A Multiobjective Gaussian Particle Swarm Approach Applied to Electromagnetic Optimization

The development of optimization techniques for multiobjective problems in electromagnetics has been flourishing in the last decade. This paper proposes an improved multiobjective particle swarm optimization approach and applies it to the multiobjective version of TEAM workshop problem 22. Simulation results show that this improved version of the algorithm finds a better Pareto-optimal front with respect to more classical PSO methods while maintaining a better spread of nondominated solutions along the front. Furthermore, the proposed algorithm is compared with the widely used Nondominated Sorting Genetic Algorithm-II (NSGA-II) method highlighting a strongly different behaviour of these strategies

Prediction of permanent deformation in asphalt pavements using a novel symbiotic organisms search–least squares support vector regression

The prediction of asphalt performance can be very important in terms of increasing service life and performance while saving energy and money. In this study, a new hybrid artificial intelligence (AI) system, SOS-LSSVR, has been proposed to predict the permanent deformation potential of asphalt pavement mixtures. SOS-LSSVR utilizes the symbiotic organisms search (SOS) and the least squares support vector regression (LSSVR), which are seen as a complementary system. The prediction model can be established from all input and output data pairs for LSSVR, while SOS optimizes the systems tuning parameters. To avoid sampling bias and to partition the dataset into testing and training, a cross-validation technique was chosen. The results can be compared to those of previous studies and other predictive methods. Through the use of four error indicators, SOS-LSSVR accuracy was verified in predicting the permanent deformation behavior of an asphalt mixture. The present study demonstrates that the proposed AI system is a valuable decision-making tool for road designers. Additionally, the success of SOS-LSSVR in building an accurate prediction model suggests that the proposed self-optimized prediction framework has found an underlying pattern in the current database and thus can potentially be implemented in various disciplines