Dual-Surrogate-Assisted Cooperative Particle Swarm Optimization for Expensive Multimodal Problems

Ji, Xinfang; Zhang, Yong; Gong, Dunwei; Sun, Xiaoyan

doi:10.1109/tevc.2021.3064835

Cited by 94 publications

(24 citation statements)

References 51 publications

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“…(2) Particle swarm algorithm: the particle swarm algorithm is an intelligent algorithm that simulates the foraging and flight behavior of bird swarms in nature. e algorithm is carried out according to the process of initialization, iterative update, and finding the optimal solution [8]. In the iterative process, the particle updates its own state by tracking two extreme values: one is the individual extreme value, that is, the current optimal solution of the particle itself, and the other is the global extreme value, that is, the current optimal solution of the entire population.…”

Section: Optimization Methods Of Artificial Bee Colony Algorithmmentioning

confidence: 99%

“…In formula (8), index is the sequence number of the tree node, depth is the depth information of the octree node, box describes the information of the constructed Obb bounding box, parent stores the parent node information of the node, and child stores the 8-child-node information of the node. If the node is a leaf node, child is empty and mesh is the member variable of the node.…”

Section: (8)mentioning

confidence: 99%

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Shortest Path Selection Algorithm for Cold Chain Logistics Transportation Based on Improved Artificial Bee Colony

Liu

2021

Scientific Programming

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Aiming at the problems of low shortest path selection accuracy, longer response time, and poor selection effect in current cold chain logistics transportation methods, a cold chain logistics transportation shortest path selection algorithm based on improved artificial bee colony is proposed. The improved algorithm is used to initialize the food source, reevaluate the fitness value of the food source, generate a new food source, optimize the objective function and food source evaluation strategy, and get an improved artificial bee colony algorithm. Based on the improved artificial bee colony algorithm, the group adaptive mechanism of particle swarm algorithm is introduced to initialize the position and velocity of each particle randomly. Dynamic detection factor and octree algorithm are adopted to dynamically update the path of modeling environment information. According to the information sharing mechanism between individual particles, the group adaptive behavior control is performed. After the maximum number of cycles, the path planning is completed, the shortest path is output, and the shortest path selection of cold chain logistics transportation is realized. The experimental results show that the shortest path selection effect of the cold chain logistics transportation of the proposed algorithm is better, which can effectively improve the shortest path selection accuracy and reduce the shortest path selection time.

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Section: Optimization Methods Of Artificial Bee Colony Algorithmmentioning

confidence: 99%

Section: (8)mentioning

confidence: 99%

Shortest Path Selection Algorithm for Cold Chain Logistics Transportation Based on Improved Artificial Bee Colony

Liu

2021

Scientific Programming

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“…In [55], GPU-based parallel multi-objective particle swarm optimization is proposed, which is mainly aimed at high-dimensional problems. In [56], a dual-surrogate assisted cooperative particle swarm optimization algorithm (SAEAs) is proposed to solve the expensive multi-mode optimization problem.…”

Section: B Multi-objective Particle Swarm Optimizermentioning

confidence: 99%

A Competitive Particle Swarm Algorithm Based on Vector Angles for Multi-Objective Optimization

2021

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Recently, the particle swarm algorithm (PSO) has demonstrated its effectiveness in solving multi-objective optimization problems. However, the performance of most existing multi-objective particle swarm algorithms depends largely on the global or individual best particles. Moreover, due to the rapid convergence of PSO in single objective optimization problems, PSO is prone to poorly distributed indicators when dealing with multi-objective optimization problems. To solve the above problems, we propose a multi-objective competitive particle swarm algorithm based on vector angles (VaCSO). Firstly, in order to remove the influence of global best particles or individual best particles on the algorithm, the competition mechanism is used. Secondly, in order to increase the diversity of solutions while maintaining the convergence of the algorithm, the population is clustered into two populations. Population 1 mainly considers the convergence of the solution in the offspring generation strategy. As a supplement, population 2 adds a new offspring generation strategy to maintain the distribution of the solution, and we innovatively proposed a three-particle competition to improve the distribution and diversity of particle swarms. Finally, based on vector angle information, we consider auxiliary learning to optimize the population gap, so as to improve the distribution of the algorithm. We have established two sets of comparative experiments to test the performance of VaCSO. We compared VaCSO with the currently popular multi-objective particle swarm optimizers and multi-objective evolutionary algorithms. Experimental results show that VaCSO has an excellent performance in convergence and distribution, and has a significant effect in optimizing quality.INDEX TERMS multi-objective optimization, competition particle swarm, competition mechanism, threeparticle competition, vector angle information, auxiliary learning.

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“…Experimental results show that the proposed algorithm can obtain a good feature subset with the lowest computational cost. Ji et al [20] studied a dual-surrogate-assisted cooperative PSO algorithm to tackle EMMOPs. e proposed DCPSO mechanism uses the two populations to seek multiple modalities simultaneously, effectively balancing exploration and exploitation of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Particle Swarm Optimization Algorithm Ensemble Model Applied to Classification of Unbalanced Data

Zheng

Qin

Liu

2021

Scientific Programming

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Unbalanced data classification is a major challenge in the field of data mining. Random forest, as an ensemble learning method, is usually used to solve the problem of unbalanced data classification. For the existing random forest-based classification prediction model, its hyperparameters are dependent on empirical settings, which leads to the problem of unsatisfactory model performance. In order to make random forest find the optimum modelling corresponding to the character of unbalanced data sets and improve the accuracy of prediction, we apply the improved particle swarm optimization to set reasonable hyperparameters of the model. This paper proposes a random forest-based adaptive particle swarm optimization on data classification, and an adaptive particle swarm used to optimize the hyperparameters in the random forest to ensure that the model can better predict the unbalanced data accurately. Aiming at the premature convergence that appears in the particle swarm optimization algorithm, the population is adaptively divided according to the population fitness and the adaptive update strategy is introduced to enhance the ability of particles to jump out of the local optimum. Experimental results show that our proposed algorithms outperform the traditional ones, especially regarding the evaluation criterion of F1-measure and accuracy. The results on the six keel unbalanced data set the advantages of our proposed algorithms are presented.

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Dual-Surrogate-Assisted Cooperative Particle Swarm Optimization for Expensive Multimodal Problems

Cited by 94 publications

References 51 publications

Shortest Path Selection Algorithm for Cold Chain Logistics Transportation Based on Improved Artificial Bee Colony

Shortest Path Selection Algorithm for Cold Chain Logistics Transportation Based on Improved Artificial Bee Colony

A Competitive Particle Swarm Algorithm Based on Vector Angles for Multi-Objective Optimization

Adaptive Particle Swarm Optimization Algorithm Ensemble Model Applied to Classification of Unbalanced Data

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