Swarm intelligence in mechanical engineering

Swarm intelligence (SI) is an artificial intelligence technique based on the study of cooperation behaviors of simple individuals (e.g. ant colonies, bird flocking, animal herding, and bees gathering honey) in various decentralized systems. The population, which consists of simple individuals, can usually solve complex tasks in nature by individuals interacting locally with one another and with their environment. Although a simple individual’s behavior is primarily characterized by autonomy, distributed functioning, and self-organizing capacities, local interactions among the individuals often lead to a global optimal. Therefore, SI is a promising way to develop powerful solution methods for complex optimization problems in mechanical engineering.

Recently, SI algorithms have attracted much attention of researchers and have also been applied successfully to solving optimization problems in mechanical engineering. However, for large and complex problems, SI algorithms often consume too much computation time due to the stochastic features of their searching approaches. Thus, there is a potential requirement to develop efficient algorithms that are able to find solutions under limited resources, time and money in real-world applications.

The aim of this Special Issue is to highlight the most significant recent developments on the topics of SI and to apply SI algorithms in real-life scenarios. Contributions containing new insights and findings in this field are welcome. Papers selected for this Special Issue present new findings and insights into this field. A broad range of topics are discussed, especially in the following areas: SI algorithms for scheduling of machinery production line, mechanical parameters adjustment based on SI, application of SI algorithms in mechanical fault diagnosis and SI and mechatronics.

In the paper titled “Pareto optimal train scheduling for urban rail transit using generalized particle swarm optimization,” W. Chu et al. established a bi-objective optimization model to study the Pareto optimal urban rail train scheduling problem. The aim of the model was to minimize the passengers’ total travel time and the number of used train stocks at the same time. A Pareto-based particle swarm optimization procedure was designed to solve the model. Finally, two different scaled urban rail lines were applied to test the model and the algorithm.

In the paper titled “Estimation of vessel collision risk index based on support vector machine,” L. Gang et al. proposed a collision risk index estimation model based on support vector machine and applied genetic algorithm to optimize the corresponding parameters. And the comparison between cross-validation-support vector machine, particle swarm optimization–support vector machine, and genetic algorithm–support vector machine models showed that genetic algorithm–support vector machine model generally provided a better performance for collision risk index estimation.

In the paper titled “Automobile chain maintenance parts delivery problem using an improved ant colony algorithm,” J. Gao et al. solved the automobile chain maintenance parts delivery problem by transferring the multi-depot vehicle routing problem with time windows to multi-depot vehicle routing problem with the virtual central depot. Then an improved ant colony optimization with saving algorithms, mutation operation, and adaptive ant-weight strategy was proposed to solve the problem. And the computational results indicated that the proposed algorithm was effective to solve the problem.

In the paper titled “Pareto front–based multi-objective real-time traffic signal control model for intersections using particle swarm optimization algorithm,” P. Jiao et al. proposed a Pareto front–based multi-objective traffic signal control model to obtain real-time signal parameters and evaluation indices. The objectives of the model were to minimize delay time, minimize number of stops, and maximize effective capacity. In addition, a step-by-step particle swarm optimization algorithm based on Pareto front was designed to solve the model. And the comparisons with the current situation and existing models showed that the proposed methodology was effective and robust enough for real-time traffic signal control.

In the paper titled “Optimization of transport network in the Basin of Yangtze River with minimization of environmental emission and transport/investment costs,” H. Shi et al. established a continual network design model to solve the bottleneck problem of the transport capacity of the ship-lock at the Three Gorges Dam with minimizing the transport cost and environmental emission as well as infrastructure construction cost. A particle swarm algorithm was designed to solve the bi-level model, and the results of the numerical study proved that expanding the ship-lock was better than constructing pass-dam highway.

In the paper titled “Research on unmanned combat aerial vehicle robust maneuvering decision under incomplete target information,” Y. Wang et al. designed a novel maneuvering decision-making method for the unmanned combat aerial vehicle to promote the real-time ability and solve the problem of uncertainty caused by incomplete target information. And simulations verified that the method effectively forecasted the general location of the enemy, shorted the time of taking position and attacking of the unmanned combat aerial vehicle (UCAV) when the enemy aircraft evaded, and extended the UCAV survival time when the enemy aircraft attacked.

In the paper titled “Prediction of bus passenger trip flow based on artificial neural network,” S. Yu et al. proposed a method to forecast the bus passenger trip flow in future period based on each zone building, land use situation, and bus accessibility. And the data of Dalian developing zone in China were used to assess the model. The results showed that the method proposed in the paper has feasibility and reliability.

In the paper titled “An improved artificial bee colony algorithm for vehicle routing problem with time windows: A real case in Dalian,” S. Yu et al. developed an integer linear model to solve a real western-style food delivery problem in Dalian city of China. And an improved artificial bee colony algorithm was proposed to solve the problem. The results indicated that the improved artificial bee colony algorithm was a feasible method to solve the real vehicle routing problem with time windows such as western-style food delivery.

In the paper titled “Forecasting traffic congestion status in terminal areas based on support vector machine,” H. Zhang et al. researched on a traffic congestion status forecasting method to improve the real-time monitoring and controlling of air traffic in terminal areas by proposing a traffic congestion status evaluation method and a traffic congestion status forecasting model. And then, a real case study from a terminal area in China was provided to test and verify the proposed evaluation method and forecasting model. The evaluation results showed that traffic congestion status of the terminal area could be classified into five levels: free, smooth, slightly congested, moderately congested, and severely congested. And the forecasting results showed that the mean absolute error and the cluster accuracy were 0.041% and 92.2%, respectively.

In the paper titled “Guidance control strategy for air traffic flow in terminal areas,” H. Zhang et al. researched on the characteristics of air traffic flow with guidance information to improve operation efficiency and safety for terminal control areas by proposing several microscopic air traffic flow models based on aircraft’s behavior and operation rules in terminal areas. In addition, a traffic flow guidance strategy was developed to optimize aircraft’s flight routes and velocities with several guidance decision factors. The proposed guidance strategy was verified using simulation with an airport terminal area in China. And the simulation results showed that the average arrival delays and the average departure delays decrease by 56.3% and 29.4%, respectively.

In the paper titled “Automatic inspection and classification for thin-film transistor liquid crystal display surface defects based on particle swarm optimization and one-class support vector machine,” A. Wu et al. proposed a non-destructive detection method using particle swarm optimization with one-class support vector machine to inspect thin-film transistor liquid crystal display surface micro-defects. The parameter optimization algorithms were used to optimize the parameters of support vector machine. The results indicated that the proposed system and method could inspect thin-film transistor liquid crystal display surface detects accurately.

In the paper titled “A prediction model and its validation of railway-induced building vibrations,” Y. Zhang et al. established a prediction model of the structural vibration response which was induced by the running trains. The building finite element (FE) model was updated by the double-confirmation analysis method. An in situ experiment of a five-floor residential building near a railway was used to validate the proposed method.

In the paper titled “Parametrisation of a Maxwell model for transient tyre forces by means of an extended firefly algorithm,” A. Hackl et al. presented an optimization strategy to find plausible and physically feasible solutions. The enhanced firefly algorithm was applied to the well-known Rastrigin functions and the tyre parametrisation problem. The results showed that the firefly algorithm could find optimization solutions with a high number.

In the paper titled “Co-evolutionary particle swarm optimization algorithm for two-sided robotic assembly line balancing problem,” Z. Li et al. optimized the two-sided robotic assembly line balancing problem with the objective of minimizing the cycle time. A co-evolutionary particle swarm optimization algorithm was developed to solve this problem. The performance of the proposed algorithm was tested by the modified seven well-known two-sided assembly line balancing problems available in the literature. The proposed algorithm was also compared with five other metaheuristics, and it outperformed most of other algorithms.

In the paper titled “Multi-objective Gaussian particle swarm algorithm optimization based on niche sorting for actuator design,” H. Liang et al. developed multi-objective optimal methods for actuator design. A multi-objective particle swarm optimization algorithm based on sorting method was designed. The simulation results showed that modified optimization algorithm could obtain a better Pareto front in contrast to classical non-dominated sorting genetic algorithm-II method.