In the past few years, multi-objective optimization (MOO) algorithms have been extensively applied in several fields including engineering design problems. A major reason is the advancement of evolutionary multi-objective optimization (EMO) algorithms that are able to find a set of non-dominated points spread on the respective Pareto-optimal front in a single simulation. Besides just finding a ...

Multi-objective genetic algorithm (GAs) is used for pump design pareto optimization, competing objectives for centrifugal pump design are total head (H), input power (Ps), hydraulic efficiency ( H η ), and input parameter are capacity (Q), and the outer radius of the impeller ( ). Multi-objective presents a set of compromised solution, and provides non-dominated optimal choices for designer. 2 ...

this paper explores the capabilities of multi-objective particle swarm optimization algorithmin a simulation-optimization model for solving waste load allocation problems. the main goals are totaltreatment costs, violation of the water quality standards and equity. in this research, the water qualitysimulation model is coupled with a multi-objective optimization model, mopso. in order to derive...

in this paper, multi-objective uniform-diversity genetic algorithm (muga) with a diversity preserving mechanism called the ε-elimination algorithm is used for pareto optimization of 5-degree of freedom vehicle vibration model considering the five conflicting functions simultaneously. the important conflicting objective functions that have been considered in this work are, namely, vertical accel...

The most recent approaches of multi-objective optimization constitute application of meta-heuristic algorithms for which, parameter tuning is still a challenge. The present work hybridizes swarm intelligence with fuzzy operators to extend crisp values of the main control parameters into especial fuzzy sets that are constructed based on a number of prescribed facts. Such parameter-less particle ...

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...

The problems presented and analyzed in this paper are interesting testproblems for multi-objective optimization. The aim is to find the distance to two points in a d-dimensional space. Closed analytical expressions of all pareto-optimal fronts and pareto-optimal sets will be derived in this paper. Moreover, a discussion of the characteristics of the properties of these sets is given. Depending ...

In this paper multi-objective genetic algorithms are employed for Pareto approach optimization of ideal Turboshaft engines. In the multi-objective optimization a number of conflicting objective functions are to be optimized simultaneously. The important objective functions that have been considered for optimization are specific thrust 0 ( / ) & F m , specific fuel consumption ( P S ), output sh...

In this paper, a multi-objective method is used to optimize a heat recovery steam generator (HRSG). Two objective functions have been used in the optimization, which are irreversibility and HRSG equivalent volume. The former expresses the exergetic efficiency and the latter demonstrates the cost of the HRSG. Decision variables are geometric and operational parameters of the HRSG. The result...

In this paper, a combination method has been developed by coupling Multi-Objective Genetic Algorithms (MOGA) and Finite Element Method (FEM). This method has been applied for determination of the optimal stacking sequence of laminated composite plate against buckling. The most important parameters in optimization of a laminated composite plate such as, angle, thickness, number, and material of ...