Browsing by Subject "Metaheuristic optimization algorithm"

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    Solution of Process Synthesis Problem Using Metaheuristic Optimization Algorithms
    (Institute of Electrical and Electronics Engineers Inc., 2022) Altay E.V.
    Real-world optimization issues have been shown to be quite difficult to solve due to the fact that their objective functions are quite complicated and there are a significant number of constraints involved. Several metaheuristics and/or different techniques for addressing constraints have been proposed as potential solutions to these challenges. A freshly constructed method has to be benchmarked against some difficult issues that occur in the current world so that its efficacy and robustness may be verified. In the body of published work, a significant number of real-world test issues have been proposed. One of these problems is the process synthesis problem, which is one of the process synthesis and design problems. In this study, the process synthesis problem has been solved with the COOT optimizer, moth-flame optimization, and Harris hawks optimization, which have been popular and very successful recently. © 2022 IEEE.
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    A Comparative Study of Metaheuristic Optimization Algorithms for Solving Real-World Engineering Design Problems
    (Tech Science Press, 2023) Altay E.V.; Altay O.; Özçevik Y.
    Real-world engineering design problems with complex objective functions under some constraints are relatively difficult problems to solve. Such design problems are widely experienced in many engineering fields, such as industry, automotive, construction, machinery, and interdisciplinary research. However, there are established optimization techniques that have shown effectiveness in addressing these types of issues. This research paper gives a comparative study of the implementation of seventeen new metaheuristic methods in order to optimize twelve distinct engineering design issues. The algorithms used in the study are listed as: transient search optimization (TSO), equilibrium optimizer (EO), grey wolf optimizer (GWO), moth-flame optimization (MFO), whale optimization algorithm (WOA), slime mould algorithm (SMA), harris hawks optimization (HHO), chimp optimization algorithm (COA), coot optimization algorithm (COOT), multi-verse optimization (MVO), arithmetic optimization algorithm (AOA), aquila optimizer (AO), sine cosine algorithm (SCA), smell agent optimization (SAO), and seagull optimization algorithm (SOA), pelican optimization algorithm (POA), and coati optimization algorithm (CA). As far as we know, there is no comparative analysis of recent and popular methods against the concrete conditions of real-world engineering problems. Hence, a remarkable research guideline is presented in the study for researchers working in the fields of engineering and artificial intelligence, especially when applying the optimization methods that have emerged recently. Future research can rely on this work for a literature search on comparisons of metaheuristic optimization methods in real-world problems under similar conditions. © 2023 Tech Science Press. All rights reserved.