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Decision variable classification based multi-objective multifactorial memetic algorithm for multi-objective multi-task optimization problem

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APPLIED SOFT COMPUTING 2024年 152卷

作者： Xu, Zhiwei Xu, Jiafeng Zhang, Kai Xu, Xin He, Juanjuan Wu, Ni Wuhan Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430065 Peoples R China Hubei Prov Key Lab Intelligent Informat Proc & Rea Wuhan 430065 Peoples R China China Univ Geosci Sch Automat Wuhan 430074 Peoples R China

Multi-task multi-objective optimization problems need to consider the algorithm's convergence and the population's diversity. The information transfer of decision variables with different characteristics may harm the effect of knowledge reuse. This paper proposes a novel hybrid multi-objective multifactorial memetic algorithm to address this issue. The proposed variable classification method will classify decision variables into convergence-related and diversity-related decision variables. Only the same type of decision variables in the source and target tasks can transfer information to avoid negative transfer. Different evolutionary operators are adopted according to the characteristics of decision variables during individual recombination. In addition, the proposed algorithm hybridizes the immune algorithm as the global evolutionary operator and the evolutionary gradient search algorithm as the local search operator into the multifactorial framework to enhance the searching ability. Finally, the proposed algorithm is compared with the state-of-the-art multi-objective evolutionary multitasking algorithms. The results of the experiments show that the proposed algorithm can achieve promising performance on the classical and complex multi-task multi-objective benchmark test suites.

关键词： evolutionary multitasking Multi-objective evolutionary algorithm evolutionary gradient search Decision variable classification Multi-objective multi-task optimization problem

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An Energy-Based Sampling Technique for Multi-Objective Restricted Boltzmann Machine

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2013年第6期17卷 767-785页

作者： Shim, Vui Ann Tan, Kay Chen Cheong, Chun Yew ASTAR Inst Infocomm Res Singapore 138632 Singapore Natl Univ Singapore Dept Elect & Comp Engn Singapore 117576 Singapore ASTAR Inst High Performance Comp Singapore 138632 Singapore

Estimation of distribution algorithms are gaining increased research interest due to their advantage in exploiting linkage information. This paper examines the sampling techniques of a restricted Boltzmann machine-based multi-objective (MO) estimation of distribution algorithm (REDA). The behaviors of the sampling techniques in terms of energy levels are rigorously investigated, and a sampling mechanism that exploits the energy information of the solutions in a trained network is proposed to improve the search capability of the algorithm. The REDA is then hybridized, with a genetic algorithm and a local search based on an evolutionary gradient approach, to enhance the exploration and exploitation capabilities of the algorithm. Thirty-one benchmark test problems, which consist of different difficulties and characteristics, are used to examine the efficiency of the proposed algorithm. Empirical studies show that the proposed algorithm gives promising results in terms of inverted generational distance and nondominance ratio in most of the test problems.

关键词： Estimation of distribution algorithms (EDAs) evolutionary gradient search genetic algorithm (GA) multi-objective (MO) optimization restricted Boltzmann machine sampling technique

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Multidrop Transmission System Based on Reflection-Canceling Scheme With Reflection Compensation Lines

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IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY 2020年第6期62卷 2576-2584页

作者： Akeboshi, Yoshihiro Itakura, Hiroshi Miyazaki, Chiharu Mitsubishi Electr Corp Informat Technol R&D Ctr Kamakura Kanagawa 2478501 Japan

In the digital communication system, waveform distortion due to reflection of the multidrop transmission line is one of the main issues for high-speed signal transmission. Although many approaches to reducing the reflection, such as asymmetrical power splitter, stub series terminated logic, and the segmental transmission line have been used in the past, the drawbacks of amplitude loss at the receivers, or high power consumption due to excessively low impedance design are still problems. In this article, we introduce a new method, "reflection compensation line (RCL)," which is constructed in isometric length of main bus segments and stub lines. The method using the RCL intentionally generates reflections to cancel the distortion and restore the ideal waveform at the receivers. In order to reduce the waveform distortion effectively, an optimal set of transmission parameters is determined by means of evolutionary gradient search. The performance of the proposed method especially at the stub lines of both ends of the main bus line is confirmed using the SPICE simulation, and its applicability to practical systems is also discussed.

关键词： Receivers Impedance Junctions Distortion Topology Impedance matching Delays evolutionary gradient search impedance matching impulse response multidrop transmission line numerical analysis reflection compensation line

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A Hybrid Adaptive evolutionary Algorithm in the Domination-based and Decomposition-based Frameworks of Multi-objective Optimization

A Hybrid Adaptive Evolutionary Algorithm in the Domination-b...

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IEEE Congress on evolutionary Computation (CEC)

作者： Shim, V. A. Tan, K. C. Tan, K. K. Natl Univ Singapore Dept Elect & Comp Engn Singapore 117548 Singapore

ISBN: (纸本)9781467315098

Under the framework of evolutionary paradigms, many variations of evolutionary algorithms have been designed. Each of the algorithms performs well in certain cases and none of them are dominating one another. This study is based on the idea of synthesizing different evolutionary algorithms so as to complement the limitations of each algorithm. On top of this idea, this paper proposes an adaptive mechanism that synthesizes a genetic algorithm, differential evolution and estimation of distribution algorithm. The adaptive mechanism takes into account the ratio of the number of promising solutions generated from each optimizer in an early stage of evolutions so as to determine the proportion of the number of solutions to be produced by each optimizer in the next generation. Furthermore, the adaptive algorithm is also hybridized with the evolutionary gradient search to further enhance its search ability. The proposed hybrid adaptive algorithm is developed in the domination-based and decomposition-based multi-objective frameworks. An extensive experimental study is carried out to test the performances of the proposed algorithms in 38 state-of-the-art benchmark test instances.

关键词： Decomposition differential evolution domination genetic algorithm estimation of distribution algorithm evolutionary gradient search hybrid multi-objective optimization

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A Hybrid Estimation of Distribution Algorithm for Solving the Multi-objective Multiple Traveling Salesman Problem

A Hybrid Estimation of Distribution Algorithm for Solving th...

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IEEE Congress on evolutionary Computation (CEC)

作者： Shim, V. A. Tan, K. C. Tan, K. K. Natl Univ Singapore Dept Elect & Comp Engn Singapore 117548 Singapore

ISBN: (纸本)9781467315098

The multi-objective multiple traveling salesman problem (MmTSP) is a generalization of the classical multi-objective traveling salesman problem. In this paper, a formulation of the MmTSP, which considers the weighted sum of the total traveling costs of all salesmen and the highest traveling cost of any single salesman, is proposed. An estimation of distribution algorithm (EDA) based on restricted Boltzmann machine is used for solving the formulated problem. The EDA is developed in the decomposition framework of multi-objective optimization. Due to the limitation of EDAs in generating a wide range of solutions, the EDA is hybridized with the evolutionary gradient search. Simulation studies are carried out to examine the optimization performances of the proposed algorithm on MmTSP with different number of objective functions, salesmen and problem sizes.

关键词： Decomposition estimation of distribution algorithm evolutionary gradient search hybrid multi-objective optimization multiple traveling salesman problem restricted Boltzmann machine

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Noisy Optimization: A Theoretical Strategy Comparison of ES, EGS, SPSA & IF on the Noisy Sphere 11

Noisy Optimization: A Theoretical Strategy Comparison of ES,...

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13th Annual Genetic and evolutionary Computation Conference (GECCO)

作者： Finck, Steffen Beyer, Hans-Georg Melkozerov, Alexander Vorarlberg Univ Appl Sci Dornbirn Austria Univ Ulm Ulm Germany

ISBN: (纸本)9781450305570

This paper presents a performance comparison of 4 direct search strategies in continuous search spaces using the noisy sphere as test function. While the results of the Evolution Strategy (ES), evolutionary gradient search (EGS), Simultaneous Perturbation Stochastic Approximation (SPSA) considered are already known from literature, Implicit Filtering (IF) as the fourth strategy is firstly analyzed in this paper. After a short review of ES, EGS, and SPSA, the derivation of the quality gain formula of IF is sketched. Using the results, a comparison of the strategies is performed that worked out the similarities and differences of the strategies.

关键词： Evolution Strategies evolutionary gradient search Simultaneous Perturbation Stochastic Approximation Implicit Filtering performance analysis and comparison progress rate quality gain

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Adaptive Memetic Computing for evolutionary Multiobjective Optimization

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IEEE TRANSACTIONS ON CYBERNETICS 2015年第4期45卷 610-621页

作者： Shim, Vui Ann Tan, Kay Chen Tang, Huajin Inst Infocomm Res Singapore 138632 Singapore Natl Univ Singapore Dept Elect & Comp Engn Singapore 117576 Singapore

Inspired by biological evolution, a plethora of algorithms with evolutionary features have been proposed. These algorithms have strengths in certain aspects, thus yielding better optimization performance in a particular problem. However, in a wide range of problems, none of them are superior to one another. Synergetic combination of these algorithms is one of the potential ways to ameliorate their search ability. Based on this idea, this paper proposes an adaptive memetic computing as the synergy of a genetic algorithm, differential evolution, and estimation of distribution algorithm. The ratio of the number of fitter solutions produced by the algorithms in a generation defines their adaptability features in the next generation. Subsequently, a subset of solutions undergoes local search using the evolutionary gradient search algorithm. This memetic technique is then implemented in two prominent frameworks of multiobjective optimization: the domination- and decomposition-based frameworks. The performance of the adaptive memetic algorithms is validated in a wide range of test problems with different characteristics and difficulties.

关键词： Differential evolution estimation of distribution algorithm evolutionary gradient search genetic algorithm memetic computing multiobjective optimization

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Robot odor localization based on evolutionary gradient algorithm under the Gaussian plume model

Robot odor localization based on evolutionary gradient algor...

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2014 International Conference on Future Information Engineering and Manufacturing Science(FIEMS 2014)

作者： Zhiqi Zhao Jiandong Fang Jin Chuan Campus of Inner Mongolia University of Technology

ISBN: (纸本)9781138026445

Inspired by the biological odor localization,this paper presents an active olfaction positioning technology used in mobile *** assignment method is used to search plume randomly in the design and a search algorithm is used to track the plume,the algorithm is called evolutionary gradient search *** experimental results show that this algorithm can guarantee mobile robot move along the plume from low concentration to high *** Environment the design simulates is point source diffusion in the breeze environment,so the Gaussian plume model is used as Plume model which is widely used in the international;the model is suitable for point source *** design ideas were completed in Matlab,verify the correctness of the design ideas on robot to search and track the gas.

关键词： Active Olfaction Positioning evolutionary gradient search Assignment Gaussian Plume

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