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Analysis of multiobjective evolutionary algorithms on the biobjective traveling salesman problem (1,2)

MULTIMEDIA TOOLS AND APPLICATIONS 2020年第41-42期79卷 30839-30860页

作者： Lai, Xinsheng Zhou, Yuren Shaoxing Univ Sch Comp Sci & Engn Shaoxing 312000 Zhejiang Peoples R China Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Peoples R China

multiobjective evolutionary algorithms have been successfully used to deal with multiobjective combinatorial optimization problems for more than two decades. However, we know little about the performance of multiobjective evolutionary algorithms on multiobjective combinatorial optimization problems in theory so far, especially on NP-hard ones from real-world, since Pareto curves are often of exponential size, meanwhile, evolutionary algorithms rely heavily on the use of randomness and are hard to understand from a theoretical point of view. In this paper, we theoretically investigate the performance of two simple multiobjective evolutionary algorithms with different population diversity mechanisms on the biobjective traveling salesman problem (1,2). It is found that one of them can efficiently find a 3/2-approximation Pareto curve for the problem, the best result so far. At the same time, these two multiobjective evolutionary algorithms are proved to be superior to a multiobjective local search algorithm, and the multiobjective local search algorithm is also proven to outperform these two multiobjective evolutionary algorithms as well. Finally, the population diversity is proved to be helpful in reducing the expected runtime of multiobjective evolutionary algorithm.

关键词： multiobjective evolutionary algorithm multiobjective traveling salesman problem Approximation algorithm Population diversity Combinatorial optimization problem

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Cooperative spectrum sensing in cognitive radio network using multiobjective evolutionary algorithms and fuzzy decision making

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AD HOC NETWORKS 2013年第3期11卷 1022-1036页

作者： Pradhan, Pyari Mohan Panda, Ganapati Indian Inst Technol Sch Elect Sci Bhubaneswar Orissa India

The cognitive radio has emerged as a potential solution to the problem of spectrum scarcity. Spectrum sensing unit in cognitive radio deals with the reliable detection of primary user's signal. Cooperative spectrum sensing exploits the spatial diversity between cognitive radios to improve sensing accuracy. The selection of the weight assigned to each cognitive radio and the global decision threshold can be formulated as a constrained multiobjective optimization problem where probabilities of false alarm and detection are the two conflicting objectives. This paper uses evolutionary algorithms to solve this optimization problem in a multiobjective framework. The simulation results offered by different algorithms are assessed and compared using three performance metrics. This study shows that our approach which is based on the concept of cat swarm optimization outperforms other algorithms in terms of quality of nondominating solutions and efficient computation. A fuzzy logic based strategy is used to find out a compromise solution from the set of nondominated solutions. Different tests are carried out to assess the stability of the simulation results offered by the heuristic evolutionary algorithms. Finally the sensitivity analysis of different parameters is performed to demonstrate their impact on the overall performance of the system. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Cognitive radio Cooperative spectrum sensing multiobjective evolutionary algorithm Fuzzy decision making

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Pareto optimization of cognitive radio parameters using multiobjective evolutionary algorithms and fuzzy decision making

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SWARM AND evolutionary COMPUTATION 2012年 7卷 7-20页

作者： Pradhan, Pyari Mohan Panda, Ganapati Indian Inst Technol Bhubaneswar Sch Elect Sci Bhubaneswar Orissa India

The design of cognitive radio engine aims at adapting the radio parameters to a predefined set of objective functions in communication system and may be formulated as a constrained multiobjective optimization problem. In the proposed work, an efficient design of orthogonal frequency division multiplexing based cognitive radio is carried out using multiobjective evolutionary algorithms. The performances of different algorithms are assessed and compared using three statistical metrics. The simulation results show that our proposed approach outperforms other algorithms while designing a cognitive radio engine. Our proposed approach which is based on the concept of cat swarm optimization, not only efficiently computes but also finds better nondominating solutions. In this paper, multiobjective evolutionary algorithms are applied to the parameter adaptation of a OFDM based cognitive radio engine. The spectral interference between primary and cognitive users is taken. into consideration which plays a major role in communication. Due to heuristic nature of evolutionary algorithms, the stability of the simulation results is verified using different statistical tests. A fuzzy logic based strategy is shown in order to find out a compromised solution on the Pareto front. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Cognitive radio Cognitive radio engine multiobjective evolutionary algorithm

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A New Fitness Evaluation Method Based on Fuzzy Logic in multiobjective evolutionary algorithms

A New Fitness Evaluation Method Based on Fuzzy Logic in Mult...

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IEEE Congress on evolutionary Computation (CEC)

作者： He, Zhenan Yen, Gary G. Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA

ISBN: (纸本)9781467315098

evolutionary algorithms have been effectively used to solve multiobjective optimization problems with a small number of objectives, two or three in general. However, when encounter problems with many objectives (more than five), nearly all algorithms performs poorly because of loss of selection pressure in fitness evaluation solely based upon Pareto domination. In this paper, we introduce a new fitness evaluation mechanism to continuously differentiate solutions into different degrees of optimality beyond the classification of the original Pareto dominance. Here, the concept of fuzzy logic is adopted to define fuzzy-dominated relation. As a case study, the fuzzy concept is incorporated into the NSGA-II, instead of the original Pareto dominance principle. Experimental results show that the proposed method exhibits a better performance in both convergence and diversity than the original NSGA-II for solving many-objective optimization problems. More importantly, it enables a fast convergence process.

关键词： Pareto optimality multiobjective evolutionary algorithm fuzzy logic NSGA-II

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Separated and Overlapping Community Detection in Complex Networks using multiobjective evolutionary algorithms

Separated and Overlapping Community Detection in Complex Net...

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2010 IEEE World Congress on Computational Intelligence

作者： Liu, Jing Zhong, Weicai Abbass, Hussein A. Green, David G. Univ New South Wales Sch Engn & Informat Technol Australian Def Force Acad Canberra ACT 2600 Australia Monash Univ Ctr Res Intelligent Syst Clayton Vic 3800 Australia

ISBN: (纸本)9781424481262

Both separated and overlapping communities are useful to analyze real networks in different situations. However, to the best of our knowledge, existing community detection methods based on evolutionary algorithms (EAs) can detect separate communities only. This is because it is difficult to represent overlapping communities in ways that are suitable for EAs. In this paper, we first design a representation method that can represent each individual as both separated and overlapping communities without assigning the number of communities in advance. We then design three objective functions to guide the evolutionary process in different conditions. Finally, based on the designed representation and objective functions, we propose a multiobjective evolutionary algorithm to solve CDPs (MEA_CDPs) under the framework of NSGA-II. In the experiments, 4 well-known real-life benchmark networks are used to validate the performance of MEA_CDPs, and the results shown that MEA_CDPs not only can find high quality communities, but also can detect both separated and overlapping communities at the same time, and present multiple types of communities. Moreover, the overlapping nodes identified by MEA_CDPs are really ambiguous according to their edge distributes in different communities. This illustrates the effectiveness of the objective functions we designed.

关键词： MEA_CDP NSGA-II complex network evolutionary computation multiobjective evolutionary algorithm

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On Neighborhood Exploration and Subproblem Exploitation in Decomposition Based multiobjective evolutionary algorithms

On Neighborhood Exploration and Subproblem Exploitation in D...

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IEEE Congress on evolutionary Computation (CEC)

作者： Zhou, Aimin Zhang, Yuting Zhang, Guixu Gong, Wenyin East China Normal Univ Shanghai Key Lab Multidimens Informat Proc 500 Dongchuan Rd Shanghai Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China

ISBN: (纸本)9781479974924

The decomposition based multiobjective evolutionary algorithm, denoted as MOEA/D, is an open framework for multiobjective optimization. This paper addresses the reproduction operation in MOEA/D. Generally, the solutions from a neighborhood of a subproblem are chosen as the mating pool for offspring reproduction. Since the Pareto set of an MOP shows some kind of structure in the decision space, the newly generated solutions based on the mating pool are arguable more likely to distribute along the population structure, which is called neighborhood exploration, and less likely to push a solution forward along the subproblem, which is called subproblem exploitation. To balance neighborhood exploration and subproblem exploitation, we propose to utilize both history and neighbor solutions for offspring reproduction. This idea is implemented through two operators based on the multivariate Gaussian distribution model, one is based on neighbor solutions and the other is based on previously visited solutions. When generating a new trial solution for a subproblem, one of the two operators is chosen with a probability. The proposed reproduction strategy is embedded in the MOEA/D framework and applied to a test suite. The comparison study has demonstrated that the new reproduction strategy is promising.

关键词： multiobjective evolutionary algorithm decomposition probabilistic model history information

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Multicast Flow Routing: Evaluation of Heuristics and multiobjective evolutionary algorithms

Multicast Flow Routing: Evaluation of Heuristics and Multiob...

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2010 IEEE World Congress on Computational Intelligence

作者： Bueno, Marcos L. P. Oliveira, Gina M. B. Univ Fed Uberlandia Fac Computat BR-38400902 Uberlandia MG Brazil

ISBN: (纸本)9781424481262

In this work, a multiobjective genetic algorithm-based model for multicast flow routing with QoS and Traffic Engineering requirements is discussed. Two heuristics for subtree reconnection are investigated, applicable in crossover and mutation operators. Experiments with three multiobjective evolutionary algorithms (NSGA-II, SPEA and SPEA2) and the proposed heuristics are carried on, whose results indicate that SPEA2 overcame SPEA and NSGA-II, besides providing the best combination with one the heuristics, obtaining the best average results. This work also shows that the proposed heuristics guarantee the consistency of the proposed model, since they fix a previous heuristic that can potentially generate invalid solutions.

关键词： NSGA-II QoS SPEA2 evolutionary computation heuristic evaluation multicast flow routing multiobjective evolutionary algorithm mutation operator quality of service quality of service subtree reconnection telecommunication network routing traffic engineering traffic engineering computing

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evolutionary algorithm with Ensemble Classifier Surrogate Model for Expensive multiobjective Optimization

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Transactions of Nanjing University of Aeronautics and Astronautics 2020年第S1期37卷 76-87页

作者： LAN Tian College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106P.R.China

For many real-world multiobjective optimization problems,the evaluations of the objective functions are computationally *** problems are usually called expensive multiobjective optimization problems(EMOPs).One type of feasible approaches for EMOPs is to introduce the computationally efficient surrogates for reducing the number of function *** from ensemble learning,this paper proposes a multiobjective evolutionary algorithm with an ensemble classifier(MOEA-EC)for *** specifically,multiple decision tree models are used as an ensemble classifier for the pre-selection,which is be more helpful for further reducing the function evaluations of the solutions than using single inaccurate *** extensive experimental studies have been conducted to verify the efficiency of MOEA-EC by comparing it with several advanced multiobjective expensive optimization *** experimental results show that MOEA-EC outperforms the compared algorithms.

关键词： multiobjective evolutionary algorithm expensive multiobjective optimization ensemble classifier surrogate model

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Atrributed Graph Embedding Based on Multi-objective evolutionary algorithm for Overlapping Community Detection

Atrributed Graph Embedding Based on Multi-objective Evolutio...

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IEEE Congress on evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Teng, Xiangyi Liu, Jing Xidian Univ Sch Artificial Intelligence Xian Peoples R China

ISBN: (纸本)9781728169293

Graph embedding methods aim to represent nodes in the network into a low-dimensional and continuous vector space while preserving the topological structure and varieties of relational information maximally. Nowadays the structural connections of networks and the attribute information about each node are more easily available than before. As a result, many community detection algorithms for attributed networks have been proposed. However, the majority of these methods cannot deal with the overlapping community detection problem, which is one of the most significant issues in the real-world complex network study. In addition, it is quite challenging to make full use of both structural and attribute information instead of only focusing on one part. To this end, in this paper we innovatively combine the graph embedding with multi-objective evolutionary algorithms (MOEAs) for overlapping community detection problems in attributed networks. As far as I am concerned, MOEA is first used to integrate with graph embedding methods for overlapping community detection. We term our method as MOEA-GEOV, which can automatically determine the number of communities without any prior knowledge and consider topological structure and vertex properties synchronously. In MOEA-GEOV, two objective functions concerning community structure and attribute similarity are carefully designed. Moreover, a heuristic initialization method is proposed to get a relatively good initial population. Then a novel encoding and decoding strategy is designed to efficiently represent the overlapping communities and corresponding embedded representation. In the experiments, the performance of MOEA-GEOV is validated on both single and multiple attribute real-world networks. The experimental results of community detection tasks demonstrate our method can effectively obtain overlapping community structures with practical significance.

关键词： graph embedding community detection multiobjective evolutionary algorithm overlapping community

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Feature-Engineering Enabled multiobjective evolutionary Impedance Fitting Technique

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2024年第5期71卷 4450-4462页

作者： Xiang, Yangxiao Pei, Xuejun Zhang, Yechi Jiang, Dong City Univ Hong Kong Hong Kong Peoples R China Huazhong Univ Sci & Technol Coll Elect & Elect Engn Wuhan 430074 Peoples R China Dalian Maritime Univ Dalian 116026 Peoples R China

The impedance fitting technique aims to make the impedance of an equivalent circuit the same as the measured impedance of the real system through parameter tuning. Since only the magnitude and phase of the impedance are measurable, the single-objective optimization algorithm, where the objective is to minimize the weighted summation of the fitting errors on magnitude and phase, has been widely used to achieve automatic impedance fitting. However, this scheme is easy to be trapped in the local minima, which, leads to poor fitting precision. A feature-engineering enabled multiobjective evolutionary impedance fitting (FEMEIF) technique is proposed in this article. By implementing several effective feature engineering techniques to fully leverage the limited impedance information, and by utilizing these synergetic features to enable multiobjective impedance fitting for realizing better hierarchy on individuals, FEMEIF much improves the impedance fitting performance with higher fitting precision and lower optimization variance compared with the traditional single-objective impedance fitting schemes. FEMEIF provides a novel methodology for improving the impedance fitting task, requires no additional measurements or expenses, and is universal for various optimization algorithms or equivalent circuits. Besides, it provides an open topic for exploring other potential features to achieve further improvement. The effectiveness of the FEMEIF is verified separately on fitting the impedance of a generic RLC-parallel circuit and on an industrial application of fitting the common-mode impedance of a motor system.

关键词： Feature engineering impedance fitting multiobjective evolutionary algorithm

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