Optimization of Heterogeneous Container Loading Problem with Adaptive Genetic Algorithm

Xia, Xiang; Yu, Caoyang; Xu, He; Zhu, Stuart X.

doi:10.1155/2018/2024184

Cited by 14 publications

(7 citation statements)

References 35 publications

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“…It provides new ideas for solving the large-scale complex problem and has been widely used in robot controlling due to its many advantages of self-organization, parallelism, distribution, flexibility, and robustness. Currently, humans have developed many swarm intelligence algorithms by imitating the biological groups and their genetic evolution process in nature, such as particle swarm optimization (PSO) algorithm, ant colony optimization (ACO) algorithm, shuffled frog-leaping algorithm, artificial fish-swarm algorithm, and cuckoo search algorithm [44][45][46][47][48]. Although many scholars have improved their theory and made achievements, the improved methods still have the disadvantages of slow convergence, computational complexity, and falling into its local optimum.…”

Section: Grey Wolf Algorithmmentioning

confidence: 99%

“…Previous Optimization Methods. For evaluating the performance of the GWO algorithm in rapidly tuning the online robust control scheme, we compare it with other online iteration algorithms like particle swarm optimization (PSO) [46], ant colony algorithm (ACO) [47], genetic algorithm (GA) [48], and Kleinman method [38]. e comparison is shown in Figure 8.…”

Section: Case 2: Comparative Computational Efficiency Withmentioning

confidence: 99%

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Robust Control of Underwater Vehicle-Manipulator System Using Grey Wolf Optimizer-Based Nonlinear Disturbance Observer and H-Infinity Controller

Dai

et al. 2020

Complexity

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This paper proposes a new trajectory tracking scheme for the constrained nonlinear underwater vehicle-manipulator system (UVMS). For overcoming the unmodeled uncertainties, external disturbances, and constraints of control inputs in the operation of UVMS, a modified constrained H∞ controller with a basic computed-torque controller (CTC) and a new designed nonlinear disturbance observer (NDO) are proposed. The CTC gives the nominal model-based control. The NDO is designed based on the system dynamics and used to online provide the estimation of the lumped disturbances. However, the designed NDO is an observer of biased estimation, i.e., it has a blind domain of disturbance estimation which cannot be rejected. In order to reject the biased estimation, the modified constrained H∞ controller is designed but with new features. To the best of our knowledge, the conventional H∞ robust controller is generally designed by calculating the Riccati equation offline and ignoring the constraints of control inputs made by the physical actuators, which are poor in handling the time-varying environment. In order to solve these issues, the modified constrained H∞ robust controller online optimized by grey wolf optimizer (GWO) is designed to ensure the control system has a compensation of the biased estimation, a satisfied constrained control input, and a fast calculation. In this paper, we modify the prior method of offline calculating the Riccati equation of the conventional H∞ robust controller to be an online optimization scheme and proposed a new constrained evaluation function. The new constrained evaluation function is online optimized by the GWO, which can both find out the constrained suboptimal control actions and compensate the biased estimation of the NDO for the UVMS. The whole system stability is proved. The effectiveness of the fast online calculation, tracking accuracy, and lumped disturbances rejection is shown by a series of UVMS simulations.

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Section: Grey Wolf Algorithmmentioning

confidence: 99%

Section: Case 2: Comparative Computational Efficiency Withmentioning

confidence: 99%

Robust Control of Underwater Vehicle-Manipulator System Using Grey Wolf Optimizer-Based Nonlinear Disturbance Observer and H-Infinity Controller

Dai

et al. 2020

Complexity

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“…Consequently, popular metaheuristics such as genetic algorithms, tabu search and simulated annealing, as well as heuristics based on the familiarity with the real problem, are often used to solve cargo loading problems. There were numerous papers in the past that used genetic algorithms (GA) for the CLP, while many papers do so even today [23,27,28]. Consequently, the paper [28] presents an adaptive GA, based on the general loading mathematical model aiming to maximize space utilization.…”

Section: Literature Reviewmentioning

confidence: 99%

“…There were numerous papers in the past that used genetic algorithms (GA) for the CLP, while many papers do so even today [23,27,28]. Consequently, the paper [28] presents an adaptive GA, based on the general loading mathematical model aiming to maximize space utilization. Based on the dynamic space division method, the authors develop a dedicated genetic algorithm that uses a two-stage real-number encoding method.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of a Novel Freight Railcar Load Planning and Monitoring System

et al. 2019

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Rail transport has unmistakable sustainable (environmental and economic) advantages in goods transportation on a massive scale. Goods loading constitutes an important segment of goods transportation by rail. Incorrect loading can be a serious threat to traffic safety as well as a generator of unforeseen expenses related to goods, railway infrastructure and vehicles. At the beginning, the paper identifies the presence of incorrect loading into freight railcars. The analysis of the available loading software has led to the conclusion that no software offers adequate support to the planning and monitoring of the loading of goods into a covered railcar using a forklift truck. For this reason, the main aim of the research is to formulate a mathematical model that includes real-world constraints, as well as the design and implementation of an original user-friendly load planning and monitoring software system. Experimental evaluations of the implemented software have been made based on single and multiple railcar pallet loading problems, considering the following three optimization criteria: maximization of wagon load weight, maximization of wagon volume utilization and maximization of weighted profit. By testing the optimization and visualization features of the software and analyzing the results, it has been concluded that it can offer full support to real load planning and monitoring problems.

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“…However, when the average fitness is close to the maximum fitness of the contemporary population, it is easy to cause a large number of individuals to have a lower crossover probability and mutation probability, which will stagnate the evolution. This improved AGA has also been applied to many fields such as the three-dimensional container loading problem [27] and the laminate stacking sequence optimization [28]. In addition, the algorithm proposed by Ren is only in view of individual fitness.…”

Section: Introductionmentioning

confidence: 99%

An Improved Adaptive Genetic Algorithm for Two-Dimensional Rectangular Packing Problem

Sang

Xiong

et al. 2021

Applied Sciences

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This paper proposes the hybrid adaptive genetic algorithm (HAGA) as an improved method for solving the NP-hard two-dimensional rectangular packing problem to maximize the filling rate of a rectangular sheet. The packing sequence and rotation state are encoded in a two-stage approach, and the initial population is constructed from random generation by a combination of sorting rules. After using the sort-based method as an improved selection operator for the hybrid adaptive genetic algorithm, the crossover probability and mutation probability are adjusted adaptively according to the joint action of individual fitness from the local perspective and the global perspective of population evolution. The approach not only can obtain differential performance for individuals but also deals with the impact of dynamic changes on population evolution to quickly find a further improved solution. The heuristic placement algorithm decodes the rectangular packing sequence and addresses the two-dimensional rectangular packing problem through continuous iterative optimization. The computational results of a wide range of benchmark instances from zero-waste to non-zero-waste problems show that the HAGA outperforms those of two adaptive genetic algorithms from the related literature. Compared with some recent algorithms, this algorithm, which can be increased by up to 1.6604% for the average filling rate, has great significance for improving the quality of work in fields such as packing and cutting.

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Optimization of Heterogeneous Container Loading Problem with Adaptive Genetic Algorithm

Cited by 14 publications

References 35 publications

Robust Control of Underwater Vehicle-Manipulator System Using Grey Wolf Optimizer-Based Nonlinear Disturbance Observer and H-Infinity Controller

Robust Control of Underwater Vehicle-Manipulator System Using Grey Wolf Optimizer-Based Nonlinear Disturbance Observer and H-Infinity Controller

Development of a Novel Freight Railcar Load Planning and Monitoring System

An Improved Adaptive Genetic Algorithm for Two-Dimensional Rectangular Packing Problem

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