Global optimization methods for engineering applications: A review

Inverse design of bistable composite laminates with switching tunneling method for global optimization

Bistability enables adaptive designs with tunable deflections for applications including morphing wings, robotic grippers, and consumer products. Composite laminates may be designed to exhibit bistability due to pre-strains that develop during the processing of the polymer matrix, enabling fast reconfiguration between two stable shapes. Unfortunately, designing bistable laminates is challenging because of their highly nonlinear behavior. Here, we propose the Switching Tunneling Method to address this challenge by alternating between gradient-based local minimization and tunneling search phases, with the enhancement of objective expression switching to improve numerical conditioning. Results demonstrate high effectiveness compared to existing optimizers; the Switching Tunneling Method achieves a 99% success rate in finding all energy minima across general composite layups. Additionally, our method facilitates the inverse design of variable pre-strain fields, enabling bioinspired, positive Gaussian curvatures, which are not possible with conventional pre-strain laminates. Validations through both finite element analysis and 3D printed samples confirm the optimal designs.

Chaotic Search-Based Salp Swarm Algorithm for Dealing with System of Nonlinear Equations and Power System Applications

The system of nonlinear equations (SNLEs) is one of the eminent problems in science and engineering, and it is still open to research. A new hybrid intelligent algorithm is presented in this research to solve SNLEs. It is a composite of the salp swarm algorithm (SSA) and chaotic search technique (CST). The proposed methodology is named chaotic salp swarm algorithm (CSSA). CSSA is designed as an optimization process, whereby feasible and infeasible solutions are updated to move closer to the optimum value. The use of this hybrid intelligent methodology aims to improve performance, increase solution versatility, avoid the local optima trap, speed up convergence and optimize the search process. Firstly, SNLEs are transformed into an optimization problem. Secondly, CSSA is used to solve this optimization problem: SSA is used to update the feasible solutions, whereas the infeasible solutions are updated by CST. One of the most significant advantages of the suggested technique is that it does not ignore infeasible solutions that are updated, because these solutions are often extremely near to the optimal solution, resulting in increased search effectiveness and effective exploration and exploitation. The algorithm’s mathematical model is presented in detail. Finally, the proposed approach is assessed with several benchmark problems and real-world applications. Simulation results show that the proposed CSSA is competitive and better in comparison to others, which illustrates the effectiveness of the proposed algorithm. In addition, a statistical analysis by the Wilcoxon rankings test between CSSA and the other comparison methods shows that all p-values are less than 0.05, and CSSA achieves negative ranks’ sum values (R−) much better than the positive ranks’ sum values (R+) in all benchmark problems. In addition, the results have high precision and show good agreement in comparison with similar methods, and they further proved the ability of CSSA to solve real-world applications.

Artificial Bee Colony Algorithm for Fresh Food Distribution without Quality Loss by Delivery Route Optimization

This paper enlightens the use of artificial intelligence (AI) for distribution of fresh foods by searching more viable route to keep intact the food attributes. In recent years, very hard-hitting competition is for food industries because of the individuals living standards and their responsiveness for fresh food products demand within stipulated time period. Food industry deals with the extensive kind of activities such as food processing, food packaging and distribution, and instrumentation and control. To meet market demand, customer satisfaction, and maintaining its own brand and ranking on global scale, artificial intelligence can play a vibrant role in decision-making by providing analytical solutions with adjusting available resources. Therefore, by integrating innovative technologies for fresh food distribution, potential benefits have been increased, and simultaneously risk associated with the food quality is reduced. Time is a major factor upon which food quality depends; hence, time required to complete the task must be minimized, and it is achieved by reducing the distance travelled; so, path optimization is the key for the overall task. Swarm intelligence (SI) is a subfield of artificial intelligence and consists of many algorithms. SI is a branch of nature-inspired algorithm, having a capability of global search, and gives optimized solution for real-time problems adaptive in nature. An artificial bee colony (ABC) optimization and cuckoo search (CS) algorithm also come into the category of SI algorithm. Researchers have implemented ABC algorithm and CS algorithm to optimize the distribution route for fresh food delivery in time window along with considering other factors: fixed number of delivery vehicles and fixed cost and fuel by covering all service locations. Results show that this research provides an efficient approach, i.e., artificial bee colony algorithm for fresh food distribution in time window without penalty and food quality loss.

Computational Biomechanics: In-Silico Tools for the Investigation of Surgical Procedures and Devices

Biomechanical investigations of surgical procedures and devices are usually developed by means of human or animal models. The exploitation of computational methods and tools can reduce, refine, and replace (3R) the animal experimentations for scientific purposes and for pre-clinical research. The computational model of a biological structure characterizes both its geometrical conformation and the mechanical behavior of its building tissues. Model development requires coupled experimental and computational activities. Medical images and anthropometric information provide the geometrical definition of the computational model. Histological investigations and mechanical tests on tissue samples allow for characterizing biological tissues' mechanical response by means of constitutive models. The assessment of computational model reliability requires comparing model results and data from further experimentations. Computational methods allow for the in-silico analysis of surgical procedures and devices' functionality considering many different influencing variables, the experimental investigation of which should be extremely expensive and time consuming. Furthermore, computational methods provide information that experimental methods barely supply, as the strain and the stress fields that regulate important mechano-biological phenomena. In this work, general notes about the development of biomechanical tools are proposed, together with specific applications to different fields, as dental implantology and bariatric surgery.

Chaotic Teaching-Learning-Based Optimization with Lévy Flight for Global Numerical Optimization

Recently, teaching-learning-based optimization (TLBO), as one of the emerging nature-inspired heuristic algorithms, has attracted increasing attention. In order to enhance its convergence rate and prevent it from getting stuck in local optima, a novel metaheuristic has been developed in this paper, where particular characteristics of the chaos mechanism and Lévy flight are introduced to the basic framework of TLBO. The new algorithm is tested on several large-scale nonlinear benchmark functions with different characteristics and compared with other methods. Experimental results show that the proposed algorithm outperforms other algorithms and achieves a satisfactory improvement over TLBO.

Virtual evolution for visual search in natural images results in behavioral receptive fields with inhibitory surrounds

The neural mechanisms driving perception and saccades during search use information about the target but are also based on an inhibitory surround not present in the target luminance profile (e.g., Eckstein et al., 2007). Here, we ask whether these inhibitory surrounds might reflect a strategy that the brain has adapted to optimize the search for targets in natural scenes. To test this hypothesis, we sought to estimate the best linear template (behavioral receptive field), built from linear combinations of Gabor channels representing V1 simple cells in search for an additive Gaussian target embedded in natural images. Statistically nonstationary and non-Gaussian properties of natural scenes preclude calculation of the best linear template from analytic expressions and require an iterative optimization method such as a virtual evolution via a genetic algorithm. Evolved linear receptive fields built from linear combinations of Gabor functions include substantial inhibitory surround, larger than those found in humans performing target search in white noise. The inhibitory surrounds were robust to changes in the contrast of the signal, generalized to a larger calibrated natural image data set, and tasks in which the signal occluded other objects in the image. We show that channel nonlinearities can have strong effects on the observed linear behavioral receptive field but preserve the inhibitory surrounds. Together, the results suggest that the apparent suboptimality of inhibitory surrounds in human behavioral receptive fields when searching for a target in white noise might reflect a strategy to optimize detection of signals in natural scenes. Finally, we contend that optimized linear detection of spatially compact signals in natural images might be a new possible hypothesis, distinct from decorrelation of visual input and sparse representations (e.g., Graham et al., 2006), to explain the evolution of center-surround organization of receptive fields in early vision.

Evolutionary design of modular robotic arms

This paper proposes a method for task based design of modular serial robotic arms using evolutionary algorithms (EA). We introduce a 3D kinematics and a global optimization for both topology and configuration from task specifications. The search features revolute as well as prismatic joints and any number of DOF to build up a solution without using any design knowledge. A study of the evolution dynamics gives some keys to set evolution parameters that enable artificial evolution. An adapted algorithm dealing with the topology/configuration search tradeoff is proposed, descibed, and discussed. Illustrations of the algorithms results are given and conclusions are drawn from their analysis. Perspectives of this work are given, extending its reach to control and complex system design.