In this work, a new multi-objective optimization algorithm called learner performance-based behavior is proposed. The proposed based on the process of moving graduated students from high school to college. technique produces set non-dominated solutions. To test ability and efficacy algorithm, it applied group benchmarks five real-world engineering problems. Several widely used metrics are emplo...

Software maintenance is an important step in the software lifecycle. module clustering a HHMO_CF_GDA optimization problem involving several targets that require minimization of coupling and maximization cohesion. Moreover, multi-objective involves assembling specific group modules according to cluster criteria. classifies into different clusters enhance process. A structure with low high cohesi...

multi objective quadratic fractional programming (moqfp) problem involves optimization of several objective functions in the form of a ratio of numerator and denominator functions which involve both contains linear and quadratic forms with the assumption that the set of feasible solutions is a convex polyhedral with a nite number of extreme points and the denominator part of each of the object...

calculating total cast of bank resources procurement methods which include current -free loan deposit, saving interest-free loan deposit, regular and net short-term investment deposit, long-term investment deposit and surety bond cash deposit and presenting their optimal integration require precise scientific studies. hence, this study is an attempt to know which methods are the best optimal in...

While evolutionary computing inspired approaches to multi-objective optimization have many advantages over conventional approaches; they generally do not explicitly exploit directional/gradient information. This can be inefficient if the underlying objectives are reasonably smooth, and this may limit the application of such approaches to real-world problems. This paper develops a local framewor...

This paper sets out a number of the popular areas from the literature in multi-objective supervised learning, along with simple examples. It continues by highlighting some specific areas of interest/concern when dealing with multi-objective supervised learning problems, and highlights future areas of potential research.