Vertical transportation systems embedded on shuffled frog leaping algorithm for manufacturing optimisation problems in industries

A whale optimization algorithm based on atom-like structure differential evolution for solving engineering design problems

The whale optimization algorithm has received much attention since its introduction due to its outstanding performance. However, like other algorithms, the whale optimization algorithm still suffers from some classical problems. To address the issues of slow convergence, low optimization precision, and susceptibility to local convergence in the whale optimization algorithm (WOA). Defining the optimization behavior of whale individuals as quantum mechanical behavior, a whale optimization algorithm based on atom-like structure differential evolution (WOAAD) is proposed. Enhancing the spiral update mechanism by introducing a sine strategy guided by the electron orbital center. Improving the random-walk foraging mechanism by applying mutation operations to both the electron orbital center and random individuals. Performing crossover operations between the newly generated individuals from the improved mechanisms and random dimensions, followed by a selection process to retain superior individuals. This accelerates algorithm convergence, enhances optimization precision, and prevents the algorithm from falling into local convergence. Finally, implementing a scouting bee strategy, where whale individuals progressively increase the number of optimization failures within a limited parameter L. When a threshold is reached, random initialization is carried out to enhance population diversity. Conducting simulation experiments to compare the improved algorithm with the whale optimization algorithm, other optimization algorithms, and other enhanced whale optimization algorithms. The experimental results indicate that the improved algorithm significantly accelerates convergence, enhances optimization precision, and prevents the algorithm from falling into local convergence. Applying the improved algorithm to five engineering design problems, the experimental results demonstrate that the improved algorithm exhibits good applicability.

Adaptive elevator kinematics optimization based dual response algorithm for determining proper levels in plaster milling process parameters

This study proposes a novel hybrid approach, called Adaptive/Elevator Kinematics Optimization algorithm based on dual response algorithm (A/EKO-DRA), to enhance the robust parameters estimation and design of the plaster milling process. The A/EKO-DRA method reduces variability while maintaining the desired output target, thereby minimizing the impact of variance on the expected stucco combined water. The performance of the A/EKO-DRA is compared with conventional processes through numerical examples and simulations. The results show that the A/EKO-DRA method has the lowest mean absolute errors among other methods in terms of parameter estimation, and it achieves the response mean of 5.927 percent, which meets the target value of 5.9 percent for industrial enclosures, with much reduction in the response variance. Overall, the A/EKO-DRA method is a promising approach for optimizing the plaster milling process parameters.

Location management for the supply of PD fluid via large neighborhood search based virus optimization algorithm

The facility location problem is extended by a new two-stage zero-one programming system (2S-ZOPS). It is a type of design optimization issue that exists in logistics implementations such as supply chain planning in healthcare or agriculture. Along with concerns regarding PD delivery time manner for connecting logistics centers and customers, recent studies have considered the zero-one location design model. This research discussed a route selection model for the 2S-ZOPS that did not exist in the published studies by taking into account the level of risk associated with physical appearance. The mathematical models were developed in response to a PD supply chain design that occurred in Thailand's National Health Insurance Program. By combining the virus optimization algorithm (VOA) with a large neighborhood search (LNS), we created a hybrid metaheuristic method for solving the 2S-ZOPS. Experiments with real-world data demonstrated that the hybrid algorithm was efficient in terms of time consumption and solution quality, saving approximately 6% on total costs. The presented practice benefits not only the healthcare industry but also various other businesses.