Threshold Based Penalty Functions for Constrained Multiobjective Optimization
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2016, Vol 7, Issue 2
Abstract
This paper compares the performance of our re-cently proposed threshold based penalty function against its dynamic and adaptive variants. These penalty functions are incorporated in the update and replacement scheme of the multiobjective evolutionary algorithm based on decomposition (MOEA/D) framework to solve constrained multiobjective op-timization problems (CMOPs). As a result, the capability of MOEA/D is extended to handle constraints, and a new algorithm, denoted by CMOEA/D-DE-TDA is proposed. The performance of CMOEA/D-DE-TDA is tested, in terms of the values of IGD-metric and SC-metric, on the well known CF-series test instances. The experimental results are also compared with the three best performers of CEC 2009 MOEA competition. Empirical results show the pitfalls of the proposed penalty functions.
Authors and Affiliations
Muhammad Jan, Nasser Tairan, Rashida Khanum, Wali Mashwani
Keywords
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- EP ID EP164407
- DOI 10.14569/IJACSA.2016.070282
- Views 64
- Downloads 0
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