Effective multiobjective hydrologic model calibration P. Reed et al. Papers published in Hydrology and Earth System Sciences Discussions are under open-access review for the journal Hydrology and Earth System Sciences Effective multiobjective hydrologic model calibration P. Reed et al. Abstract This study provides a comprehensive assessment of state-of-the-art evolutionary multi-objective optim...

This paper gives a concise introduction to Multi-Objective Genetic Algorithms and FPGAs and it reveals that how Automatic Test Pattern Generation method can be formulated in terms of CNF form which in turn used to generate test patterns using Multi-Objective Genetic Algorithm. By applying a Multi-Objective Genetic Algorithm on this CNF form, it has been observed from the experiments that as the...

In trying to solve multiobjective optimization problems, many traditional methods scalar-ize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands the user to have knowledge about the underlying problem. Moreover, in solving multiobjective problems, designers may be interested in...

One of the major distinguishing features of the dynamic multiobjective optimization problems (DMOPs) is the optimization objectives will change over time, thus tracking the varying Pareto-optimal front becomes a challenge. One of the promising solutions is reusing the “experiences” to construct a prediction model via statistical machine learning approaches. However most of the existing methods ...

Multiobjective evolutionary algorithms (EAs) that use nondominated sorting and sharing have been criticized mainly for their: 1) ( ) computational complexity (where is the number of objectives and is the population size); 2) nonelitism approach; and 3) the need for specifying a sharing parameter. In this paper, we suggest a nondominated sorting-based multiobjective EA (MOEA), called nondominate...

abstract this paper presents a multiobjective power control algorithm that updates the transmitted power based on local information. the proposed algorithm is expanded by using multiobjective optimization schemes. the objectives to be optimized in this paper are determined so as to reduce the sinr fluctuations as well as maintaining the sinr to an acceptable level with minimizing an average tra...

<span id="docs-internal-guid-a50ef6a8-7fff-b6d7-8e58-d434be6097d4"><span>This paper proposes a novel approach based on the NSCE (elitist non dominated sorting cross entropy), for optimization of location and size flexible AC transmission system device (FACTS) namely: unified power flow controller (UPFC) to achieve optimal reactive (ORPF). In present work, main objective is minimize ...

In this paper a comparison of the most recent algorithms for Multiobjective Optimization is realized. For this comparison are used the followings algorithms: Strength Pareto Evolutionary Algorithm (SPEA), Pareto Archived Evolution Strategy (PAES), Nondominated Sorting Genetic Algorithm (NSGA II), Adaptive Pareto Algorithm (APA). The comparison is made by using five test functions.

Scheduling of semiconductor wafer manufacturing system is identified as a complex problem, involving multiple and conflicting objectives (minimization of facility average utilization, minimization of waiting time and storage, for instance) to simultaneously satisfy. In this study, we propose an efficient approach based on an artificial neural network technique embedded into a multiobjective gen...

In a recent paper by Tang, Reed and Wagener (2006, hereafter referred to as TRW) a comparison assessment was presented of three state-of-the-art evolutionary algorithms for multiobjective calibration of hydrologic models. Through three illustrative case studies, TRW demonstrate that the Strength Pareto Evolutionary Algorithm 2 (SPEA2) and Epsilon Dominance Nondominated Sorted Genetic Algorithm ...