Penerapan Multi-Objective Optimization on the Basis of Ratio (Moora) Dalam Penentuan Asisten Laboratorium

Penerimaan asisten laboratorim merupakan tahap proses perekrutan yang dilakukan oleh penanggung jawab lab, terdapat beberapa kriteria yang dijadikan patokan penilaian dari kualifikasi masing-masing calon asisten yang melamar. Penelitian ini bertujuan untuk menerapkan Meotde Multi-Objective Optimization on The Basis Of Ratio (MOORA) dalam penentuan aisten laboratorium. Terdapat 3 kriteria yaitu Nilai Ujian, Indeks Prestasi dan Semester. Alternatif dengan rangking nomor 1 merupakan alternatif dengan nilai tertinggi pada perhitungan Metode Moor

Multi-objective optimization shapes ecological variation

Ecological systems contain a huge amount of quantitative variation between and within species and locations, which makes it difficult to obtain unambiguous verification of theoretical predictions. Ordinary experiments consider just a few explanatory factors and are prone to providing oversimplified answers because they ignore the complexity of the factors that underlie variation. We used multi-objective optimization (MO) for a mechanistic analysis of the potential ecological and evolutionary causes and consequences of variation in the life-history traits of a species of moth. Optimal life-history solutions were sought for environmental conditions where different life stages of the moth were subject to predation and other known fitness-reducing factors in a manner that was dependent on the duration of these life stages and on variable mortality rates. We found that multi-objective optimal solutions to these conditions that the moths regularly experience explained most of the life-history variation within this species. Our results demonstrate that variation can have a causal interpretation even for organisms under steady conditions. The results suggest that weather and species interactions can act as underlying causes of variation, and MO acts as a corresponding adaptive mechanism that maintains variation in the traits of organisms

Multi-objective global optimization for hydrologic models

The development of automated (computer-based) calibration methods has focused mainly on the selection of a single-objective measure of the distance between the model-simulated output and the data and the selection of an automatic optimization algorithm to search for the parameter values which minimize that distance. However, practical experience with model calibration suggests that no single-objective function is adequate to measure the ways in which the model fails to match the important characteristics of the observed data. Given that some of the latest hydrologic models simulate several of the watershed output fluxes (e.g. water, energy, chemical constituents, etc.), there is a need for effective and efficient multi-objective calibration procedures capable of exploiting all of the useful information about the physical system contained in the measurement data time series. The MOCOM-UA algorithm, an effective and efficient methodology for solving the multiple-objective global optimization problem, is presented in this paper. The method is an extension of the successful SCE-UA single-objective global optimization algorithm. The features and capabilities of MOCOM-UA are illustrated by means of a simple hydrologic model calibration study