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The results show that the proposed MOPSO (MOPSO+EPD) significantly outperforms MOPSO on the majority of test functions in terms of both convergence and coverage.
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The results of MSSA show that this algorithm can approximate Pareto optimal solutions with high convergence and coverage.
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ALO is selected for solving the multi-objective problem due to high convergence and coverage in obtaining Pareto optimal solution [35].
It outperforms potential field algorithms regarding both convergence and optimality.
This is a case for both convergence and divergence.
These significant group effects occur for both convergence and divergence.
The latter are defined as motifs if both convergence and divergence obey some statistical conditions.
The first 10%% of total generations were discarded as burnin for both convergence and tree estimates.
We then summarize and discuss our results for both convergence of coverage and robustness to failures.
Experimental studies demonstrate that the proposed HMOGWO outperforms other algorithms in terms of convergence, spread and coverage.
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