We propose a novel Evolutionary Algorithm (EA) based on the Differential Evolution algorithm for solving global numerical optimization problem in real-valued continuous parameter space. The proposed MadDE algorithm leverages the power of the multiple adaptation strategy with respect to the control parameters and search mechanisms, and is tested on the benchmark functions taken from the CEC 2021 special session & competition on single-objective bound-constrained optimization. Experimental results indicate that MadDE is able to achieve superior performance on global numerical optimization problems when compared against state-of-the-art real-parameter optimizers. We also provide a hyperparameter optimization algorithm SUBHO for improving the search performance of any EA by finding an optimal set of control parameters, and demonstrate its efficacy in enhancing MadDE’s performance on the same benchmark. The source code of our implementation is publicly available at https://github.com/subhodipbiswas/MadDE.

A new framework for optimization of the internal structure of transport aircraft wings having curvilinear spars and ribs (SpaRibs) is described. SpaRibs are parameterized by third-order B-splines with constraints on the starting, ending, and various control points. A broad range of SpaRibs topologies can be created using a limited number of parameters. For local size optimization, panels are generated from the wing finite element model by an algorithm that is independent of the nodal coordinates and the order in which the elements are distributed, and which can be used for a broad range of aircraft wings. The local panel thicknesses are optimized to satisfy buckling and stress constraints of the wing using an algorithm that increases the thickness of the panels that are found to be vulnerable to buckling or wherein the stress is higher than the maximum permissible stress. This algorithm has been integrated with the particle swarm algorithm to optimize the SpaRibs topology for multiple objectives (weight and flutter velocity). The code is demonstrated by optimizing the internal structure of the NASA Common Research Model wing model with SpaRibs considering stress and buckling constraints at trim condition at Mach number equal to 0.85 for two different angles of attack ($-2$ and 6 deg).

The paper describes a fully automated process to generate a shell-based finite element model of a large hybrid truck chassis to perform mass optimization considering multiple load cases and multiple constraints. A truck chassis consists of different parts that could be optimized using shape and size optimization. The cross members are represented by beams, and other components of the truck (batteries, engine, fuel tanks, etc.) are represented by appropriate point masses and are attached to the rail using multiple point constraints to create a mathematical model. Medium-fidelity finite element models are developed for front and rear suspensions and they are attached to the chassis using multiple point constraints, hence creating the finite element model of the complete truck. In the optimization problem, a set of five load conditions, each of which corresponds to a road event, is considered, and constraints are imposed on maximum allowable von Mises stress and the first vertical bending frequency. The structure is optimized by implementing the particle swarm optimization algorithm using parallel processing. A mass reduction of about 13.25% with respect to the baseline model is achieved.