A Multi-Objective Carnivorous Plant Algorithm for Solving Constrained Multi-Objective Optimization Problems

Satisfying various constraints and multiple objectives simultaneously is a significant challenge in solving constrained multi-objective optimization problems. To address this issue, a new approach is proposed in this paper that combines multi-population and multi-stage methods with a Carnivorous Pla...

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Veröffentlicht in:Biomimetics (Basel, Switzerland) Jg. 8; H. 2; S. 136
Hauptverfasser: Yang, Yufei, Zhang, Changsheng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 26.03.2023
MDPI
Schlagworte:
Algorithms
carnivorous plant algorithm
constrained multi-objective optimization
Convergence
Cross-pollination
Feasibility
Genetic algorithms
Optimization
Plant reproduction
quadratic interpolation
quasi-reflection learning
R&D
Research & development
Traveling salesman problem
Violations
United States
cross-pollination
quadratic interpolation
constrained multi-objective optimization
carnivorous plant algorithm
quasi-reflection learning
ISSN:2313-7673, 2313-7673
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Zusammenfassung:Satisfying various constraints and multiple objectives simultaneously is a significant challenge in solving constrained multi-objective optimization problems. To address this issue, a new approach is proposed in this paper that combines multi-population and multi-stage methods with a Carnivorous Plant Algorithm. The algorithm employs the ϵ-constraint handling method, with the ϵ value adjusted according to different stages to meet the algorithm’s requirements. To improve the search efficiency, a cross-pollination is designed based on the trapping mechanism and pollination behavior of carnivorous plants, thus balancing the exploration and exploitation abilities and accelerating the convergence speed. Moreover, a quasi-reflection learning mechanism is introduced for the growth process of carnivorous plants, enhancing the optimization efficiency and improving its global convergence ability. Furthermore, the quadratic interpolation method is introduced for the reproduction process of carnivorous plants, which enables the algorithm to escape from local optima and enhances the optimization precision and convergence speed. The proposed algorithm’s performance is evaluated on several test suites, including DC-DTLZ, FCP, DASCMOP, ZDT, DTLZ, and RWMOPs. The experimental results indicate competitive performance of the proposed algorithm over the state-of-the-art constrained multi-objective optimization algorithms.
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These authors contributed equally to this work.
ISSN:2313-7673
2313-7673
DOI:10.3390/biomimetics8020136