Constrained Multi-objective Optimization Algorithm with Adaptive Truncation Strategy

BI Xiaojun; ZHANG Lei

doi:10.11999/JEIT151237

Volume 38 Issue 8

Sep. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(8): 2047-2053

BI Xiaojun, ZHANG Lei. Constrained Multi-objective Optimization Algorithm with Adaptive Truncation Strategy[J]. Journal of Electronics & Information Technology, 2016, 38(8): 2047-2053. doi: 10.11999/JEIT151237

Citation:

BI Xiaojun, ZHANG Lei. Constrained Multi-objective Optimization Algorithm with Adaptive Truncation Strategy[J]. Journal of Electronics & Information Technology, 2016, 38(8): 2047-2053. doi: 10.11999/JEIT151237

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Constrained Multi-objective Optimization Algorithm with Adaptive Truncation Strategy

doi: 10.11999/JEIT151237

BI Xiaojun¹,
ZHANG Lei¹

Funds:

The National Natural Science Foundation of China (61175126)

Received Date: 2015-11-05
Rev Recd Date: 2016-03-17
Publish Date: 2016-08-19

Abstract

Abstract

To improve distribution and convergence of the obtained solution set in constrained multi-objective optimization problems, this paper presents a constrained multi-objective optimization algorithm based on adaptive truncation strategy. Firstly, through the proposed truncation strategy, the Pareto optimal solutions and the infeasible solutions with low constraint violation and good objective function values are retained to improve diversity. Besides, both diversity and convergence are coordinated. Secondly, the exponential variation is added for further enhancing the local exploitation ability after mutation and crossover operation. Finally, the improved crowding density estimation chooses a part of the Pareto optimal individuals and the near individuals to take part in the calculation, thus it not only assesses the distribution of the solution set more accurately, but also reduces the computational quantity. The comparative experiment results with another four excellent constrained multi- objective algorithms on the standard constrained multi-objective optimization problems (CTP series) show that diversity and convergence of the proposed algorithm are improved, and it has certain advantages compared with these algorithms.
- Constrained multi-objective optimization,
- Constraint handling technique,
- truncation,
- Distribution,
- Convergence

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References(18)

References

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