Genetic Algorithm For Structural Optimization Research Articles

Enhanced optimization algorithm for the structural design of an air‐cooled battery pack considering battery lifespan and consistency

Electric scooters are increasingly popular for short-distance commuting. To improve the thermal safety, performance, and lifespan of their batteries, their heat needs to be managed. This study proposes a method for optimizing the air channels in a scooter battery pack. It includes an electro-thermal-degradation model for predicting the battery's electrical and thermal behaviors and capacity loss, a heat transfer model for predicting convective heat exchange between the battery and the air, and a genetic algorithm for structural optimization of an air-cooled battery thermal management system (BTMS). Unlike conventional optimization of a BTMS, the proposed algorithm aims to improve the electrical consistency, lifespan, and thermal safety of the battery via rapid global optimization of its air ducts. The optimization algorithm was tested on a 3P4S air-cooled battery pack from an electric scooter. It improved the pack's consistency of state of charge (SOC) and its lifespan by reducing its heat and temperature gradient. Under on-design conditions, the optimized air ducts reduced the maximum pack temperature by 0.45°C and the difference between the average temperatures of the cells in a branch to 15.9% that of the original pack. Moreover, the optimized air ducts decrease the SOC difference by 81.1% and improved the state of health by 0.03%. Hence, the proposed air duct optimization method can improve the pack's thermal performance, SOC distribution, and lifespan under off-design conditions.

An improved genetic algorithm for structural optimization of Au–Ag bimetallic nanoparticles

The structures are crucial for bimetallic nanoparticles (NPs) because they can determine their unique physical and chemical properties. Therefore, structures optimization of bimetallic NPs from theoretical calculation is of increasing importance for understanding their stabilities and catalytic performance. In this article, an improved genetic algorithm (IGA) is proposed to systematically investigate the structural stabilities of Au–Ag NPs. In the IGA, a layered coordinate ranking method is adopted to enhance the structural stability during initialization. Meanwhile, a difference transition fitness function is introduced to keep the population diversity and preserve the best individual of IGA. Furthermore, for improving the global searching ability and local optimization speed, a sphere-cut-splice crossover is employed to replace the classical plane-cut-splice crossover in general genetic algorithm. The performance of IGA has been compared with Monte Carlo simulation method and particle swarm optimization algorithm, the results reveal our algorithm possesses superior convergence and stability.

Application of Improved Combined Shape Genetic Algorithm on Structural Optimization

A new approach to the generation of an initial point is proposed for discrete combined shape, which improves fully the local searching capability of discrete combined shape algorithm. Combined shape algorithm is embedded into genetic algorithm as a combined shape operator. Consequently a hybrid genetic algorithm for structural optimization with discrete variables is proposed. The constrained optimization problems were dealt with by adaptive annealing penalty factors and penalty function. The numerical results show that improved combined shape genetic algorithm for structural optimization with discrete variable problems has a faster convergence speed, which has advantages of local searching capability and globally searching capability of genetic algorithm. Improved combined shape genetic algorithm is an efficient optimal design method for engineering structure.

Structural optimization of power distribution networks under uncertainty

The results of the development of the operators of genetic algorithm for structural optimization of electricity distribution networks with unclearly defined goals. The algorithm contains both standard operators and integrated nonlinear programming procedures, as well as the original ranking operator

Distributed Hybrid Genetic Algorithms for Structural Optimization on a PC Cluster

Even though several genetic algorithm (GA)-based optimization algorithms have been successfully applied to complex optimization problems in various engineering fields, such methods are computationally too expensive for practical use in the field of structural optimization, particularly for large-scale problems. Furthermore, the successful implementation of GA-based optimization algorithm requires a cumbersome routine through trial-and-error for tuning the GA parameters that are different depending on each problem. Therefore, to overcome these difficulties, a high-performance GA is developed in the form of a distributed hybrid genetic algorithm for structural optimization, implemented on a cluster of personal computers. The distributed hybrid genetic algorithm proposed in this paper consists of a μ-GA running on a master computer and multiple simple GAs running on slave computers. The algorithm is implemented on a PC cluster and applied to the minimum weight design of steel structures. The results show that the computation time required for GA-based optimization can be drastically reduced and the problem-dependent parameter tuning process can be avoided.

A robust genetic algorithm for structural optimization

The focus of this paper is on the development and implementation of a methodology for automated design of discrete structural systems. The research is aimed at utilizing Genetic Algorithms (GA) as an automated design tool. Several key enhancements are made to the simple GA in order to increase the efficiency, reliability and accuracy of the methodology for code-based design of structures. The AISC-ASD design code is used to illustrate the design methodology. Small as well as large-scale problems are solved. Simultaneous sizing, shape and topology optimal designs of structural framed systems subjected to static and dynamic loads are considered. Comparisons with results from prior publications and solution to new problems show that the enhancements made to the GA do indeed make the design system more efficient and robust.

Distributed Genetic Algorithm for Structural Optimization

Parallel algorithms for optimization of structures reported in the literature have been restricted to shared-memory multiprocessors. This paper presents a distributed genetic algorithm for optimization of large structures on a cluster of workstations connected via a local area network (LAN). The selection of genetic algorithm is based on its adaptability to a high degree of parallelism. Two different approaches are used to transform the constrained structural optimization problem to an unconstrained optimization problem: a penalty-function method and augmented Lagrangian approach. For the solution of the resulting simultaneous linear equations the iterative preconditioned conjugate gradient (PCG) method is used because of its low memory requirement. A dynamic load-balancing mechanism is developed to account for the unpredictable multiuser, multasking environment of a networked cluster of workstations, heterogeneity of machines, and indeterminate nature of the interative PCG equation solver. The algorithm has been applied to optimization of a large space steel structure subjected to vertical and horizontal loads and the constraints of the AISC ASD specifications.

Augmented Lagrangian Genetic Algorithm for Structural Optimization

This paper presents a robust hybrid genetic algorithm for optimization of space structures using the augmented Lagrangian method. An attractive characteristic of genetic algorithm is that there is no line search and the problem of computation of derivatives of the objective function and constraints is avoided. This feature of genetic algorithms is maintained in the hybrid genetic algorithm presented in this paper. Compared with the penalty function‐based genetic algorithm, only a few additional simple function evaluations are needed in the new algorithm. Furthermore, the trial and error approach for the starting penalty function coefficient and the process of arbitrary adjustments are avoided. There is no need to perform extensive numerical experiments to find a suitable value for the penalty function coefficient for each type or class of optimization problem. The algorithm is general and can be applied to a broad class of optimization problems.