An superior tracking artificial bee colony for global optimization problems

Chu, Xianghua; Hu, Guozheng; Niu, Ben; Li, Li; Chu, Zhengrong

doi:10.1109/cec.2016.7744130

Cited by 9 publications

(4 citation statements)

References 23 publications

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“…This may lead to immature convergence or low exploitation for complex real-world problems (Karaboga et al 2014). To address this issue, inspired by PSO and comprehensive learning model (Liang et al 2006), we proposed a superior tracking strategy (STS) to enhance the search capability of original ABC for continuous functions (Chu et al 2016a). In this study, the novel STABC is developed to address the proposed combinatorial optimization problem.…”

Section: Superior Tracking Artificial Bee Colony Algorithmmentioning

confidence: 99%

An efficient auction mechanism for regional logistics synchronization

Chu

Cai

et al. 2018

J Intell Manuf

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This paper is the first attempt to propose an efficient auction mechanism for the regional logistics synchronization (RLS) problem, which aims to capture both logistics punctuality and simultaneity in a regional logistics network. The main motivation of RLS is motivated by our industrial collaborator, i.e. a third-party logistics (3PL) company, that if the delay has already occurred or will occur, the customers tend to pursue the simultaneity. We develop the one-sided Vickrey-Clarke-Groves (O-VCG) auction to realize incentive compatibility (on the buy side), budget balance, and individual rationality. The vehicle routing problem faced by the 3PL company is formulated as the lane covering problem with RLS requirements. Given the complexity of the proposed model, three canonical swarm intelligence meta-heuristics are employed to address the auction-based RLS problem. Besides, a superior tracking artificial bee colony with novel information learning mechanism is further developed to explore better solutions. Comparison results reveal the effectiveness of the proposed optimizers in terms of realized social welfare. Experimental results show that the O-VCG auction can achieve high synchronization level, approximately allocative efficiency and (ex post) budget balance.

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Section: Superior Tracking Artificial Bee Colony Algorithmmentioning

confidence: 99%

An efficient auction mechanism for regional logistics synchronization

Chu

Cai

et al. 2018

J Intell Manuf

Self Cite

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“…In the original search behavior of ABC, only one dimension of a bee is selected to learn and renew in each round of information updating. This search rule may result in the slow convergence or inferior exploring capability [49]. To address this issue, the STABC is introduced to improve the search capability and convergence speed for global optimization.…”

Section: Superior Tracking Artificial Bee Colony Algorithmmentioning

confidence: 99%

“…For the standard PSO, the constant =4.1 and the values =0.7298 and r 1 =r 2 =2.05 are gathered (Bratton and Kennedy 2007). For the STABC, the range of is empirically set as [0, 1.49], which could be a time-varying function as well [49]. With respect to the threshold probability Pr of STABC, the method introduced in Liang et al 's research [51] is adopted.…”

Section: Experiments Settingsmentioning

confidence: 99%

Cross-Trained Worker Assignment Problem in Cellular Manufacturing System Using Swarm Intelligence Metaheuristics

Cai

et al. 2018

Mathematical Problems in Engineering

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Cross-trained worker assignment has become increasingly important for manufacturing efficiency and flexibility in cellular manufacturing system because of the recent increase in labor cost. Researchers mainly focused on assigning skilled workers to tasks for favorable capacity or cost. However, few of them have recognized the need for skill level enhancement through cross-training to avoid excessive training, especially for workload balance across multiple cells. This study presents a new mathematical programming model aimed at minimum training and maximum workload balance with economical labor utilization, to address the worker assignment problem with a cross-training plan spanning multiple cells. The model considers the trade-off between training expenditure and workload balance to achieve a more flexible solution based on decision-maker’s preference. Considering the computational complexity of the problem, the classical swarm intelligence optimizers, i.e., particle swarm optimization (PSO) and artificial bee colony (ABC), are implemented to search the problem landscape. To improve the optimization performance, a superior tracking ABC with an augmented information sharing strategy is designed to address the problem. Ten benchmark problems are employed for numerical experiments. The results indicate the efficiency and effectiveness of the proposed models as well as the developed algorithms.

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“…In this work, a binary superior tracking artificial bee colony with dynamic Cauchy mutation (BSTABC-DCM) is proposed to further improve convergence speed and exploitation capability of ABC for feature selection. Two efficient search strategies, namely, superior tracking strategy [20] and dynamic Cauchy mutation, are integrated into the proposed algorithm. Compared with original ABC, superior tracking strategy enhances ABC' learning behaviors in two aspects: (1) instead of only updating one dimension in each iteration, bees learn from others in each dimension in each iteration; (2) instead of learning randomly, bees select individuals with better fitness to follow.…”

Section: Introductionmentioning

confidence: 99%

A Binary Superior Tracking Artificial Bee Colony with Dynamic Cauchy Mutation for Feature Selection

Chu

Gao

et al. 2020

Complexity

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This paper aims to propose an improved learning algorithm for feature selection, termed as binary superior tracking artificial bee colony with dynamic Cauchy mutation (BSTABC-DCM). To enhance exploitation capacity, a binary learning strategy is proposed to enable each bee to learn from the superior individuals in each dimension. A dynamic Cauchy mutation is introduced to diversify the population distribution. Ten datasets from UCI repository are adopted as test problems, and the average results of cross-validation of BSTABC-DCM are compared with other seven popular swarm intelligence metaheuristics. Experimental results demonstrate that BSTABC-DCM could obtain the optimal classification accuracy and select the best representative features for the UCI problems.

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An superior tracking artificial bee colony for global optimization problems

Cited by 9 publications

References 23 publications

An efficient auction mechanism for regional logistics synchronization

An efficient auction mechanism for regional logistics synchronization

Cross-Trained Worker Assignment Problem in Cellular Manufacturing System Using Swarm Intelligence Metaheuristics

A Binary Superior Tracking Artificial Bee Colony with Dynamic Cauchy Mutation for Feature Selection

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