Application of Memetic Algorithms in Multi-Criteria Structural Optimization

Peter Pechac,Milan Saga,Marcin Kubiak,Tomasz Domanski,Peter Weis

doi:10.26552/com.c.2017.2a.106-111

Abstract

The goal of this paper is to present new innovative algorithms for discrete structural optimization of problems involving multi-criteria objective function, which contains stress, displacement and frequency constraints. The algorithm is tested on a shell finite element structure, whose optimization variables are geometrical parameters and shell thicknesses. Performance of memetic algorithm will be compared with genetic algorithm.

Highlights

Problems solved by engineering practice often incorporate multiple constraints which have to be taken into account [1 - 5]
The remaining 70% of solutions are used to generate new solutions using crossover and mutation operators. 10% of the remaining solutions were improved by local search
Maximum displacement magnitude was 3.052 mm, which was lower than the displacement limit

Summary

Introduction

Problems solved by engineering practice often incorporate multiple constraints which have to be taken into account [1 - 5]. Problems involving multiple criteria can be often divided into multiple sub-problems, which have to be calculated separately. Multi-criteria objective function has to take into account all subproblems to properly evaluate quality of a solution [6]. Each subproblem affects the character of the objective function and so it can be hard to choose a single optimization method, which would be effective for all sub-problems. Modern optimization methods like Memetic Algorithms (MA) can be used to overcome these difficulties. Memetic algorithms are optimization methods which combine global search capabilities of global optimization methods like genetic algorithms or particle swarm optimization and fast converging local search methods like conjugate gradient method or simplex method [7]

Objectives

Methods

Findings

Conclusion