As one of the most competitive approaches to multi-objective optimization, evolutionary algorithms have been shown to obtain very good results for many realworld multi-objective problems. One of the issues that can affect the performance of these algorithms is the uncertainty in the quality of the solutions which is usually represented with the noise in the objective values. Therefore, handling...

In many optimization problems in practice, multiple objectives have to be optimized at the same time. Some multi-objective problems are characterized by multiple connected Pareto-sets at different parts in decision space – also called equivalent Pareto-subsets. We assume that the practitioner wants to approximate all Pareto-subsets to be able to choose among various solutions with different cha...

Multi-objective problems are a category of optimization problem that contains more than one objective function and these objective functions must be optimized simultaneously. Should the objective functions be conflicting, then a set of solutions instead of a single solution is required. This set is known as Pareto optimal. Multi-objective optimization problems arise in many real world applicati...

In most real world multi-objective optimization problems the objectives are conflicting and therefore, they do not lend themselves to a single solution but result in a set of non-dominating solutions. Several issues arise in Multi-objective Optimization. Firstly, the entire search space has to be searched (in order to find all the good nondominated solutions) without getting stuck in local opti...

in this paper, experimentally derived correlations of heat transfer and pressure drop are used in a pareto based multi-objective optimization (moo) approach to find the best possible combinations of heat transfer and pressure drop of tio2-water nanofluid flow in tubes fitted with multiple twisted tape inserts in different arrangement. in this study there are four independent design variables: t...

In some real applications of Statistical Process Control it is necessary to design a control chart to not detect small process shifts, but keeping a good performance to detect moderate and large shifts in the quality. In this work we develop a new quality control chart, the synthetic T control chart, which can be designed to cope with this objective. A multi-objective optimization is carried ou...

The use of evolutionary algorithms (EAs) to solve problems with multiple objectives (known as Multi-objective Optimization Problems (MOPs)) 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 ...

Nowadays, with extending applications of bi-layer metallic sheets in different industrial sectors, accurate specification of each layer is very prominent to achieve desired properties. In order to predict behavior of sheets under different forming modes and determining rupture limit and necking, the concept of Forming Limit Diagram (FLD) is used. Optimization problem with objective functions an...

in this paper, we proposed an algorithm for solving the problem of task scheduling using particle swarm optimization algorithm, with changes in the selection and removing the guide and also using the technique to get away from the bad, to move away from local extreme and diversity. scheduling algorithms play an important role in grid computing, parallel tasks scheduling and sending them to appr...