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In this study, a joint scheduling problem of taxiways and gates are considered where both the flight taxiing time and the gate idle time are optimized in the objective function. The problem is formulated as a nonlinear mathematical programming optimization problem which is NP-hard. A modified version of a particle swarm algorithm base on receding horizon is applied to solve it. A novel matrix coding scheme is designed to convert a particle position vector into the priority sequence of gates for each flight while a taxiing path planning heuristic algorithm based on conflict avoidance is proposed to allocate taxiway paths for all incoming and outgoing flights. In order to improve the search capability of PSO, an opposition-based learning search strategy is introduced to exploit more latitude of search space and solve the global minimum localization problem. The algorithm is tested based on the simulated operational data from the actual airport. Experimental results reveal that the proposed algorithm is effective in solving the problem.

The person recognition problem in different domain is a hard challenge on lower accuracy of object detection with massive image data, that is the gap between person target and background is small and hard to distinguish different posture of person target. The novel domain adaptive person detection model based on transfer data training is proposed for detecting data on new target domain. And domain adaptive loss function is designed to control the difference between the two models with self training, so as to avoid catastrophic forgetting in the source domain. And then the experiment results show that the improved deep self training domain adaptive learning model and loss function algorithm are effective and efficient for person recognition problem.

This paper studied a flight gate assignment problem where both the passenger service quality and the flight taxiing distance are considered in the objective function. A more efficient flight gate assignment model is built. Based on this model, we further propose an improved particle swarm optimization algorithm to solve it. A novel "flight-to-gate" coding scheme is designed to convert a particle position vector into the priority sequence of gates for each flight. Some tournament selection comparison rules are employed to update the position vectors of particles where a small part of infeasible individuals are kept in the iterative process of PSO. In order to improve the performance of PSO algorithm, a local search strategy based on swap operation is introduced to exploit more latitude of search space to anchor the global optimum. The algorithm is tested based on the simulated operational data from the actual airport. Experimental results reveal that our algorithm can achieve a good performance.

The path planning working space of resident community for escort robot is a complicated NP-Hard problem in the optimization research. The novel escort robot path planning model in resident community is presented, path describing as a grid graph of mathematical model. The Improved Quantum Particle Swarm Optimization algorithm is proposed for solving path planning model. The escort robot prototype is developed for the elderly assist in the community, which is also designed to walk with global path indoor and outdoor. The experiment platform is built on the developed escort robot and test environment. And then the experiment results are implemented for verifying effective and robust Improved Quantum Particle Swarm Optimization algorithm, as well as the algorithm can solve the real optimal escort robot path.

The pension robot can guide the elderly in community. The path planning environment of community for pension robot is so complicated that it is a NP-Hard problem in the optimization research. The local optimization solution is solved by the traditional swarm intelligent algorithm in path optimization problem. The novel model of path planning with community pension robot is presented, and the Quantum Particle Swarm Optimization algorithm is proposed for path planning. The robot can walk with global path in community, so as to avoid obstacle collision. The simulation results are implemented for verifying effective and robust algorithm, as well as the algorithm can overcome the shortcoming by traditional swarm intelligent algorithm. The performance is obviously improved for robot path planning.

This paper studied a flowshop scheduling problem with fuzzy due dates where the objective of maximizing the sum of customer satisfaction for the completion times of jobs is concerned. A self-adaptive differential evolution (SDE) algorithm has been employed to solve the fuzzy flowshop problem (FFP). The mutation scaling factor F and crossover probability CR are self-adaptive during the evolutionary process which can balance the exploration and the exploitation effectively. In order to make the SDE algorithm more effective and efficient, a local search strategy based on insert operation is introduced to exploit more latitude of search space to anchor the global optimum. The experimental results on the problem instance from literature demonstrate the good performance of the proposed SDE, thereby proving its worth as an attractive alternative for flowshop problems in uncertain environments.

This paper introduces an improved differential evolution algorithm for solving short-term multipurpose multistage batch scheduling problems (MMBSP) with the objective of minimizing the maximum completion time of all batches. A novel individual encoding and decoding scheme is designed for representing a scheduling solution for MMBSP. To improve the performance of DE further, a local search mechanism is introduced to exploit more latitude of search space to anchor the global optimum. In addition, the proposed DE is combined with a position bound disturbance strategy to add population diversity. Using those strategies, the proposed DE algorithm can get good balance between exploitation and exploration. The experimental results on some random generated problem instances demonstrate the good performance of the proposed DE.

This paper introduces an improved particle swarm optimization algorithm for continuous global optimization problems. Different from the traditional PSO, the proposed PSO adjusts dynamically the update modes of particles in terms of the population density where a new method based on the center of the particles is defined to measure the population density. Initial candidate solutions are generated by uniform design, and a local PSO search mechanism is designed to keep the diversity of swarm. In addition, an elite acceleration strategy is also introduced into the proposed PSO to improve the algorithm's intensification ability further. Using those operators, the proposed algorithm can get good balance between exploitation and exploration. The experimental results on unimodal and multimodal standard test problems demonstrate the good performance of the proposed PSO.

By considering the imprecise or fuzzy nature of the data in real-world problems, a single machine scheduling problem with uncertainty demand is investigated. A triangular fuzzy number is used to represent the uncertainty demand, and a half-trapezoid one is employed to represent fuzzy duedate. On the basis of the agreement index of fuzzy duedate and fuzzy completion time, this problem is formulated with the objective to maximize the total weighting agreement indexes for all the customer orders. We presented a hybrid algorithm QPSO of particle swarm optimization (PSO) and quantum evolutionary algorithm (QEA) to solve this problem. In the proposed QPSO, some novel coding schemes are designed for transforming a particle into a feasible process sequence of customer orders. Moreover, a mutation mechanism is also introduced into the QPSO and improves the diversity of the swarm greatly. The feasibility and effectiveness of the proposed QPSO is demonstrated by some simulation experiments.

The flowshop scheduling problem with fuzzy processing times is concerned in this paper. A triangular fuzzy number is used to represent the uncertainty processing times of jobs. The due windows have been assigned to all jobs. If a job is completed within its due window, then it incurs no scheduling cost. Otherwise, an earliness or tardiness cost is incurred. The objective is to find a job schedule such that the weighted sum of earliness and tardiness penalties of jobs is minimized. Schedules are generated by a proposed hybrid algorithm in the context of quantum evolutionary algorithm and particle swarm optimization approach. Three novel coding schemes are designed for transforming an individual into a sequence of jobs. Furthermore, a velocity disturbance strategy is also introduced into the proposed algorithm to improve the diversity of the swarm. The simulation results show that the proposed algorithm is able to obtain higher quality solutions stably and efficiently in the fuzzy flowshop scheduling problem.