Journal
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
Volume 42, Issue 1, Pages 203-217Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMCB.2011.2161467
Keywords
Constrained evolutionary optimization; constraint-handling technique; dynamic hybrid framework (DyHF); multiobjective optimization
Categories
Funding
- National Natural Science Foundation of China [60805027, 90820302]
- Research Fund for the Doctoral Program of Higher Education of China [200805330005]
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Based on our previous work, this paper presents a dynamic hybrid framework, called DyHF, for solving constrained optimization problems. This framework consists of two major steps: global search model and local search model. In the global and local search models, differential evolution serves as the search engine, and Pareto dominance used in multiobjective optimization is employed to compare the individuals in the population. Unlike other existing methods, the above two steps are executed dynamically according to the feasibility proportion of the current population in this paper, with the purpose of reasonably distributing the computational resource for the global and local search during the evolution. The performance of DyHF is tested on 22 benchmark test functions. The experimental results clearly show that the overall performance of DyHF is highly competitive with that of a number of state-of-the-art approaches from the literature.
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