 Evolutionary multi-objective optimization (EMOO) finds a set of Pareto solutions rather than any single aggregated optimal solution for a multi-objective problem. The purpose of this paper is to describe a newly developed evolutionary approach --Paretobased multi-objective differential evolution (MODE). In this paper, the concept of differential evolution, which is well-known in the continuou...

in (golchin a. and rezaei p., subpullbacks and flatness properties of s-posets. comm. algebra. 37: 1995-2007 (2009)) study was initiated of flatness properties of right -posets  over a pomonoid  that can be described by surjectivity of  corresponding to certain (sub)pullback diagrams and new properties such as  and  were discovered. in this article first of all we describe po-flatness propertie...

PT. XYZ is a company engaged in the packaging printing industry. In production process, defective products are quite natural, but every has tolerance limit or maximum for as their reference order to know whether company's process and quality good not. The itself percentage target products, which 1.5% of one week's production. reality on ground, exceeds limit. From background these problems need...

Abstract This paper proposes a method for improving the diversity of Pareto front and uniformity non-dominated solution distributions in fast elitist sorting genetic algorithm (NSGA-II), which is an evolutionary multi-objective optimization algorithm. Conventional NSGA-II has excellent convergence to front, but it been reported that some test cases, does not produce more diverse distribution th...

The higher-order linear moments (LH-moments) method is one of the most commonly used methods for estimating parameters probability distributions in flood frequency analysis without sample censoring. This article presents relationships necessary to estimate eight analysis. Eight three using first- and second-order LH-moments are presented, namely Pearson V distribution, CHI inverse Wilson–Hilfer...

Most engineering optimization uses multiple objective functions rather than single objective function. To realize an artificial life algorithm based multi-objective optimization, this paper proposes a Pareto artificial life algorithm that is capable of searching Pareto set for multi-objective function solutions. The Pareto set of optimum solutions is found by applying two objective functions fo...

We propose a new approach to convex nonlinear multiobjective optimization that captures the geometry of the Pareto set by generating a discrete set of Pareto points optimally. We show that the problem of finding an optimal representation of the Pareto surface can be formulated as a mathematical program with complementarity constraints. The complementarity constraints arise from modeling the set...

Previous research using plot-level agricultural data from Burkina Faso found that the allocation of resources within African households was Pareto inefficient, contradicting most collective models of intrahousehold bargaining. I provide an explanation for these households' Pareto inefficient behavior and I test its robustness using an alternative dataset also collected in Burkina Faso. Househol...