Preference-Based Solution Selection Algorithm for Evolutionary Multiobjective Optimization

Since multiobjective evolutionary algorithms (MOEAs) provide a set of nondominated solutions, decision making of selecting a preferred one out of them is required in real applications. However, there has been some research on MOEA in which the user's preferences are incorporated for this purpos...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation Vol. 16; no. 1; pp. 20 - 34
Main Authors: Kim, Jong-Hwan, Han, Ji-Hyeong, Kim, Ye-Hoon, Choi, Seung-Hwan, Kim, Eun-Soo
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.02.2012
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Applied sciences
Classical and quantum physics: mechanics and fields
Computer science; control theory; systems
Data processing. List processing. Character string processing
Decision making
Decision theory. Utility theory
Evolutionary computation
Exact sciences and technology
Fuzzy integral
fuzzy path planning
Memory organisation. Data processing
multiobjective quantum-inspired evolutionary algorithm
multiple criteria decision making (MCDM)
Operational research and scientific management
Operational research. Management science
Operations research
Optimization
Physics
Power measurement
preference-based MOEA
Preferences
Quantum computation
Quantum information
Robots
Simulation
Software
Sorting
Studies
Weight measurement
Nondominated set
Moving robot
Computer simulation
Decision support system
Multicriteria analysis
Decision making
Evolutionary algorithm
Multiobjective programming
preference-based MOEA
Dominance
Routing
Path planning
fuzzy path planning
Fuzzy integral
Fuzzy logic
Quantum computation
multiobjective quantum-inspired evolutionary algorithm
Genetic algorithm
Efficiency
Preference
Optimal trajectory
multiple criteria decision making (MCDM)
User behavior
Mathematical programming
ISSN:1089-778X, 1941-0026
Online Access:Get full text
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Summary:Since multiobjective evolutionary algorithms (MOEAs) provide a set of nondominated solutions, decision making of selecting a preferred one out of them is required in real applications. However, there has been some research on MOEA in which the user's preferences are incorporated for this purpose. This paper proposes preference-based solution selection algorithm (PSSA) by which user can select a preferred one out of nondominated solutions obtained by any one of MOEAs. The PSSA, which is a kind of multiple criteria decision making (MCDM) algorithm, represents user's preference to multiple objectives or criteria as a degree of consideration by fuzzy measure and globally evaluates obtained solutions by fuzzy integral. The PSSA is also employed in each and every generation of evolutionary process to propose multiobjective quantum-inspired evolutionary algorithm with preference-based selection (MQEA-PS). To demonstrate the effectiveness of PSSA and MQEA-PS, computer simulations and real experiments on evolutionary multiobjective optimization for the fuzzy path planner of mobile robot are carried out. Computer simulation and experiment results show that the user's preference is properly reflected in the selected solution. Moreover, MQEA-PS shows improved performance for the DTLZ problems and fuzzy path planner optimization problem compared to MQEA with dominance-based selection and other MOEAs like NSGA-II and MOPBIL.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2010.2098412