm augmented with local search for the prediction of breast cancer. The approach is named memetic pareto artificial neural network (MPANN). Artificial neural networks (ANNs) could be used to improve the work of medical practitioners in the diagnosis of breast cancer. Their abilities to approximate nonlinear functions and capture complex relationships in the data are instrumental abilities which could support the medical domain. We compare our results against an evolutionary programming approach and standard backpropagation (BP), and we show experimentally that MPANN has better generalization and much lower computational cost.