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Volume 299 - The 7th International Conference on Computer Engineering and Networks (CENet2017) - Session III -Information Theory
Constrained Multi-objective Optimization Algorithm Based on ε Adaptive Weighted Constraint Violation
J. Wang,* B. Li, Y. Cao
*corresponding author
Full text: pdf
Pre-published on: 2017 July 17
Published on: 2017 September 06
Abstract
A new constrained multi-objective optimization algorithm based on ε adaptive weighted constraint violation, AW-CMOA, is proposed to solve the constrained multi-objective optimization problems. Considering different levels of difficulty in the satisfaction of varied constraints, a new ε constraint handling strategy for multi-objective optimization, in which the constraint violation is redefined and the self-adapting ε level parameter is set, is designed to make the constraint violation for individual to reflect its true quality more objectively and accurately. Simultaneously, according to the features of the constrained multi-objective and the evolutionary mechanism of BBO, the model of constrained multi-objective optimization applicable to BBO is built. In the model, the habitat suitability index, in combination with the degree of feasible and the Pareto dominance relation between the individuals, is redefined. Moreover, the self-adaptive method of determining the migration rate is designed to improve the ability for exploitation and the utilization of better individual. Numerical experiments have shown that the proposed algorithm is competitive to other constrained multi-objective optimization algorithms in terms of convergence and distribution, and is capable of solving the complex constrained multi-objective optimization problems more effectively and efficiently.
DOI: https://doi.org/10.22323/1.299.0062
Open Access
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