A dual distance dominance based evolutionary algorithm with selection-replacement operator for many-objective optimization

Zhang, Wei; Liu, Jianchang; Liu, Junhua; Liu, Yuanchao; Tan, Shubin

doi:10.1016/j.eswa.2023.121244

Cited by 8 publications

(1 citation statement)

References 92 publications

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“…Their prevalence in real-world scenarios necessitates continuous optimization efforts to enhance efficiency. In recent years, evolutionary algorithms have garnered significant interest and are progressively emerging as vital tools for addressing real-world challenges in the era of burgeoning big data [ 1 , 2 ]. Optimizing reservoir yield stands as a critical imperative within real-world industrial production.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Tree-Seed Algorithm for Function Optimization and Production Optimization

Zhou,

Dai,

Zhou

et al. 2024

Biomimetics

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As the fields of engineering, energy, and geology become increasingly complex, decision makers face escalating challenges that require skilled solutions to meet practical production needs. Evolutionary algorithms, inspired by biological evolution, have emerged as powerful methods for tackling intricate optimization problems without relying on gradient data. Among these, the tree-seed algorithm (TSA) distinguishes itself due to its unique mechanism and efficient searching capabilities. However, an imbalance between its exploitation and exploration phases can lead it to be stuck in local optima, impeding the discovery of globally optimal solutions. This study introduces an improved TSA that incorporates water-cycling and quantum rotation-gate mechanisms. These enhancements assist the algorithm in escaping local peaks and achieving a more harmonious balance between its exploitation and exploration phases. Comparative experimental evaluations, using the CEC 2017 benchmarks and a well-known metaheuristic algorithm, demonstrate the upgraded algorithm’s faster convergence rate and enhanced ability to locate global optima. Additionally, its application in optimizing reservoir production models underscores its superior performance compared to competing methods, further validating its real-world optimization capabilities.

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Section: Introductionmentioning

confidence: 99%