An Improved Flower Pollination Algorithm for Global and Local Optimization

Meta-heuristic algorithms have emerged as a powerful optimization tool for handling non-smooth complex optimization problems and also to address engineering and medical issues. However, the traditional methods face difficulty in tackling the multimodal non-linear optimization problems within the vast search space. In this paper, the Flower Pollination Algorithm has been improved using Dynamic switch probability to enhance the balance between exploitation and exploration for increasing its search ability, and the swap operator is used to diversify the population, which will increase the exploitation in getting the optimum solution. The performance of the improved algorithm has investigated on benchmark mathematical functions, and the results have been compared with the Standard Flower pollination Algorithm (SFPA), Genetic Algorithm, Bat Algorithm, Simulated annealing, Firefly Algorithm and Modified flower pollination algorithm. The ranking of the algorithms proves that our proposed algorithm IFPDSO has outperformed the above-discussed nature-inspired heuristic algorithms.

Design and Automated Optimization of an Internal Turret Mooring System in the Frequency and Time Domain

A cost-effective mooring system design has been emphasized for traditional offshore industry applications and in the design of floating offshore wind turbines. The industry consensus regarding mooring system design is mainly inhibited by previous project experience. The design of the mooring system also requires a significant number of design cycles. To take aim at these challenges, this paper studies the application of an optimization algorithm to the Floating Production Storage and Offloading (FPSO) mooring system design with an internal turret system at deep-water locations. The goal is to minimize mooring system costs by satisfying constraints, and an objective function is defined as the minimum weight of the mooring system. Anchor loads, a floating body offset and mooring line tensions are defined as constraints. In the process of optimization, the mooring system is analyzed in terms of the frequency domain and time domain, and global and local optimization algorithms are also deployed towards reaching the optimum solution. Three cases are studied with the same initial conditions. The global and local optimization algorithms successfully find a feasible mooring system by reducing the mooring system cost by up to 52%.

Multi-objective aerodynamic optimization of high-speed train heads based on the PDE parametric modeling

With the increasing running speed, the aerodynamic issues of high-speed trains are being raised and impact the running stability and energy efficiency. The optimization design of the head shape is significantly important in improving the aerodynamic performance of high-speed trains. Existing aerodynamic optimization methods are limited by the parametric modeling methods of train heads which are unable to accurately and completely parameterize both global shapes and local details. Due to this reason, they cannot optimize both global and local shapes of train heads. In order to tackle this problem, we propose a novel multi-objective aerodynamic optimization method of high-speed train heads based on the partial differential equation (PDE) parametric modeling. With this method, the half of a train head is parameterized with 17 PDE surface patches which describe global shapes and local details and keep the surface smooth. We take the aerodynamic drag and lift as optimization objectives; divide the optimization design process into two stages: global optimization and local optimization; and develop global and local optimization methods, respectively. In the first stage, the non-dominated sorting genetic algorithm (NSGA-II) is adopted to obtain the framework of the train head with an optimized global shape. In the second stage, Latin hypercube sampling (LHS) is applied in the local shape optimization of the PDE surface patches determined by the optimized framework to improve local details. The effectiveness of our proposed method is demonstrated by better aerodynamic performance achieved from the optimization solutions in global and local optimization stages in comparison with the original high-speed train head.

APPLICATION OF GLOBAL OPTIMIZATION TO PREDICT STRAINS IN RC COMPRESSED COLUMNS

In this paper, the results of an application of global and local optimization methods to solve a problem of determination of strains in RC compressed structure members are presented. Solutions of appropriate sets of nonlinear equations in the presence of box constraints have to be found. The use of the least squares method leads to finding global solutions of optimization problems with box constraints. Numerical examples illustrate the effects of the loading value and the loading eccentricity on the strains in concrete and reinforcing steel in the a cross-section.Three different minimization methods were applied to compute them: trust region reflective, genetic algorithm tailored to problems with real double variables and particle swarm method. Numerical results on practical data are presented. In some cases, several solutions were found. Their existence has been detected by the local search with multistart, while the genetic and particle swarm methods failed to recognize their presence.

Characterization and optimization of hybrid carbon–glass epoxy composites under combined loading

This work is intended to characterize the mechanical behavior of hybrid carbon–glass composite plates under combined loading of bending and torsion, and to determine the optimal ply fiber orientations to minimize the maximum out-of-plane displacement under such loading conditions. Hybrid composite plates were manufactured with 10 plies each and different stacking sequences using hand lay-up, with carbon fiber and glass fiber reinforcements in an epoxy matrix. Two experimental setups (involving two distinct boundary conditions) are here considered to test the composite plates, both simulating combined loading of bending and torsion. Numerical simulations of the experimental tests were performed in ABAQUS® and validated with the experimental data. Using the ply fiber orientations as design variables, the hybrid composite plates were then optimized using global and local optimization using direct search (GLODS). The objective function of minimization of the maximum out-of-plane displacement is carried out through an interactive cycle between GLODS and ABAQUS®. Specimens of three optimized laminates were also manufactured for experimental validation. The optimization process contributed to improve the performance of the hybrid composite plates in more than 30% when compared to some non-optimized plates.