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Modified Hybrid Moth Optimization Algorithm for PFSS Problem

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Abstract

Generally, local search algorithms are better than population-based algorithms in exploiting the search space. Meanwhile, the population-based algorithms are better in exploring the search space. Lately, a Hybrid Moth Optimization Algorithm (HMOA) was proposed as a decent algorithm for the permutation flow shop scheduling problem. However, this algorithm has a limitation in determining the stopping condition and its operation may be early terminated, thus adversely impacting exploration and exploitation of the solution. Therefore, the objective of this paper was to propose a Modified Hybrid Moth Optimization Algorithm (MHMOA) in an effort to adaptively tune the stopping condition and, then, to evaluate performance of this algorithm. Taillard benchmark datasets for the flow shop scheduling problem were used in this study as an assessment domain. Taking this study goal into consideration, performance of MHMOA was compared with levels of performance of the HMOA and other related algorithms retrieved from the literature. The study results demonstrate that performance of the MHMOA compares with levels of performance of the other investigated algorithms and that it has reasonably good performance on many of the studied Taillard benchmark datasets.

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Data Availability

 The raw data (benchmarks) of the quantitative evaluations are available from http://mistic.heig-vd.ch/taillard/.

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Appendix

Appendix

See Tables A1, A2, and A3

Table A1 Comparison among MHMOA and HMOA algorithms
Table A2 Results analysis achieved by MHMOA algorithm using 20 runs
Table A3 Comparison among MHMOA and other approaches in the literature

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Abuhamdah, A. Modified Hybrid Moth Optimization Algorithm for PFSS Problem. SN COMPUT. SCI. 4, 298 (2023). https://doi.org/10.1007/s42979-023-01743-y

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