TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Adaptive Truncation technique for Constrained Multi-Objective Optimization |
| 영문제목(English Title) |
Adaptive Truncation technique for Constrained Multi-Objective Optimization |
| 저자(Author) |
Lei Zhang
Xiaojun Bi
Yanjiao Wang
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| 원문수록처(Citation) |
VOL 13 NO. 11 PP. 5489 ~ 5511 (2019. 11) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
The performance of evolutionary algorithms can be seriously weakened when constraints limit the feasible region of the search space. In this paper we present a constrained multi-objective optimization algorithm based on adaptive ε-truncation (ε-T-CMOA) to further improve distribution and convergence of the obtained solutions. First of all, as a novel constraint handling technique, ε-truncation technique keeps an effective balance between feasible solutions and infeasible solutions by permitting some excellent infeasible solutions with good objective value and low constraint violation to take part in the evolution, so diversity is improved, and convergence is also coordinated. Next, an exponential variation is introduced after differential mutation and crossover to boost the local exploitation ability. At last, the improved crowding density method only selects some Pareto solutions and near solutions to join in calculation, thus it can evaluate the distribution more accurately. The comparative results with other state-of-the-art algorithms show that ε-T-CMOA is more diverse than the other algorithms and it gains better in terms of convergence in some extent.
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| 키워드(Keyword) |
evolutionary computing
constrained multi-objective optimization
constraint handling
diversity maintenance
convergence
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| 파일첨부 |
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