Computationally expensive multi-objective optimization problems via optimization state-driven adaptive evolution
•An adaptive evolution framework to integrate association and optimized state.•Two metrics with different convergence characteristics to select individuals.•Two reference points to switch and guide evolution directions.•An optimized population update to mitigate parent population diversity loss.•Sup...
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| Vydáno v: | Information sciences Ročník 729; s. 122847 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Inc
01.03.2026
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| Témata: | |
| ISSN: | 0020-0255 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | •An adaptive evolution framework to integrate association and optimized state.•Two metrics with different convergence characteristics to select individuals.•Two reference points to switch and guide evolution directions.•An optimized population update to mitigate parent population diversity loss.•Superior performance obtained on benchmarks and real engineering applications.
The existing surrogate-assisted algorithms for computationally expensive multi-objective optimization problems (EMOPs) face three key challenges, i.e., the gradual loss of diversity of the population, the excessive randomness of local search, and the low adaptability to problems with complex PF shapes. This paper proposes an optimization state-driven adaptive evolution algorithm called OSAE to address EMOPs, where both the association and update states are employed to adjust the search directions adaptively. Specifically, two different types of evolution starting points are determined based on the association state. Thus, high-potential sub-populations, obtained by a two-step sub-population generation strategy, are employed as the starting populations of the designed RBF-based local search to accelerate the local exploitation for each sub-problem. Subsequently, the exact offspring can be obtained by a two-metric-driven selection, and both the exact and optimized populations are updated where the update state represented by inverted generational distance comparison is employed to determine whether to maintain or transform the reference point configuration. Therefore, OSAE achieves the adaptive adjustment of reference point configuration and adaptive search for each sub-problem, thus enhancing the adaptability to complex problems such as irregular PF shapes. Experimental studies on both classical test suites and real-world application verify the performance of OSAE. |
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| ISSN: | 0020-0255 |
| DOI: | 10.1016/j.ins.2025.122847 |