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首页> 外文期刊>Computer Methods in Applied Mechanics and Engineering >Young's double-slit experiment optimizer : A novel metaheuristic optimization algorithm for global and constraint optimization problems
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Young's double-slit experiment optimizer : A novel metaheuristic optimization algorithm for global and constraint optimization problems

机译：Young's double-slit experiment optimizer : A novel metaheuristic optimization algorithm for global and constraint optimization problems

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Due to the global progress, the optimization problems are becoming more and more complex. Hence, deterministic and heuristic approaches are no longer adequate for dealing with such sophisticated problems. subsequently, metaheuristics have recently emerged as an effective alternative for addressing the optimization problems. This paper proposes a novel metaheuristic called Young's Double-Slit Experiment (YDSE) optimizer, derived from a physical backdrop. The YDSE optimizer is inspired by Young's double-slit experiment, which is regarded as one of the most well-known classical physics experiments, revealing the wave nature of light. In YDSE optimizer, each fringe represents a possible solution in the population. Many concepts are modeled from the experiment, such as monochromatic light waves, Huygens' principle, constructive and destructive interference, wave intensity, amplitude, and path difference. The YDSE optimizer strikes a balance between exploration and exploitation by selecting either a constructive interference or a destructive interference based on the order number of the fringe. During the optimization process, the solution moves in search space based on its order number. If the solution has an odd number, it moves in the dark regions towards the central bright region, which is expected to contain the optimal solution. The algorithm exploits the promising areas in the bright fringe areas, which are assumed to contain the optimum. The performance of the YDSE optimizer is compared with another twelve metaheuristics using CEC 2014, CEC 2017, and CEC 2022. The benchmarks cover different unimodal, multimodal, hybrid, and composite test functions. Also, we consider ten constrained and unconstrained engineering optimization design problems. YDSE proved its superiority over the CEC 2014 and CEC 2017 winners, such as L -SHADE, LSHADE-cnEpSin, and LSHADE-SPACMA. The results and the statistical analysis demonstrated the outperformance of the proposed YDSE optimizer at a 95% confidence interval. Published by Elsevier B.V.

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