Multileveled Symbiotic Evolutionary Algorithm: Application to FMS Loading Problems

Authors:
Jae Yun Kim;Yeo Keun Kim
Affiliations:
School of Business Administration, Chonnam National University, Gwangju, Republic of Korea 500-757;Department of Industrial Engineering, Chonnam National University, Gwangju, Republic of Korea 500-757
Venue:
Applied Intelligence
Year:
2005

Citing 9
Cited 4

Genetic algorithms + data structures = evolution programs (2nd, extended ed.)

Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Integrating machining speed, part selection and machine loading decisions in flexible manufacturing systems

Computers and Industrial Engineering
Evolutionary computation: toward a new philosophy of machine intelligence

Evolutionary computation: toward a new philosophy of machine intelligence
Artificial symbiogenesis

Artificial Life
The design and analysis of a computational model of cooperative coevolution

The design and analysis of a computational model of cooperative coevolution
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
A Coevolutionary Algorithm for Balancing and Sequencing in Mixed Model Assembly Lines

Applied Intelligence
An Endosymbiotic Evolutionary Algorithm for Optimization

Applied Intelligence
Optimal design of flywheels using an injection island genetic algorithm

Artificial Intelligence for Engineering Design, Analysis and Manufacturing

Modified immune algorithm for job selection and operation allocation problem in flexible manufacturing systems

Advances in Engineering Software
Part-selection and machine-loading problems in a flexible manufacturing system environment: a heuristic approach based on reallocation paradigm

International Journal of Computer Applications in Technology
A mathematical model and a genetic algorithm for two-sided assembly line balancing

Computers and Operations Research
Multi-objective FMS process planning with various flexibilities using a symbiotic evolutionary algorithm

Computers and Operations Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

Recently, there has been an increasing effort to address integrated problems that are composed of multiple interrelated sub-problems. Many integrated problems in the real world have a multileveled structure. This paper proposes a new method of solving integrated and multileveled problems. The proposed method is named Multileveled Symbiotic Evolutionary Algorithm (MSEA). MSEA is an evolutionary algorithm that imitates the process of symbiotic evolution, including endosymbiotic evolution. It is designed to promote the balance of population diversity and population convergence. To verify its applicability, MSEA is applied to loading problems of flexible manufacturing systems with various flexibilities. Through computer experiments, the features of MSEA are shown and their effects on search capability are discussed. The proposed algorithm is also compared with existing ones in terms of solution quality. The experimental results confirm the effectiveness of our approach.