A multi-objective memetic algorithm based on locality-sensitive hashing for one-to-many-to-one dynamic pickup-and-delivery problem

Zhu, Zexuan; Xiao, Jun; He, Shan; Ji, Zhen; Sun, Yiwen

doi:10.1016/j.ins.2015.09.006

Cited by 68 publications

(23 citation statements)

References 57 publications

(63 reference statements)

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“…When compared with the other four closely related classification algorithms, including hmAntMiner-C, hmAntMiner, CLUS-HSC, and CLUS-SC, ℎ AntMiner order performs superiorly or competitively in terms of predictive accuracy and obtains preferable comprehensibility. In the future work, other components like local search [44][45][46][47] and differential operators [48] can be introduced to ℎ AntMiner order to improve the efficiency of the algorithm.…”

Section: Discussionmentioning

confidence: 99%

Orderly Roulette Selection Based Ant Colony Algorithm for Hierarchical Multilabel Protein Function Prediction

Liang¹,

Guo²,

Sun³

et al. 2017

Mathematical Problems in Engineering

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Ant colony optimization (ACO) algorithms have been successfully applied to identify classification rules in data mining. This paper proposes a new ant colony optimization algorithm, named ℎ AntMiner order , for the hierarchical multilabel classification problem in protein function prediction. The proposed algorithm is characterized by an orderly roulette selection strategy that distinguishes the merits of the data attributes through attributes importance ranking in classification model construction. A new pheromone update strategy is introduced to prevent the algorithm from getting trapped in local optima and thus leading to more efficient identification of classification rules. The comparison studies to other closely related algorithms on 16 publicly available datasets reveal the efficiency of the proposed algorithm.

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Section: Discussionmentioning

confidence: 99%

Orderly Roulette Selection Based Ant Colony Algorithm for Hierarchical Multilabel Protein Function Prediction

Liang¹,

Guo²,

Sun³

et al. 2017

Mathematical Problems in Engineering

Self Cite

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“…Recently, research into the OPDP (e.g. Pérez et al [17], Sahin et al [18] and Soysal et al [19]) outnumbered studies into the MMPDP (e.g., Rieck et al [4]) and OMOPDP (e.g., Zhu et al [20]).…”

Section: Introductionmentioning

confidence: 99%

Multi-start heuristic approaches for one-to-one pickup and delivery problems with shortest-path transport along real-life paths

Zhuo

Xiong

et al. 2020

PLoS ONE

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The One-to-one Pickup and Delivery Problem with Shortest-path Transport along Real-life Paths (OPDPSTRP) is presented in this opaper. It is a variation of the One-to-one Pickup and Delivery Problem (OPDP), which is common in daily life, such as the Passenger Train Operation Plans (PTOP) and partial Taxi-sharing Problem. Unlike the classical OPDP, in the OPDPSTRP, (1) each demand must be transported along the shortest path according to passengers/shippers requirements, and (2) each vehicle should travel along a real-life path. First, six route structure rules are proposed for the OPDPSTRP, and a kind of Mixed-Integer Programming (MIP) models is formulated for it. Second, A Variable Neighborhood Descent (VND), a Variable Neighborhood Research (VNS), a Multi-Start VND (MS_VND) and a Multi-Start VNS (MS_VNS) with five neighborhood operators has been developed to solve the problem. Finally, The Gurobi solver, the VND, the VNS, the MS_VND and the MS_VNS have been compared with each other by 84 random instances partitioned in small size connected graphs, medium size connected graphs and large size connected graphs. From the test results we found that solutions generated by these approaches are often comparable with those found by the Gurobi solver, and the solutions found by these approaches are better than the solutions found by the Gurobi solver when solving instances with larger numbers of demands. In almost all instances, the MS_VND significantly outperforms the VND and the VNS in terms of solution quality, and outperforms the MS_VNS both in terms of solution quality and CPU time. In the instances with large numbers of demands, the MS_VND is still able to generate good feasible solutions in a reasonable CPU time, which is of vital practical significance for real-life instances. OPEN ACCESSCitation: Qi X, Fu Z, Xiong J, Zha W (2020) Multistart heuristic approaches for one-to-one pickup and delivery problems with shortest-path transport along real-life paths. PLoS ONE 15(2): e0227702.

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“…Although there has been considerable research conducted on Pareto-based optimal methods [30][31][32], further study is needed to enhance the convergence and diversity of the approximate Pareto front in the context of cloud computing. Table 1 summarizes important notations and their definitions used throughout this paper.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Metaheuristic for Multi-Objective Scientific Workflow Scheduling in a Cloud Environment

Anwar

Deng

2018

Applied Sciences

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Cloud computing has emerged as a high-performance computing environment with a large pool of abstracted, virtualized, flexible, and on-demand resources and services. Scheduling of scientific workflows in a distributed environment is a well-known NP-complete problem and therefore intractable with exact solutions. It becomes even more challenging in the cloud computing platform due to its dynamic and heterogeneous nature. The aim of this study is to optimize multi-objective scheduling of scientific workflows in a cloud computing environment based on the proposed metaheuristic-based algorithm, Hybrid Bio-inspired Metaheuristic for Multi-objective Optimization (HBMMO). The strong global exploration ability of the nature-inspired metaheuristic Symbiotic Organisms Search (SOS) is enhanced by involving an efficient list-scheduling heuristic, Predict Earliest Finish Time (PEFT), in the proposed algorithm to obtain better convergence and diversity of the approximate Pareto front in terms of reduced makespan, minimized cost, and efficient load balance of the Virtual Machines (VMs). The experiments using different scientific workflow applications highlight the effectiveness, practicality, and better performance of the proposed algorithm.

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A multi-objective memetic algorithm based on locality-sensitive hashing for one-to-many-to-one dynamic pickup-and-delivery problem

Cited by 68 publications

References 57 publications

Orderly Roulette Selection Based Ant Colony Algorithm for Hierarchical Multilabel Protein Function Prediction

Orderly Roulette Selection Based Ant Colony Algorithm for Hierarchical Multilabel Protein Function Prediction

Multi-start heuristic approaches for one-to-one pickup and delivery problems with shortest-path transport along real-life paths

A Hybrid Metaheuristic for Multi-Objective Scientific Workflow Scheduling in a Cloud Environment

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