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A two-step personalized location recommendation based on multi-objective immune algorithm

INFORMATION SCIENCES 2019年 475卷 161-181页

作者： Geng, Bingrui Jiao, Licheng Gong, Maoguo Li, Lingling Wu, Yue Xidian Univ Key Lab Intelligent Percept & Image Understanding Int Res Ctr Intelligent Percept & ComputatSch Ar Minist EducJoint Int Res Lab Intelligent Percept Xian 710071 Shaanxi Peoples R China Xidian Univ Sch Comp Sci & Technol Xian 710071 Shaanxi Peoples R China Xidian Univ Key Lab Intelligent Percept & Image Understanding Int Res Ctr Intelligent Percept & Computat Minist EducJoint Int Res Lab Intelligent Percept Xian 710071 Shaanxi Peoples R China

The increasing number of users participating in location-based social networks has resulted in an information overload problem. Recommendation is a process that can free users from this dilemma. Most algorithms either ignore geographical and social properties, or require a tunable coefficient to determine the effect of each property on the outcome. Simultaneously combining these properties has proven to be a challenge. In this paper, we propose a two-step personalized location recommendation that is based on a multi-objective immune algorithm. It can simultaneously optimize the matching qualities of similarity and geographic properties as two functions, thereby providing location recommendations by improving one desired objective without detracting from the other. In the process, each list provides a different compromise between the similarity of check-in preferences and the geographical influence of the user. The user is offered choices from a set of lists that are compiled from the individual's selection of the various tradeoffs. The advantage of this algorithm is that it can recommend user lists without the need to tune any of the weighting coefficients. Experiments performed using the actual data demonstrated that the proposed algorithm is promising and is an effective means for providing accurate recommendations for a user's desired location. (C) 2018 Elsevier Inc. All rights reserved.

关键词： Location-based social networks Geographical information multi-objective immune algorithm Recommender systems

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Moia: multi-objective immune algorithm

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ENGINEERING OPTIMIZATION 2003年第2期35卷 143-164页

作者： Luh, GC Chueh, CH Liu, WW Tatung Univ Dept Mech Engn Taipei Taiwan

The paper describes a novel algorithm for finding Pareto optimal solutions to multi-objective optimization problems based on the features of a biological immune system. Inter-relationships within the proposed multi-objective immune algorithm (MOIA) resemble antibody-antigen relationships in terms of specificity, germinal center, and the memory characteristics of adaptive immune responses. Gene fragment recombination and several antibody diversification schemes (including somatic recombination, somatic mutation, gene conversion, gene reversion, gene drift, and nucleotide addition) were incorporated into the MOIA in order to improve the balance between exploitation and exploration. Using five performance metrics, MOIA simulation figures were compared with data derived from a strength Pareto evolutionary algorithm (SPEA). The results indicate that the MOIA outperformed the SPEA in several areas.

关键词： biological immune system multi-objective immune algorithm Pareto optimal solutions strength Pareto evolutionary algorithm

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Q-learning based multi-objective immune algorithm for fuzzy flexible job shop scheduling problem considering dynamic disruptions

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SWARM AND EVOLUTIONARY COMPUTATION 2023年 83卷

作者： Chen, Xiao-long Li, Jun-qing Xu, Ying Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Peoples R China HengXing Univ Sch Informat Engn Qingdao 266199 Peoples R China Yunnan Normal Univ Dept Math Kunming 650500 Yunnan Peoples R China

Confronted with complex industrial environments, dynamic disruptions like new job arrival and machine breakdown bring significant challenges to the robustness and stability of the manufacturing process, making the static production depart from the original scheduling scheme. To address this problem, a flexible job shop scheduling problem with fuzzy processing time, dynamic disruptions, and variable processing speeds is considered simultaneously. As well as three objectives of maximum completion time, total energy consumption, and average agreement index are demonstrated in this study. Then, a predictive-reactive dynamic/static rescheduling model is developed, where the off-line based mixed integer linear programming model and the on- line based rescheduling heuristics are proposed. Next, a multi-objective immune algorithm combined with a Q learning algorithm (Q-MOIA) is developed. In the proposed algorithm, an active decoding heuristic based on the interval insertion mechanism is used to optimize the initial solutions. After that, the clonal selection-based immune algorithm and the Q-learning algorithm are adopted to improve the exploration and exploitation ca- pabilities, respectively, where four objective-thiten Tierghborifood structures are designet: Everftually, extensive computational experiments were conducted on 27 instances under static and dynamic scenarios to demonstrate the superiority and stability of the proposed predictive-reactive dynamic static rescheduling model and the Q- MOIA. Comparative analysis with four state-of-the-art approaches revealed that proposed Q-MOIA outperformed in approximately 51.9, 66.7, and 83.3 % of the instances for the three multi-objective metrics.

关键词： Flexible job shop scheduling problem Fuzzy processing time Dynamic disruptions Predictive-reactive dynamic/static reacheduling model multi-objective immune algorithm Q-learning algorithm

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Wireless sensor network node deployment based on multi-objective immune algorithm

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INTERNATIONAL JOURNAL OF INTERNET PROTOCOL TECHNOLOGY 2018年第1期11卷 12-18页

作者： Li, Shanshan Huaihua Univ Coll Elect & Informat Engn Huaihua 418000 Hunan Peoples R China

Wireless sensor network (WSN) is made up of a large number of low-cost wireless sensor nodes, which can collect all types of data in its lifetime. Node deployment of WSN is a NP complete problem, and it can significantly influence the network coverage and energy consumption in WSN. In this paper, we try to exploit multi-objective immune algorithm to solve the node deployment problem in WSN. The proposed node deployment algorithm aims to maximise the degree of network coverage and minimise the energy consumption. In the proposed multi-objective immune algorithm, each antibody refers to a candidate solution in node deployment process, and the antibodies are randomly initialised with a specific range. In addition, we design a fitness function by two ranking modes. Finally, experiments are conducted to test the performance of the proposed algorithm. Particularly, we use some typical performance metric in this experiment, such as coverage degree, energy consumption, and maximum moved distance. Experimental results demonstrate that the proposed can both enhance the network coverage degree and reduce the energy consumption by minimising the moved distance.

关键词： wireless sensor network WSN node deployment algorithm multi-objective immune algorithm network coverage energy consumption

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A bi-subpopulation coevolutionary immune algorithm for multi-objective combinatorial optimization in multi-UAV task allocation

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COMPLEX & INTELLIGENT SYSTEMS 2025年第2期11卷 1-19页

作者： Chen, Xi Wan, Yu Qi, Jingtao Zhao, Zipeng Ruan, Yirun Tang, Jun Natl Univ Def Technol Lab big data & decis Changsha 410073 Peoples R China

With the development of Unmanned Aerial Vehicle (UAV) technology towards multi-UAV and UAV swarm, multi-UAV cooperative task allocation has more and more influence on the success or failure of UAV missions. From the operational research point of view, such problems belong to high-dimensional combinatorial optimization problems, which makes the solving process face many challenges. One is that the discrete and high-dimensional decision variables make the quality of the solution obtained with acceptable time not guaranteed. Second, the desired solution of real missions often needs to satisfy multiple objective functions, or a set of solutions for decision-making. Therefore, this paper constructs a multi-objective Combinatorial Optimization in multi-UAV Task Allocation Problem (MCOTAP) model, and proposes a Bi-subpopulation Coevolutionary immune algorithm (BCIA). The two coevolutionary mechanisms improve the lower limit of population diversity, and the evolutionary strategy pool integrating multiple strategies and the adaptive strategy selection mechanism enhance the local search ability in the late evolution. In the experiments, BCIA competes fairly with the mainstream multi-objective evolutionary algorithms (MOEAs), multi-objective immune algorithms (MOIAs) and the recently proposed multi-UAV mission planning algorithms. The experimental results on different test problems (including several multi-objective combinatorial optimization benchmark problems and the proposed MCOTAP model) show that BCIA has superior performance in solving multi-objective combinatorial optimization problems (MCOPs). At the same time, the effectiveness of each design component of BCIA has been comprehensively verified in the ablation study.

关键词： Coevolutionary algorithm multi-objective combinatorial optimization multi-objective immune algorithm multi-UAV Task allocation problem

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Joint Nodes and Sink Mobility Based immune Routing-Clustering Protocol for Wireless Sensor Networks

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WIRELESS PERSONAL COMMUNICATIONS 2021年第2期118卷 1189-1210页

作者： Rady, Asmaa Shokair, Mona El-Rabaie, EL-Sayed M. Sabor, Nabil Menoufia Univ Fac Elect Engn Menoufia Egypt Assiut Univ Fac Engn Elect Engn Dept Assiut 71516 Egypt

Recently, mobile wireless sensor network has drawn attention widely. In this paper, Joint Nodes and Sink Mobility based immune routing-Clustering protocol (JNSMIC) is proposed to support the mobility of the sink and the sensor nodes together. It depends on using the mobile sink for solving the hot spot problem and the multi-objective immune algorithm (MOIA) for clustering the network and finding the visiting locations of the mobile sink. The JNSMIC protocol considers different objectives during the clustering process, namely the consumption energy, network coverage, link connection time (LCT), residual energy and mobility. Also, it reduces the computational time of finding cluster heads (CHs) by dividing it into two phases. In the first phase, the candidate CHs set is formed based on residual energy, mobility factor and LCT of sensor nodes. While in the second phase, the MOIA algorithm is utilized to determine the final CHs subject to reducing the communication cost, improving the packet delivery ratio and ensuring network stability. JNSMIC performs the clustering process only if the remaining energy is below a threshold value thus the computational time and overhead control packets are reduced. In JNSMIC, the deputy CH concept is considered to perform the task of CH during CH failure. Furthermore, the proposed protocol performs a fault-tolerance process after transmitting each frame to maintain the link stability among CHs and their members which improves the throughput. Simulation results show that the JNSMIC protocol can effectively ameliorate the throughput while simultaneously giving lower energy expenditure and end-to-end delay.

关键词： Mobile wireless sensor networks (MWSN) Mobile sink Mobile sensors multi-objective immune algorithm Connectivity

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Rearrangement of mobile wireless sensor nodes for coverage maximization based on immune node deployment algorithm

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COMPUTERS & ELECTRICAL ENGINEERING 2015年 43卷 76-89页

作者： Abo-Zahhad, Mohammed Ahmed, Sabah M. Sabor, Nabil Sasaki, Shigenobu Assiut Univ Fac Engn Dept Elect & Elect Engn Assiut Egypt Niigata Univ Dept Elect & Elect Engn Niigata 9502181 Japan

One of the primary objectives of Wireless Sensor Network (WSN) is to provide full coverage of a sensing field as long as possible. The deployment strategy of sensor nodes in the sensor field is the most critical factor related to the network coverage. However, the traditional deployment methods can cause coverage holes in the sensing field. Therefore, this paper proposes a new deployment method based on multi-objective immune algorithm (MIA) and binary sensing model to alleviate these coverage holes. MIA is adopted here to maximize the coverage area of WSN by rearranging the mobile sensors based on limiting their mobility within their communication range to preserve the connectivity among them. The performance of the proposed algorithm is compared with the previous algorithms using Matlab simulation for different network environments with and without obstacles. Simulation results show that the proposed algorithm improves the coverage area and the mobility cost of WSN. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Wireless mobile sensor network Coverage holes multi-objective immune algorithm Deployment strategies Connectivity Mobility cost

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An Unequal multi-hop Balanced immune Clustering protocol for wireless sensor networks

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APPLIED SOFT COMPUTING 2016年 43卷 372-389页

作者： Sabor, Nabil Abo-Zahhad, Mohammed Sasaki, Shigenobu Ahmed, Sabah M. Assiut Univ Fac Engn Dept Elect & Elect Engn Assiut 71516 Egypt Niigata Univ Dept Elect & Elect Engn Niigata 9502181 Japan

In multi-hop routing, cluster heads near the base station act as relays for far cluster heads and thus will deplete their energy very quickly. Thus, hot spots in the sensor field result. This paper introduces a new clustering algorithm named an Unequal multi-hop Balanced immune Clustering protocol (UMBIC) to solve the hot spot problem and improve the lifetime of small and large scale/homogeneous and heterogeneous wireless sensor networks with different densities. UMBIC protocol utilizes the Unequal Clustering Mechanism (UCM) and the multi-objective immune algorithm (MOIA) to adjust the intra-cluster and inter-cluster energy consumption. The UCM is used to partition the network into clusters of unequal size based on distance with reference to base station and residual energy. While the MOIA constructs an optimum clusters and a routing tree among them based on covering the entire sensor field, ensuring the connectivity among nodes and minimizing the communication cost of all nodes. The UMBIC protocol rotates the role of cluster heads among the nodes only if the residual energy of one of the current cluster heads less than the energy threshold, as a result the time computational and overheads are saved. Simulation results show that, compared with other protocols, the UMBIC protocol can effectively improve the network lifetime, solve the hot spot problem and balance the energy consumption among all nodes in the network. Moreover, it has less overheads and computational complexity. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Hot spot problem Unequal clustering multi-hop routing multi-objective immune algorithm Homogeneous and heterogeneous networks

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Dynamic immune cooperative scheduling of agricultural machineries

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COMPLEX & INTELLIGENT SYSTEMS 2021年第6期7卷 2871-2884页

作者： Liu, Xiaoyan Zhu, Xinmeng Hao, Kuangrong Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China Minist Educ Engn Res Ctr Digitized Text & Apparel Technol Shanghai Peoples R China

Considering the low flexibility and efficiency of the scheduling problem, an improved multi-objective immune algorithm with non-dominated neighbor-based selection and Tabu search (NNITSA) is proposed. A novel Tabu search algorithm (TSA)-based operator is introduced in both the local search and mutation stage, which improves the climbing performance of the NNTSA. Special local search strategies can prevent the algorithm from being caught in the optimal solution. In addition, considering the time costs of the TSA, an adapted mutation strategy is proposed to operate the TSA mutation according to the scale of Pareto solutions. Random mutations may be applied to other conditions. Then, a robust evaluation is adopted to choose an appropriate solution from the obtained Pareto solutions set. NNITSA is used to solve the problems of static partitioning optimization and dynamic cross-regional co-operative scheduling of agricultural machinery. The simulation results show that NNITSA outperforms the other two algorithms, NNIA and NSGA-II. The performance indicator C-metric also shows significant improvements in the efficiency of optimizing search.

关键词： Scheduling of agricultural machineries multi-objective immune algorithm Non-dominated neighbor-based selection Tabu search algorithm Self-adapted mutation operator

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Planning Aircraft Taxiing Trajectories via a multi-Ojective immune Optimisation

Planning Aircraft Taxiing Trajectories via a Multi-Ojective ...

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2011 Seventh International Conference on Natural Computation(第七届自然计算国际会议 ICNC 2011)

作者： Jun Chen Paul Stewart School of Engineering University of Lincoln Brayford Pool Lincoln United Kingdom

Airport operations include departure sequencing, arrival sequencing, gate/stand allocation and ground movements (taxiing). During the past few decades, air traffic at major airports has been significantly increased and is expected to be so in the near future, which imposes a high requirement for more efficient cooperation across all airport operations. A very important element of this is an accurate estimation of the ground movement, which serves as a link to other operations. Previous researches have been concentrated on the estimation of aircraft taxi time. However, such a concept should be stretched more than just predicting time. It should also be able to estimate the associated cost, e.g. fuel burn, for it to achieve such an expected time. Hence, in this paper, an immune inspired multi-objective optimisation method is employed to investigate such trade-offs for different segments along taxiways, which leads to a set of different taxiing trajectories for each segment Each of these trajectories, on the one hand, provides an estimation of aircraft taxi time, and on the other hand, has great potential to be integrated into the optimal taxiway routing and scheduling process in a bid to find out the optimal taxiing not only in terms of reducing total taxi time but also in terms of lowering fuel consumption.

关键词： multi-objective immune algorithm aircraft ground movement optimal taxiing trajectories

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