A simulated annealing-based multiobjective optimization algorithm: AMOSA

被引:654
作者
Bandyopadhyay, Sanghamitra [1 ]
Saha, Sriparna [1 ]
Maulik, Ujjwal [2 ]
Deb, Kalyanmoy [3 ]
机构
[1] Indian Stat Inst, Machine Intelligence Unit, Kolkata 700108, India
[2] Jadavpur Univ, Dept Comp Sci & Engn, Kolkata 700032, India
[3] Indian Inst Technol, Dept Mech Engn, KanGAL, Kanpur 208016, Uttar Pradesh, India
关键词
amount of domination; archive; clustering; multi-objective optimization (MOO); Pareto-optimal (PO); simulated annealing (SA);
D O I
10.1109/TEVC.2007.900837
中图分类号
TP18 [人工智能理论];
学科分类号
081104 [模式识别与智能系统]; 0812 [计算机科学与技术]; 0835 [软件工程]; 1405 [智能科学与技术];
摘要
This paper describes a simulated annealing based multiobjective optimization algorithm that incorporates the concept of archive in order to provide a set of tradeoff solutions for the problem under consideration. To determine the acceptance probability of a new solution vis-a-vis the current solution, an elaborate procedure is followed that takes into account the domination status of the new solution with the current solution, as well as those in the archive. A measure of the amount of domination between two solutions is also used for this purpose. A complexity analysis of the proposed algorithm is provided. An extensive comparative study of the. proposed algorithm with two other existing and well-known multiobjective evolutionary algorithms (MOEAs) demonstrate the effectiveness of the former with respect to five existing performance measures, and several test problems of varying degrees of difficulty. In particular, the proposed algorithm is found to be significantly superior for many objective test problems (e.g., 4, 5, 10, and 15 objective problems), while recent studies have indicated that the Pareto ranking-based MOEAs perform poorly for such problems. In a part of the investigation, comparison of the real-coded version of the proposed algorithm is conducted with a very recent multiobjective simulated annealing algorithm, where the performance of the former is found to be generally superior to that of the latter.
引用
收藏
页码:269 / 283
页数:15
相关论文
共 38 条
[1]
[Anonymous], J MULTICRITERIA DECI, DOI DOI 10.1002/(SICI)1099-1360(199907)8:4{
[2]
Multiobjective GAs, quantitative indices, and pattern classification [J].
Bandyopadhyay, S ;
Pal, SK ;
Aruna, B .
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2004, 34 (05) :2088-2099
[3]
Clustering using simulated annealing with probabilistic redistribution [J].
Bandyopadhyay, S ;
Maulik, U ;
Pakhira, MK .
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 2001, 15 (02) :269-285
[4]
BANDYOPADHYAY S, 2007, REMOTE SENSING, V45, P1506
[5]
Quantitative comparison of the performance of SAR segmentation algorithms [J].
Caves, R ;
Quegan, S ;
White, R .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 1998, 7 (11) :1534-1546
[6]
Coello C. A. C., 2002, EVOLUTIONARY ALGORIT
[7]
Czyzzak P., 1998, Journal of Multi-Criteria Decision Analysis, V7, P34, DOI [DOI 10.1002/(SICI)1099-1360(199801)7:1<34::AID-MCDA161>3.0.CO
[8]
2-6, 10.1002/(SICI)1099-1360(199801)7:13.0.CO
[9]
2-6, DOI 10.1002/(SICI)1099-1360(199801)7:13.0.CO
[10]
2-6]