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A hybrid ant colony optimization algorithm for a multi-objective vehicle routing problem with flexible time windows

INFORMATION SCIENCES 2019年 490卷 166-190页

作者： Zhang, Huizhen Zhang, Qinwan Ma, Liang Zhang, Ziying Liu, Yun Univ Shanghai Sci & Technol Sch Management Mail Box 459516 Jungong Rd Shanghai 200093 Peoples R China Shanghai Univ Engn Sci Sch Mat Engn Shanghai Peoples R China

In this paper, we present a multi-objective vehicle routing problem with flexible time windows (MOVRPFIexTW). In this problem, a fleet of vehicles can service a set of customers earlier and later than the required time with a given tolerance. This flexibility enables a logistics company to save distribution costs at the expense of customer satisfaction. This paper uses a direct interpretation of the vehicle routing problem with flexible time windows (VRPFIexTW) as a multi-objective problem, where the total distribution costs (including travel costs and fixed vehicle costs) are minimized and the overall customer satisfaction is maximized. We propose a solution strategy based on ant colony optimization and three mutation operators, which incorporates the concept of Pareto optimality for multi-objective optimization. The performance of the proposed approach was evaluated using the well-known benchmark Solomon's problems. Our experimental results show that the suggested approach is effective, because it provides solutions that are comparative to the best known results in the literatures. Finally, we not only highlight the advantages of MOVRPFIexTW when compared with the single objective VRPFIexTW, but also illustrate its applicability and validation by analyzing a real case. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Vehicle routing problem Flexible time windows ant colony optimization algorithm Multi-objective optimization Mutation operator

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A modified ant colony optimization algorithm for multi-item inventory routing problems with demand uncertainty

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2010年第5期46卷 598-611页

作者： Huang, Shan-Huen Lin, Pei-Chun Natl Cheng Kung Univ Dept Transportat & Commun Management Sci Tainan 701 Taiwan Natl Kaohsiung First Univ Sci & Technol Dept Logist Management Kaohsiung 811 Taiwan

This paper addresses an integrated model that schedules multi-item replenishment with uncertain demand to determine delivery routes and truck loads, where the actual replenishment quantity only becomes known upon arrival at a demand location. This paper departs from the conventional ant colony optimization (ACO) algorithm, which minimizes total travel length, and incorporates the attraction of pheromone values that indicate the stockout costs on nodes. The contributions of the paper to the literature are made both in terms of modeling this combined multi-item inventory management with the vehicle-routing problem and in introducing a modified ACO for the inventory routing problem. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Inventory routing problems ant colony optimization algorithm

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Application of ant colony optimization algorithm based on farthest point optimization and multi-objective strategy in robot path planning☆

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APPLIED SOFT COMPUTING 2024年 167卷

作者： Wu, Shuai Dong, Ani Li, Qingxia Wei, Wenhong Zhang, Yuhui Ye, Zijing Dongguan City Univ Sch Artificial Intelligence Dongguan 523419 Peoples R China Dongguan Univ Technol Sch Comp Sci & Technol Dongguan 523808 Peoples R China

With the continuous development of high technology and the continuous progress of intelligent industry, mobile robots are gradually widely used in various fields. In the field of mobile robot research, path planning is crucial. However, the current ant colony optimization algorithm applied to mobile robot path planning still has some limitations, such as early blind search, slower convergence speed, and lower path smoothness. To overcome these problems, this paper proposes an ant colony optimization algorithm based on farthest point optimization and multi-objective strategy. The algorithm introduces new heuristic information such as the normal distribution model, triangle inequality principle, smoothness function, safety value function, etc. It adopts multi-objective comprehensive evaluation indexes to judge the quality of paths. For the high-quality and poor-quality paths, the algorithm takes additional pheromone increments and decrements in pheromone concentration to speed up the algorithm's convergence. Besides, the farthest point optimization strategy is used to improve the quality of the paths further. Finally, to verify the algorithm's effectiveness, the algorithm is compared with 20 existing methods for solving the robot path planning problem, and the experimental results show that the algorithm exhibits better results in terms of convergence, optimal path length, and smoothness. Specifically, the algorithm can produce the shortest path in four different environments while realizing the least number of turns with faster convergence, further proving the effectiveness of the improved algorithm in this paper.

关键词： ant colony optimization algorithm Path planning Normal distribution Multi-objective strategy Farthest point optimization

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Hybridizing ant colony optimization algorithm with nonlinear programming method for effective optimal design of sewer networks

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WATER ENVIRONMENT RESEARCH 2019年第4期91卷 300-321页

作者： Moeini, Ramtin Afshar, Mohammad Hadi Univ Isfahan Fac Civil Engn & Transportat Dept Civil Engn Esfahan Iran Iran Univ Sci & Technol Sch Civil Engn Tehran Iran Iran Univ Sci & Technol Envirohydroinformat Ctr Excellence Tehran Iran

The ant colony optimization algorithm (ACOA) is hybridized with nonlinear programming (NLP) for the optimal design of sewer networks. The resulting problem is a highly constrained mixed integer nonlinear problem (MINLP) presenting a challenge even to the modern heuristic search methods. In the proposed hybrid method, The ACOA is used to determine pipe diameters while the NLP is used to determine the pipe slopes of the network by proposing two different formulations. In the first formulation, named ACOA-NLP1 a penalty method is used to satisfy the problem constraints while in the second one, named ACOA-NLP2, the velocity and flow depth constraints are expressed in terms of the slope constraints which are easily satisfied as box constraint of the NLP solver leading to a considerable reduction of the search space size. In addition, the assumption of minimum cover depth at the network inlets is used to calculate the nodal cover depths and the pump and drop heights at the network nodes, if required, leading to a complete solution. The total cost of the constructed solution is used as the objective function of the ACOA, guiding the ant toward minimum cost solutions. Proposed hybrid methods are used to solve three test examples, and the results are presented and compared with those produced by a conventional application of ACOA. The results indicate the effectiveness and efficiency of the proposed formulations and in particular the ACOA-NLP2 to optimally solve the sewer network design optimization problems. (C) 2018 Water Environment Federation

关键词： ant colony optimization algorithm explicit constraints nonlinear programming optimal design sewer network

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Wavelet phase estimation using ant colony optimization algorithm

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JOURNAL OF APPLIED GEOPHYSICS 2015年 122卷 159-166页

作者： Wang, Shangxu Yuan, Sanyi Ma, Ming Zhang, Rui Luo, Chunmei China Univ Petr CNPC Key Lab Geophys Explorat State Key Lab Petr Resources & Prospecting Beijing 102249 Peoples R China Univ Louisiana Lafayette Geol Sch Geosci Lafayette LA 70504 USA

Eliminating seismic wavelet is important in seismic high-resolution processing. However, artifacts may arise in seismic interpretation when the wavelet phase is inaccurately estimated. Therefore, we propose a frequency-dependent wavelet phase estimation method based on the ant colony optimization (ACO) algorithm with global optimization capacity. The wavelet phase can be optimized with the ACO algorithm by fitting nearby-well seismic traces with well-log data. Our proposed method can rapidly produce a frequency-dependent wavelet phase and optimize the seismic-to-well tie, particularly for weak signals. Synthetic examples demonstrate the effectiveness of the proposed ACO-based wavelet phase estimation method, even in the presence of a colored noise. Real data example illustrates that seismic deconvolution using an optimum mixed-phase wavelet can provide more information than that using an optimum constant-phase wavelet. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Wavelet phase estimation ant colony optimization algorithm Mixed-phase wavelet Deconvolution Weak signal

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An ant colony optimization algorithm-based classification for the diagnosis of primary headaches using a website questionnaire expert system

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2017年第5期25卷 4200-4210页

作者： Celik, Ufuk Yurtay, Nilufer Bandirma Onyedi Eylul Univ Dept Comp Programming Gonen Vocat Sch Balikesir Turkey Sakarya Univ Dept Comp Engn Fac Comp & Informat Sci Adapazari Turkey

The purpose of this research was to evaluate the classification accuracy of the ant colony optimization algorithm for the diagnosis of primary headaches using a website questionnaire expert system that was completed by patients. This cross-sectional study was conducted in 850 headache patients who randomly applied to hospital from three cities in Turkey with the assistance of a neurologist in each city. The patients filled in a detailed web-based headache questionnaire. Finally, neurologists' diagnosis results were compared with the classification results of an ant colony optimization-based classification algorithm. The ant colony algorithm for diagnosis classified patients with 96.9412% overall accuracy. Diagnosis accuracies of migraine, tension-type, and cluster headaches were 98.2%, 92.4%, and 98.2% respectively. The ant colony optimization-based algorithm has a successful classification potential on headache diagnosis. On the other hand, headache diagnosis using a website-based algorithm will be useful for neurologists in order to gather quick and precise results as well as tracking patients for their headache symptoms and medication usage by using electronic records from the Internet.

关键词： Data mining electronic health records and systems clinical decision support headache disorders ant colony optimization algorithm

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A modified ant colony optimization algorithm for multi-objective assembly line balancing

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SOFT COMPUTING 2017年第22期21卷 6881-6894页

作者： Zhong, Yu-guang Ai, Bo Harbin Engn Univ Coll Mech & Elect Engn Harbin 150001 Heilongjiang Peoples R China

In this paper, a novel ant colony optimization algorithm called modified ant colony optimization algorithm (MACO) is proposed for multi-objective single-model assembly line balancing problem (SALBP). The proposed MACO presents a novel heuristic information combined with subsequent task number and deviation time that can guide ants to find better solutions for SALBP. The proposed MACO also adopts three assignment methods (i.e., forward, backward and local rebalancing assignment methods) and stratified sequential algorithm combined with Pareto-optimal front as multi-objective decision. The objectives of SALBP are to minimize the number of workstations, maximize assembly line efficiency and minimize workload variation among workstations. In the latter part of the paper, the proposed MACO has been applied to solve Scholl benchmark problems which include both small-size and large-size problems. The performance of the proposed MACO has been compared with the multi-objective genetic algorithm and the multiple assignment genetic algorithm and has obtained improved results in many test problems.

关键词： ant colony optimization algorithm Assembly line balancing Multi-objective Heuristic information

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Bi-criteria ant colony optimization algorithm for minimizing makespan and energy consumption on parallel batch machines

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APPLIED SOFT COMPUTING 2017年 55卷 226-237页

作者： Jia, Zhao-hong Zhang, Yu-lan Leung, Joseph Y. -T. Li, Kai Minist Educ Key Lab Intelligent Comp & Signal Proc Beijing Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230039 Anhui Peoples R China Hefei Univ Technol Sch Management Hefei 230009 Anhui Peoples R China New Jersey Inst Technol Dept Comp Sci Newark NJ 07102 USA

We investigate the problem of minimizing the makespan and the total electric power cost simultaneously on a set of parallel identical batch-processing machines, where the jobs with non-identical sizes dynamically arrive. To address the bi-criteria problem, a Pareto-based ant colony optimization (PACO) algorithm is proposed. Depending on whether the current batch being delayed after the job is added into, two candidate lists are constructed to narrow the search space. Moreover, heuristic information is designed for each candidate list to guide the search. In addition, the objective-oriented local optimization methods are applied to improve the solution quality. Finally, the proposed algorithm is compared with existing multi objective algorithms through extensive simulation experiments. The experimental results indicate that the proposed algorithm outperforms all of the compared algorithms, especially for large-scale problems. (c) 2017 Elsevier B.V. All rights reserved.

关键词： Parallel batch machines ant colony optimization algorithm Makespan Energy consumption Green manufacturing

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Identification of gene knockout strategies using a hybrid of an ant colony optimization algorithm and flux balance analysis to optimize microbial strains

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2014年第PartB期53卷 175-183页

作者： Lu, Shi Jing Salleh, Abdul Hakim Mohamed Mohamad, Mohd Saberi Denis, Safaai Omatu, Sigeru Yoshioka, Michifumi Univ Teknol Malaysia Fac Comp Artificial Intelligence & Bioinformat Res Grp Skudai 81310 Johor Malaysia Osaka Inst Technol Dept Elect Informat & Commun Engn Osaka 5358585 Japan Osaka Prefecture Univ Fac Engn Naka Ku Sakai Osaka 5998531 Japan

Reconstructions of genome-scale metabolic networks from different organisms have become popular in recent years. Metabolic engineering can simulate the reconstruction process to obtain desirable phenotypes. In previous studies, optimization algorithms have been implemented to identify the near-optimal sets of knockout genes for improving metabolite production. However, previous works contained premature convergence and the stop criteria were not clear for each case. Therefore, this study proposes an algorithm that is a hybrid of the ant colony optimization algorithm and flux balance analysis (ACOFBA) to predict near optimal sets of gene knockouts in an effort to maximize growth rates and the production of certain metabolites. Here, we present a case study that uses Baker's yeast, also known as Saccharomyces cerevisiae, as the model organism and target the rate of vanillin production for optimization. The results of this study are the growth rate of the model organism after gene deletion and a list of knockout genes. The ACOFBA algorithm was found to improve the yield of vanillin in terms of growth rate and production compared with the previous algorithms. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： ant colony optimization algorithm Flux balance analysis Metabolic engineering optimization algorithm Gene knockout strategy Microbial strains

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Sequencing and scheduling of job and tool in a flexible manufacturing system using ant colony optimization algorithm

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2010年第9-12期50卷 1075-1084页

作者： Udhayakumar, P. Kumanan, S. Natl Inst Technol Dept Prod Engn Tiruchirappalli 620015 Tamil Nadu India

Many optimization problems from the manufacturing systems are very complex in nature and quite hard to solve by conventional optimization techniques. The theme of this paper is to generate an active schedules and optimal sequence of job and tool that can meet minimum makespan schedule for the flexible manufacturing system. It consists of similar work center which is capable of doing many operations. The tools are stored in a common tool magazine that shares with and serves for several work centers to reduce the cost of duplicating tools in each and every work center. This type of manufacturing system is used for a manufacturing environment in which tools are expensive. To achieve the objective, the jobs and tools are sequenced and scheduled. In this work, non-traditional optimization technique such as ant colony optimization (ACO) algorithm are proposed to derive near-optimal solutions which adopt the Extended Giffler and Thompson algorithm for active feasible schedule generation. In this paper, the proposed algorithm is used for solving number of problems taken from the literature. The results available for the various existing algorithms are compared with results obtained by the ACO algorithm. The analysis reveals that ACO algorithm provides better solution with reasonable computational time.

关键词： Scheduling Sequencing Flexible manufacturing system ant colony optimization algorithm

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