A Multi-Objective Ant Colony System Algorithm for Airline Crew Rostering Problem With Fairness and Satisfaction

Zhou, Shuzi; Zhan, Zhi-Hui; Chen, Zong-Gan; Kwong, Sam; Zhang, Mengjie

doi:10.1109/tits.2020.2994779

Cited by 67 publications

(26 citation statements)

References 33 publications

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“…e moving environments of mobile robots are 20 × 20, 30 × 30, and 50 × 50 grid maps, respectively. At the same time, in order to verify the superiority of the proposed algorithm, the results obtained by the proposed algorithm are compared with those obtained by Ant System algorithm (AS) [13] and Ant Colony System algorithm (ACS) [26][27][28] in the same environment. In addition, in order to verify the stability of the improved ant colony algorithm, the three algorithms are simulated for 10 times and the experimental results are shown in Table 1.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Smooth Path Planning of Mobile Robot Based on Improved Ant Colony Algorithm

Wang

Zhao

et al. 2021

Journal of Robotics

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Aiming at the problems of slow convergence, easy to fall into local optimum, and poor smoothness of traditional ant colony algorithm in mobile robot path planning, an improved ant colony algorithm based on path smoothing factor was proposed. Firstly, the environment map was constructed based on the grid method, and each grid was marked to make the ant colony move from the initial grid to the target grid for path search. Then, the heuristic information is improved by referring to the direction information of the starting point and the end point and combining with the turning angle. By improving the heuristic information, the direction of the search is increased and the turning angle of the robot is reduced. Finally, the pheromone updating rules were improved, the smoothness of the two-dimensional path was considered, the turning times of the robot were reduced, and a new path evaluation function was introduced to enhance the pheromone differentiation of the effective path. At the same time, the Max-Min Ant System (MMAS) algorithm was used to limit the pheromone concentration to avoid being trapped in the local optimum path. The simulation results show that the improved ant colony algorithm can search the optimal path length and plan a smoother and safer path with fast convergence speed, which effectively solves the global path planning problem of mobile robot.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Smooth Path Planning of Mobile Robot Based on Improved Ant Colony Algorithm

Wang

Zhao

et al. 2021

Journal of Robotics

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“…Chen et al (2019) proposed a multi-objective ACS based on the MPMO framework for optimizing both the execution time and executing cost in cloud workflow scheduling problems. Zhou et al (2020) modeled the airline crew rostering problem as a bi-objective optimization problem that aimed at optimizing both the fairness and satisfaction of crew, and they extended the MPMO framework to efficiently solve the proposed model. Zhao et al (2021) proposed to use the MPMO framework for solving multi-objective cardinality constrained portfolio optimization problems.…”

Section: Extending Application Fieldmentioning

confidence: 99%

A survey on evolutionary computation for complex continuous optimization

et al. 2021

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Complex continuous optimization problems widely exist nowadays due to the fast development of the economy and society. Moreover, the technologies like Internet of things, cloud computing, and big data also make optimization problems with more challenges including Many-dimensions, Many-changes, Many-optima, Many-constraints, and Many-costs. We term these as 5-M challenges that exist in large-scale optimization problems, dynamic optimization problems, multi-modal optimization problems, multi-objective optimization problems, many-objective optimization problems, constrained optimization problems, and expensive optimization problems in practical applications. The evolutionary computation (EC) algorithms are a kind of promising global optimization tools that have not only been widely applied for solving traditional optimization problems, but also have emerged booming research for solving the above-mentioned complex continuous optimization problems in recent years. In order to show how EC algorithms are promising and efficient in dealing with the 5-M complex challenges, this paper presents a comprehensive survey by proposing a novel taxonomy according to the function of the approaches, including reducing problem difficulty, increasing algorithm diversity, accelerating convergence speed, reducing running time, and extending application field. Moreover, some future research directions on using EC algorithms to solve complex continuous optimization problems are proposed and discussed. We believe that such a survey can draw attention, raise discussions, and inspire new ideas of EC research into complex continuous optimization problems and real-world applications.

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“…Heuristic methods, such as greedy algorithm, VNS [53]- [55], and ACS [4], [14] are methods for obtaining a suboptimal solution in a reasonable time while the global optimum is not guaranteed.…”

Section: B Solution Representationmentioning

confidence: 99%

“…ACS+2opt: ACS [4], [14] is a population-based iterative approach in which several routing plans are constructed independently according to probability-based rules. The routing plan with lower cost is rewarded by strengthening the probability to select the edges that are involved in the routing plan.…”

Section: Cvrp Solversmentioning

confidence: 99%

SAFE: Scale-Adaptive Fitness Evaluation Method for Expensive Optimization Problems

Zhan

Zhang

2021

IEEE Trans. Evol. Computat.

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The key challenge of expensive optimization problems (EOP) is that evaluating the true fitness value of the solution is computationally expensive. A common method to deal with this issue is to seek for a less expensive surrogate model to replace the original expensive objective function. However, this method also brings in model approximation error. To efficiently solve the EOP, a novel scale-adaptive fitness evaluation (SAFE) method is proposed in this article to directly evaluate the true fitness value of the solution on the original objective function. To reduce the computational cost, the SAFE method uses a set of evaluation methods (EM) with different accuracy scales to cooperatively complete the fitness evaluation process. The basic idea is to adopt the low-accuracy scale EM to fast locate promising regions and utilize the high-accuracy scale EM to refine the solution accuracy. To this aim, two EM switch strategies are proposed in the SAFE method to adaptively control the multiple EMs according to different evolutionary stages and search requirements. Moreover, a neighbor best-based evaluation (NBE) strategy is also put forward to evaluate the solution according to its nearest high-quality evaluated solution, which can further reduce computational cost. Extensive experiments are carried out on the case study of crowdshipping scheduling problem in the smart city to verify the effectiveness and efficiency of the proposed SAFE method, and to investigate the effects of the two EM switch strategies and the NBE strategy. Experimental results show that the proposed SAFE method achieves better solution quality than some baseline and state-of-the-art algorithms, indicating an efficient method for solving EOP with a better balance between solution accuracy and computational cost.

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A Multi-Objective Ant Colony System Algorithm for Airline Crew Rostering Problem With Fairness and Satisfaction

Cited by 67 publications

References 33 publications

Smooth Path Planning of Mobile Robot Based on Improved Ant Colony Algorithm

Smooth Path Planning of Mobile Robot Based on Improved Ant Colony Algorithm

A survey on evolutionary computation for complex continuous optimization

SAFE: Scale-Adaptive Fitness Evaluation Method for Expensive Optimization Problems

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