Optimizing the NACA0015 airfoil which is widely applied in small-scale vertical axis wind turbine to make it has a better aerodynamic performance. In the optimization process, using CST parameterization method to perturb the airfoil geometry, the thickness and camber of the airfoil are selected as the constraint, and the value of the maximum tangential force coefficient is chosen as the objecti...

This paper presents an evolutionary multi-objective simulation-optimization system for personnel scheduling. The system is developed for the Swedish postal services and aims at finding personnel schedules that minimizes both total man hours and the administrative burden of the person responsible for handling schedules. For the optimization, the multi-objective evolutionary algorithm NSGA-II is ...

This paper details two problems that employ Genetic-based Multi-objective Optimization algorithms on dynamic systems. The first is a simple, single input system and the second is a complex two input system. First, the two systems are detailed with the derivation of their models. From these models, an optimization algorithm based on NSGA-II is run, constructing the Pareto Frontiers. The results ...

The paper deals with efficiency comparison of two global evolutionary optimization methods implemented in MATLAB. Attention is turned to an elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and a novel multi-objective Particle Swarm Optimization (PSO). The performance of optimizers is compared on three different test functions and on a cavity resonator synthesis. The microwave resonator...

Lens system design provides ideal problems for evolutionary algorithms: a complex nonlinear optimization task, often with intricate physical constraints, for which there is no analytical solutions. This paper demonstrates, through the use of two evolution strategies, namely non-isotropic SA-ES and CMA-ES, as well as multiobjective NSGA-II optimization, the human competitiveness of an approach w...

This paper presents NSGA-PINN, a multi-objective optimization framework for the effective training of physics-informed neural networks (PINNs). The proposed uses non-dominated sorting genetic algorithm (NSGA-II) to enable traditional stochastic gradient algorithms (e.g., ADAM) escape local minima effectively. Additionally, NSGA-II enables satisfying initial and boundary conditions encoded into ...

This paper presents a multi-objective order allocation planning problem with the consideration of various real-world production features. A novel hybrid intelligent optimization model, integrating a multi-objective memetic optimization process, a Monte Carlo simulation technique and a heuristic pruning technique, is proposed to handle this problem. Experiments based on industrial data are condu...

Handling uncertainties for parameters in nonlinear constraints using chance constrained programming (CCP) is not as straight forward as its linear counterparts. A simulation based CCP approach which can be thought as an alternative to handle such a situation, has been adopted in this work while solving a multi-objective optimization problem of an industrial grinding operation under various para...

Flexible job-shop scheduling problem (FJSP) is an NP-hard puzzle which inherits the job-shop scheduling problem (JSP) characteristics. This paper presents a bee evolutionary guiding nondominated sorting genetic algorithm II (BEG-NSGA-II) for multiobjective FJSP (MO-FJSP) with the objectives to minimize the maximal completion time, the workload of the most loaded machine, and the total workload ...