Browse > Article
http://dx.doi.org/10.3745/KIPSTB.2004.11B.7.841

Evolutionary Multi-Objective Optimization Algorithms for Uniform Distributed Pareto Optimal Solutions  

Jang Su-Hyun (명지대학교 대학원 컴퓨터공학과)
Yoon Byungjoo (명지대학교 컴퓨터공학과)
Publication Information
The KIPS Transactions:PartB / v.11B, no.7, 2004 , pp. 841-848 More about this Journal
Abstract
Evolutionary a1gorithms are well-suited for multi-objective optimization problems involving several, often conflicting objectives. Pareto-based evolutionary algorithms, in particular, have shown better performance than other multi-objective evolutionary algorithms in comparison. However, generalized evolutionary multi-objective optimization algorithms have a weak point, in which the distribution of solutions are not uni-formly distributed onto Pareto optimal front. In this paper, we propose an evolutionary a1gorithm for multi-objective optimization which uses seed individuals in order to overcome weakness of algorithms Published. Seed individual means a solution which is not located in the crowded region on Pareto front. And the idea of our algorithm uses seed individuals for reproducing individuals for next generation. Thus, proposed a1go-rithm takes advantage of local searching effect because new individuals are produced near the seed individual with high probability, and is able to produce comparatively uniform distributed pareto optimal solutions. Simulation results on five testbed problems show that the proposed algo-rithm could produce uniform distributed solutions onto pareto optimal front, and is able to show better convergence compared to NSGA-II on all testbed problems except multi-modal problem.
Keywords
Multi-objective Outimization; Evolutionary A1gorithms; Uniform Distributed Paretot Front; Seed Individual;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Kalyanmoy Deb, Samir Agrawal, Amrit Pratab, and T. Meyarivan, 'A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization : NSGA- II,' Proceedings of the Parallel Problem Solving from Nature VI Conference, pp.849-858, Springer, 2000
2 N. Srinivas and Kalyanrnoy Deb, 'Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms,' Evolutionary Computation, Vol.2, No.3 pp.221-248, 1994   DOI
3 Frank Kursawe, 'A Variant of evolution strategies for vector optimization,' In Parallel Problem Solving from Nature. 1st Workshop, PPSN I, Vol.496 of Lecture Notes in Computer Science, pp.193-197, 1991
4 Carlos M. Fonseca and Peter J. Fleming, 'Genetic Algorithms for Multiobjective Optimization : Formulation, Discussion and Generalization,' In Proceedings of the Fifth International Conference on Genetic Algorithms, pp.416-423, 1993
5 Jeffrey Horn and Nicholas Nafpliotis, 'Multiobjective Optimization using the Niched Pareto Ganetic Algorithm,' Technical Report IlliGAl Report 93005, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA, 1993
6 Carlos A. Coello Coello, 'An Updated Survey of GA-Based Multiobjective Optimization Techniques,' ACM Computing Surveys, Vol.32, No.2, pp.109-143, June, 2000   DOI   ScienceOn
7 Eckart Zitzler and Lothar Thiele, 'Multiobjective optimization using evolutionary algorithms - a Comparative study,' In Parallel Problem Solving from Nature V, pp.292-301, 1998
8 Eckart Zitzler, Kalyanmoy Deb, and Lothar Thiele, 'Comparison of Multiobjective Evolutionary Algorithms : Empirical Results,' Evolutionary Computation, Vol.8, No.2, pp.173- 195, 2000   DOI   ScienceOn
9 장수현, 윤병주, '유전자알고리즘에서의 실수처리 방법 비교', 정보처리학회논문지, Vol.5, No.2, pp.361-371, 1998
10 J. D. Schaffer, 'Multiple objective optimization with vector evaluated genetic algorithms,' In Genetic Algorithms and their Applications : Proceedings of the First International Conference on Genetic Algorithms, pp.93-100, 1985
11 Jason R. Schott, Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization. Master's thesis, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, Massachusetts, 1995
12 Eckart Zitzler, Marco Laumanns and Lothar Thiele, 'SPEA 2 : Improving the Strength Pareto Evolutionary Algorithm,' EUROGEN 2001, Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems, pp.12-21, 2001
13 Kalyanmoy Deb, 'Multi-Objective Genetic Algorithms : Problem Difficulties and Construction of Test Problems,' Evolutionary Computation, Vol.7, No.3, pp.205-230, 1999   DOI   ScienceOn
14 Carlos A. Coello Coello and Nareli Cruz Cortes, 'Solving Multiobjective Optimization Problems using an Artificial Immune System,' Technical Report EVOCINV-05-2002, Evolutionary Computation Group at CINVESTAV, Kluwer Academic, 2002