Cohort intelligence with self-adaptive penalty function approach hybridized with colliding bodies optimization algorithm for discrete and mixed variable constrained problems
نویسندگان
چکیده
Abstract Recently, several socio-/bio-inspired algorithms have been proposed for solving a variety of problems. Generally, they perform well when applied unconstrained problems; however, their performance degenerates constrained Several types penalty function approaches so far handling linear and non-linear constraints. Even though the approach is quite easy to understand, precise choice parameter very much important. It may further necessitate significant number preliminary trials. To overcome this limitation, new self-adaptive (SAPF) incorporated into socio-inspired Cohort Intelligence (CI) algorithm. This referred as CI–SAPF. Furthermore, CI–SAPF hybridized with Colliding Bodies Optimization (CBO) algorithm CI–SAPF–CBO The validated by discrete mixed variable problems from truss structure domain, design engineering nonlinear in nature. applicability techniques two real-world applications manufacturing domain. results obtained are promising computationally efficient compared other nature inspired optimization algorithms. A non-parametric Wilcoxon’s rank sum test performed on statistical solutions examine significance CI–SAPF–CBO. In addition, effect pseudo-objective function, violations analyzed discussed along advantages over
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ژورنال
عنوان ژورنال: Complex & Intelligent Systems
سال: 2021
ISSN: ['2198-6053', '2199-4536']
DOI: https://doi.org/10.1007/s40747-021-00283-3