In this paper, we propose a new scenario generation algorithm called Combinatorial Testing
Based on Complexity (CTBC) based on both combinatorial testing (CT) method and Test …

In particle swarm optimization (PSO), the inertia weight is an important parameter for
controlling its search capability. There have been intensive studies of the inertia weight in …

The testing of the intelligent driving systems is faced with the challenges of efficiency
because real traffic scenarios are infinite, uncontrollable and difficult to be precisely defined …

An adaptive gradient multiobjective particle swarm optimization (AGMOPSO) algorithm,
based on a multiobjective gradient (stocktickerMOG) method and a self-adaptive flight …

With the rapid development of e-commerce, logistics industry becomes a crucial component
in the e-commercial ecological chain. Impelled by both economical and environmental …

Recently, many meta-heuristic algorithms have been proposed to serve as the basis of a t-
way test generation strategy (where t indicates the interaction strength) including Genetic …

The sine-cosine algorithm (SCA) is a new population-based meta-heuristic algorithm. In
addition to exploiting sine and cosine functions to perform local and global searches (hence …

Particle swarm optimization (PSO) is one of the most popular population-based stochastic
algorithms for solving complex optimization problems. While PSO is simple and effective, it is …

In this paper, a quantum convolutional neural network (CNN) architecture is proposed to find
the optimal number of convolutional layers. Since quantum bits use probability to represent …

In this paper, a methodology of automatic generation of test scenarios for intelligent driving
systems is proposed, which is based on the combination of the test matrix (TM) and …