Elitist - Multi-objective Differential Evolution (E-MODE) Algorithm for Multi-objective Optimization

نویسندگان

  • B. V. Babu
  • Ashish M. Gujarathi
چکیده

Several problems in the engineering domain are multi-objective in nature. The solution to multi-objective optimization is a set of solutions rather than a single point solution. Such a set of non-dominated solutions are called Pareto optimal solutions or non-inferior solutions. In this paper, a new algorithm, Elitist-Multi-objective Differential Evolution (E-MODE) is proposed. The proposed algorithm is applied successfully on several test functions, and the results are discussed extensively. Results obtained from the proposed algorithm are compared with those obtained using Multi-objective Differential Evolution (MODE) algorithm. E-MODE is found to give better solutions in terms of wide range of solutions, spread, and diversity of Pareto front than those obtained using MODE.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Pareto Optimal Balancing of Four-bar Mechanisms Using Multi-Objective Differential Evolution Algorithm

Four-bar mechanisms are widely used in the industry especially in rotary engines. These mechanisms are usually applied for attaining a special motion duty like path generation; their high speeds in the industry cause an unbalancing problem. Hence, dynamic balancing is essential for their greater efficiency. In this research study, a multi-objective differential evolution algorithm is used for P...

متن کامل

MULTI-OBJECTIVE OPTIMIZATION OF ARCH DAMS USING DIFFERENTIAL EVOLUTION METHODS

For optimization of real-world arch dams, it is unavoidable to consider two or more conflicting objectives. This paper employs two multi-objective differential evolution algorithms (MoDE) in combination of a parallel working MATLAB-APDL code to obtain a set of Pareto solutions for optimal shape of arch dams. Full dam-reservoir interaction subjected to seismic loading is considered. A benchmark ...

متن کامل

Multi-Objective Optimization Using Differential Evolution

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...

متن کامل

EMCSO: An Elitist Multi-Objective Cat Swarm Optimization

This paper introduces a novel multi-objective evolutionary algorithm based on cat swarm optimizationalgorithm (EMCSO) and its application to solve a multi-objective knapsack problem. The multi-objective optimizers try to find the closest solutions to true Pareto front (POF) where it will be achieved by finding the less-crowded non-dominated solutions. The proposed method applies cat swarm optim...

متن کامل

Pareto Optimal Multi-Objective Dynamical Balancing of a Slider-Crank Mechanism Using Differential Evolution Algorithm

The present paper aims to improve the dynamical balancing of a slider-crank mechanism. This mechanism has been widely used in internal combustion engines, especially vehicle engines; hence, its dynamical balancing is important significantly. To have a full balance mechanism, the shaking forces and shaking moment of foundations should be eliminated completely. However, this elimination is usuall...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2007