What are some effective techniques for parallelizing multi objective optimization processes to reduce computational times?
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Effective techniques for parallelizing multi-objective optimization processes to reduce computational times include the use of parallel computing methods . These methods leverage parallel architectures to conduct either synchronous or asynchronous processing, allowing for faster evaluations and improved efficiency. Additionally, incorporating a sliding time window based on parallel computing can help maintain population diversity and reuse evolutionary information, leading to quicker convergence in time-varying environments. Furthermore, employing a Parallel Dynamic Multi-Objective Evolutionary Algorithm (PDMOEA) has shown to enhance results in less execution time compared to sequential counterparts, especially when dealing with complex problems. Overall, these techniques demonstrate significant improvements in computational efficiency for multi-objective optimization tasks.
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| Papers (4) | Insight |
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| The paper proposes Grey Wolf Optimization (GWO) and Oppositional-based Learning (OBL) integration to enhance convergence rates and solution accuracy, reducing computational time for multi-objective optimization processes. | |
3 Citations | Parallel Dynamic Multi-Objective Evolutionary Algorithm (PDMOEA) efficiently reduces computational times by utilizing parallel processing, improving objective space, minimizing time, and converging to optimal solutions. |
| The Multi-Objective Parallel Local Surrogate-assisted search (MOPLS) algorithm utilizes parallel computing, tabu mechanism, and asynchronous options to enhance efficiency in multi-objective optimization processes. | |
17 Jul 2021 | The paper suggests using a sliding time window based on parallel computing (STW-PC) to enhance multi-objective optimization by maintaining diversity and reducing computational time effectively. |
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