A Hybrid Multi-objective Evolutionary Approach to Engineering Shape Design

Authors:
Kalyanmoy Deb;Tushar Goel
Affiliations:
-;-
Venue:
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Year:
2001

Citing 5
Cited 13

Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization

Proceedings of the 5th International Conference on Genetic Algorithms
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II

PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Muiltiobjective optimization using nondominated sorting in genetic algorithms

Evolutionary Computation
The gambler's ruin problem, genetic algorithms, and the sizing of populations

Evolutionary Computation
A multi-objective genetic local search algorithm and itsapplication to flowshop scheduling

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews

Parallel and Hybrid Models for Multi-objective Optimization: Application to the Vehicle Routing Problem

PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Target matching problems and an adaptive constraint strategy for multiobjective design optimization using genetic algorithms

Computers and Structures
On the flexible applied boundary and support conditions of compliant mechanisms using customized evolutionary algorithm

SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
Graph partitioning by multi-objective real-valued metaheuristics: A comparative study

Applied Soft Computing
Domain-specific initial population strategy for compliant mechanisms using customized genetic algorithm

Structural and Multidisciplinary Optimization
Multiobjective scheduling of jobs with incompatible families on parallel batch machines

EvoCOP'06 Proceedings of the 6th European conference on Evolutionary Computation in Combinatorial Optimization
A novel approach for traffic accidents sanitary resource allocation based on multi-objective genetic algorithms

Expert Systems with Applications: An International Journal
Advances in evolutionary multi-objective optimization

SSBSE'12 Proceedings of the 4th international conference on Search Based Software Engineering
Cellular growth algorithms for shape design of truss structures

Computers and Structures
A framework to schedule surgeries in an eye hospital

Proceedings of the Winter Simulation Conference
A framework to schedule surgeries in an eye hospital

Proceedings of the Winter Simulation Conference
Memetic algorithm based task scheduling using probabilistic local search

SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
Comprehensive Survey of the Hybrid Evolutionary Algorithms

International Journal of Applied Evolutionary Computation

Quantified Score

Hi-index	0.01

Visualization

Abstract

Evolutionary optimization algorithms work with a population of solutions, instead of a single solution. Since multi-objective optimization problems give rise to a set of Pareto-optimal solutions, evolutionary optimization algorithms are ideal for handling multi-objective optimization problems. Over many years of research and application studies have produced a number of efficient multi-objective evolutionary algorithms (MOEAs), which are ready to be applied to real-world problems. In this paper, we propose a practical approach, which will enable an user to move closer to the true Pareto-optimal front and simultaneously reduce the size of the obtained non-dominated solution set. The efficacy of the proposed approach is demonstrated in solving a number of mechanical shape optimization problems, including a simply-supported plate design, a cantilever plate design, a hoister design, and a bicycle frame design. The results are interesting and suggest immediate application of the proposed technique in more complex engineering design problems.