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The efficiency of this GA is first demonstrated in solving various mathematical test-cases and then applied to the industrial problem.
The obtained results are compared with existing popular and competent techniques in literature and its competence as a tool in the optimization problem are demonstrated in solving some benchmark examples, the proposed approach provided lighter truss structures than the available solutions reported in the literature.
Furthermore, the flexibility and robustness of the method, as demonstrated in solving complex three-dimensional problems at organ scale, are quite appealing and promising and will be further explored in combination with the development of advanced approaches to maximise code performance.
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These could include demonstrated success in solving or engaging in a significant problem, perhaps reflected in assessments by policy-makers and managers.
However, a certain portion of students who experienced states of boredom and anxiety did not demonstrate in-depth problem solving strategies.
The CRO method has demonstrated its capability in solving NP-hard optimization problems.
CRO has demonstrated excellent performance in solving many engineering problems such as the quadratic assignment problem, neural network training, multimodal continuous problems, etc.
Many of these techniques including neural networks, genetic algorithms, and fuzzy logic have demonstrated their utility in solving problems independent of other methods.
Due to its ability to escape from local optima, CRO has demonstrated excellent performance in solving a variety of scheduling problems, such as flexible job-shop scheduling, grid scheduling, network scheduling etc.
PSO and various variant PSO or hybrid PSO have demonstrated their performance in solving these scheduling problems.
The moving kriging (MK) interpolation was first proposed by Gu [27] and successfully demonstrated the effectiveness in solving steady-state heat conduction problems.
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