Quantum immune clone for solving constrained multi-objective optimization

This paper proposes a quantum immune clone algorithm to solve the constrained multi-objective optimization problem. Firstly, constraints deviation value is added to objective function value to form a new objective function value, which translates the constrained multi-objective optimization problem...

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Vydáno v:IEEE transactions on evolutionary computation s. 3049 - 3056
Hlavní autoři: Ronghua Shang, Licheng Jiao, Hao Xu, Yangyang Li
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.05.2015
Témata:
Algorithm design and analysis
Cloning
Linear programming
Optimization
Quantum computing
Sociology
Statistics
ISSN:1089-778X
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Shrnutí:This paper proposes a quantum immune clone algorithm to solve the constrained multi-objective optimization problem. Firstly, constraints deviation value is added to objective function value to form a new objective function value, which translates the constrained multi-objective optimization problem into an unconstrained multi-objective optimization problem. Secondly, it does not only retain the feasible non-dominated solutions, but also utilizes the non-feasible solutions which have small constraint deviation value and objective function value. The appearing of the non-feasible solutions expands the search scope and makes it easy to evolve solutions near the Pareto front. Then, a quantum rotating gate is designed to accelerate the computational speed. At last, crossover and mutation are used to obtain better individuals. Compared with the state-of-art algorithm, simulation results show that the proposed algorithm has a better improvement on GD distance and on the diversity.
ISSN:1089-778X
DOI:10.1109/CEC.2015.7257269