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Computing Offloading Decision Based on Multi-objective immune algorithm in Mobile Edge Computing Scenario

WIRELESS PERSONAL COMMUNICATIONS 2023年第2期130卷 1025-1043页

作者： Zhu, Si-feng Sun, En-lin Zhang, Qing-hua Cai, Jiang-hao Tianjin Chengjian Univ Sch Comp & Informat Engn Tianjin 300384 Peoples R China Tianjin Chengjian Univ Lib Tianjin 300384 Peoples R China

Mobile edge computing (MEC) technology enables mobile devices in communication network systems offloading tasks to edge servers, effectively reducing response time and energy consumption. However, task offloading decisions become a significant difficulty when the system's mobile and service device count rises. In this paper, the problem of response time and energy consumption of the system is modeled as a multi-objective optimization problem, and we design an improved evolutionary algorithm based on immune algorithm, which can effectively obtain a set of solutions between response time and energy consumption. The simulation results show that our scheme can meet the response time requirements and obtain a lower energy consumption strategy when compared to the offloading scheme in the existing literature.

关键词： Mobile edge computing Computing offloading Multi-objective algorithm immune algorithm

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A weight-based multiobjective immune algorithm: WBMOIA

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ENGINEERING OPTIMIZATION 2010年第8期42卷 719-745页

作者： Gao, Jiaquan Fang, Lei Wang, Jun Zhejiang Univ Technol Zhijiang Coll Hangzhou 310024 Zhejiang Peoples R China McGill Univ Sch Comp Sci Montreal PQ H3A 2A7 Canada

A novel immune algorithm is suggested for finding Pareto-optimal solutions to multiobjective optimization problems based on opt-aiNET, the artificial immune system algorithm for multi-modal optimization. In the proposed algorithm, a randomly weighted sum of multiple objectives is used as a fitness function, and a local search algorithm is incorporated to facilitate the exploitation of the search space. Specifically, a new truncation algorithm with similar individuals (TASI) is proposed to preserve the diversity of the population. Also, a new selection operator is presented to create the new population based on TASI. Simulation results on seven standard problems (ZDT2, ZDT6, DEB, VNT, BNH, OSY and KIT) show that the proposed algorithm is able to find a much better spread of solutions and better convergence near the true Pareto-optimal front compared to the vector immune algorithm and the elitist non-dominated sorting genetic system.

关键词： immune algorithm multiobjective optimization similar individuals evolutionary algorithm

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Optimal Placement of Fault Indicators Using the immune algorithm

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IEEE TRANSACTIONS ON POWER SYSTEMS 2011年第1期26卷 38-45页

作者： Ho, Chin-Ying Lee, Tsung-En Lin, Chia-Hung Natl Kaohsiung Univ Appl Sci Dept Elect Engn Kaohsiung 807 Taiwan Kao Yuan Univ Dept Elect Engn Kaohsiung Taiwan

This paper examines the application of the immune algorithm for the problem of optimal placement of fault indicators to minimize the total cost of customer service outage and investment cost of fault indicators. The reliability index of each service zone is derived to solve the expected energy not served due to fault contingency, and the customer interruption cost is then determined according to the customer type and power consumption within the service zone. To demonstrate the effectiveness of the proposed IA methodology and solve the optimal placement of fault indicators, a practical distribution feeder of Taiwan Power Company is selected for computer simulation to explore the cost benefit of fault indicator placement.

关键词： Distribution automation system fault indicator immune algorithm

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Design of optimal digital IIR filters by using an improved immune algorithm

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2006年第12期54卷 4582-4596页

作者： Tsai, Jinn-Tsong Chou, Jyh-Homg Natl Pingtung Univ Educ Dept Comp Sci Pingtung 900 Taiwan Natl Kaohsiung First Univ Sci & Technol Dept Mech & Automat Engn Kaohsiung 824 Taiwan

In this paper, based on both the features of a biological immune system and the systematic reasoning ability of the Taguchi method, an improved immune algorithm, named the Taguchi-immune algorithm (TIA), is proposed to solve the problem of designing the optimal digital infinite impulse response (TIR) filters. In the TIA, the clonal proliferation within hypermutation for several antibody diversifications and the recombination by using the Taguchi method for the local search are integrated to improve the capabilities of exploration and exploitation. The systematic reasoning ability of the Taguchi method is executed in the recombination operations to select the better antibody genes to achieve the potential recombination, and consequently enhance the TIA. Based on minimizing the L-p-norm approximation error and minimizing the ripple magnitudes of both passband and stopband, a multicriterion combination is employed as the design criterion to obtain the optimal IIR filter that can fit different performance requirements. The proposed TIA approach is effectively applied to solve the multiparameter and multicriterion optimization problems of designing the digital low-pass, high-pass, bandpass, and bandstop filters. In these studied problems, there are many parameters and numerous local optima so that these studied problems are challenging enough for evaluating the performances of any proposed evolutionary approaches. The computational experiments show that the proposed TIA approach can obtain better digital IIR filters than the existing methods such as the genetic-algorithms-based method and the classic methods.

关键词： digital IIR filters immune algorithm multiple criteria Taguchi method

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A Multi-Learning immune algorithm for Numerical Optimization

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2015年第1期E98A卷 362-377页

作者： Wang, Shuaiqun Gao, Shangce Aorigele Todo, Yuki Tang, Zheng Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China Toyama Univ Fac Engn Toyama 9308555 Japan Kanazawa Univ Sch Elect & Comp Engn Kanazawa Ishikawa 9201192 Japan

The emergence of nature-inspired algorithms (NIA) is a great milestone in the field of computational intelligence community. As one of the NIAs, the artificial immune algorithm (AIS) mimics the principles of the biological immune system, and has exhibited its effectiveness, implicit parallelism, flexibility and applicability when solving various engineering problems. Nevertheless, AIS still suffers from the issues of evolution premature, local minima trapping and slow convergence due to its inherent stochastic search dynamics. Much effort has been made to improve the search performance of AIS from different aspects, such as population diversity maintenance, adaptive parameter control, etc. In this paper, we propose a novel multi-learning operator into the AIS to further enrich the search dynamics of the algorithm. A framework of embedding multiple commonly used mutation operators into the antibody evolution procedure is also established. Four distinct learning operators including baldwinian learning, cauchy mutation, gaussian mutation and lateral mutation are selected to merge together as a multi-learning operator. It can be expected that the multi-learning operator can effectively balance the exploration and exploitation of the search by enriched dynamics. To verify its performance, the proposed algorithm, which is called multi-learning immune algorithm (MLIA), is applied on a number of benchmark functions. Experimental results demonstrate the superiority of the proposed algorithm in terms of convergence speed and solution quality.

关键词： artificial immune algorithm mutation operators immune algorithm hybridization multi-learning

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A disassembly sequence planning approach with an advanced immune algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2012年第C11期226卷 2739-2749页

作者： Lu, Cong Liu, Ya-Chao Univ Elect Sci & Technol China Sch Mechatron Engn Chengdu 610054 Peoples R China Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian Peoples R China

This article proposes a disassembly sequence planning approach with an advanced immune algorithm. First, an enhanced support matrix considering the fasteners is constructed to represent the stability of a product, by which the method to derive the stability of parts in the product is given, and this stability is considered as one of the evaluation objectives in disassembly sequence planning. Second, the immune algorithm is improved to solve the disassembly sequence planning problem, where, a multiple single-parent mutation method is used to replace the crossover;two kinds of vaccines are extracted in two ways and inoculated with different probabilities, and the elite metabolism is used to ensure the effectiveness of the antibodies and also maintain the diversity of the population. Finally, the influence of different factors such as the vaccine extraction interval, the inoculation probability, elite metabolism probability on the evolution performance of the algorithm is investigated, and the validity of the proposed approach is verified through a case study.

关键词： Disassembly sequence planning immune algorithm stability support matrix

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Improving recognition and generalization capability of back-propagation NN using a self-organized network inspired by immune algorithm (SONIA)

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APPLIED SOFT COMPUTING 2005年第1期6卷 72-84页

作者： Widyanto, MR Nobuhara, H Kawamoto, K Hirota, K Kusumoputro, B Tokyo Inst Technol Dept Computat Intelligence & Syst Sci Midori Ku Yokohama Kanagawa 2268502 Japan Univ Indonesia Fac Comp Sci W Java Indonesia

To improve recognition and generalization capability of back-propagation neural networks (BP-NN), a hidden layer self-organization inspired by immune algorithm called SONIA, is proposed. B cell construction mechanism of immune algorithm inspires a creation of hidden units having local data recognition ability that improves recognition capability. B cell mutation mechanism inspires a creation of hidden units having diverse data representation characteristics that improves generalization capability. Experiments on a sinusoidal benchmark problem show that the approximation error of the proposed network is 1/17 times lower than that of BP-NN. Experiments on real time-temperature-based food quality prediction data shows that the recognition capability is 18% improved comparing to that of BP-NN. The development of the world first time-temperature-based food quality prediction demonstrates the real applicability of the proposed method in the field of food industry. (C) 2004 Elsevier B.V. All rights reserved.

关键词： back-propagation immune algorithm self-organization food quality prediction

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Fault section estimation in electric power systems using an optimization immune algorithm

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ELECTRIC POWER SYSTEMS RESEARCH 2010年第11期80卷 1341-1352页

作者： Leao, Fabio Bertequini Pereira, Rodrigo A. F. Mantovani, Jose R. S. Sao Paulo State Univ Engn Coll Ilha Solteira Dept Elect Engn Res Grp Elect Power Syst PlanningUNESP BR-15385000 Ilha Solteira SP Brazil

This paper proposes a methodology based on the unconstrained binary programming (UBP) model and an optimization immune algorithm (IA) to estimate fault sections in electric power systems. The UBP model is formulated using the parsimonious set covering theory for associating the alarms of the protective relay functions informed by the SCADA (supervisory control and data acquisition) system and the expected states of the protective relay functions. The IA is developed to minimize the UBP model and to estimate the fault sections as quickly and reliably as possible. The proposed methodology is tested using part of the South-Brazilian electric power system. The control parameters of the IA are set to reach the maximum computational efficiency and reduction of the processing time. The results show the methodology's potential to estimate fault sections in electric power system control centers in real-time. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Fault section estimation immune algorithm Power system protection Protective relaying

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immune algorithm for Solving the Optimization Problems of Computer Communication Networks

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The Journal of China Universities of Posts and Telecommunications 2000年第4期7卷 18-24页

作者： GAO Jie 1, WU Xin yu 2 (1. Department of Applied Mathematics and Physics, Nanjing University of Posts and Telecommunications, Nanjing 210003, P. R. China 2. Department of Electronical Engineering, Nanjing University of Posts and Telecommunications, Department of Applied Mathematics and Physics Nanjing University of Posts and Telecommunications Nanjing 210003 P. R. China Department of Electronical Engineering Nanjing University of Posts and Telecommunications Nanjing 210003 P. R. China

The basic problem in optimizing communication networks is to assign a proper circuit for each origin destination pair in networks so as to minimize the average network delay, and the network optimal route selection model is a multi constrained 0 1 nonlinear programming problem. In this paper, a new stochastic optimization algorithm, immune algorithm, is applied to solve the optimization problem in communication networks. And the backbone network vBNS is chosen to illustrate the technique of evaluating delay in a virtual network. At last, IA is compared with the optimization method in communication networks based on Genetic algorithm, and the result shows that IA is better than GA in global optimum finding.

关键词： immune algorithm computer communication networks network delay optimal route select

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Multiobjective optimization immune algorithm in dynamic environments and its application to greenhouse control

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APPLIED SOFT COMPUTING 2008年第2期8卷 959-971页

作者： Zhang, Zhuhong Guizhou Univ Coll Sci Inst Syst Sci & Informat Technol Guiyang 550025 Peoples R China

A novel multiobjective optimization immune algorithm in dynamic environments, as associated with Pareto optimality and immune metaphors of germinal center in the immune system, is proposed to deal with a class of dynamic multiobjective optimization problems which the dimension of the objective space may change over time. Several immune operators, depending on both somatic maturation and T-cell regulation, are designed to adapt to the changing environment so that the algorithm can achieve a reasonable tradeoff between convergence and diversity of population, among which an environmental recognition rule related to the past environmental information is established to identify an appearing environment. Preliminary experiments show that the proposed algorithm cannot only obtain great superiority over two popular algorithms, but also continually track the time-varying environment. Comparative analysis and practical application illustrate its potential. (c) 2007 Elsevier B.V. All rights reserved.

关键词： dynamic multiobjective optimization germinal center immune algorithm Pareto optimality greenhouse control

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