A Two-Phase Hybrid Metaheuristic Framework for Engineering Optimization
DOI:
https://doi.org/10.4108/dtip.12972Keywords:
Population-Based Algorithm, Constrained and Unconstrained Optimization, Two-Phase Framework, C-Sine Algorithm, GWO:SineL Algorithm, Levy Flight StrategyAbstract
Population-based optimization metaheuristic algorithms generate a pool of candidate solutions in the “Initialization” phase and these approximate solutions are iteratively refined further in the subsequent “Improvement” phase(s) towards the optimal/near-optimal solution. Any population-based algorithm may have a single or multiple “Improvement” phase(s). This paper analyses the impact of having two improvement phases. Different updating expressions are considered in each phase of the algorithm. In one case, the “C-Sine” algorithm, two new untested expressions are used, and performance is analysed. In the other case, the better performing Grey Wolf Optimizer (GWO) is applied in the first phase, and a new updating trigonometric expression is used in the second phase (termed as GWO:SineL algorithm) and analysis is carried out. The second phase applies the trigonometric "Sine" function over the random numbers generated using the Levy Flight Strategy. Mathematical functions, the CEC2019 dataset and a few real-world engineering problems are used for the analyses. Finally, the application of the “C-Sine” algorithm for solving multi-objective problems and the “GWO:SineL” algorithm for supply chain problems are studied. Codes are generated in MATLAB and run on an i5 PC with 4 GB RAM.
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Copyright (c) 2026 Baskar A, Justine Yasappan, M. Anthony Xavior, Anna Burduk, Suthep Butdee
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