C-PSA: Constrained Pareto simulated annealing for constrained multi-objective optimization

“…Jue Wang multi-objective optimization algorithms which are the most representative and best optimized such as C-PSA [2], MOABC [3] and CMOPSO [4] on the accepted test functions TNK, SRN, CONSTR and OSY [4]. The initial population size of AW-CMOA and the other 3 algorithms are set as 100.…”

Section: Constrained Multi-objective Optimization Algorithm Based On mentioning

confidence: 99%

Constrained Multi-objective Optimization Algorithm Based on ε Adaptive Weighted Constraint Violation

Wang¹,

Cao²

2017

Proceedings of the 7th International Conference on Computer Engineering and Networks — PoS(CENet2017)

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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.

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“…The maximum size to which the archive may be filled before clustering is used to reduce its size to HL For more details about AMOSA, one can refer to Bandyopadhyay et al (2008) and Singh et al (2010).…”

Section: Slmentioning

confidence: 99%

A knowledge-based archive multi-objective simulated annealing algorithm to optimize series–parallel system with choice of redundancy strategies

Zaretalab

Hajipour

Sharifi

et al. 2015

Computers & Industrial Engineering

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C-PSA: Constrained Pareto simulated annealing for constrained multi-objective optimization

Cited by 62 publications

References 41 publications

Increasing selective pressure towards the best compromise in evolutionary multiobjective optimization: The extended NOSGA method

Increasing selective pressure towards the best compromise in evolutionary multiobjective optimization: The extended NOSGA method

Constrained Multi-objective Optimization Algorithm Based on ε Adaptive Weighted Constraint Violation

A knowledge-based archive multi-objective simulated annealing algorithm to optimize series–parallel system with choice of redundancy strategies

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