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Improving quantum genetic optimization through granular computing

GRANULAR COMPUTING 2023年第4期8卷 709-729页

作者： Acampora, Giovanni Vitiello, Autilia Univ Naples Federico II Dept Phys Ettore Pancini Via Cintia 21 I-80126 Naples Italy Ist Nazl Fis Nucl INFN Sez Napoli Via Cintia 21 I-80126 Naples Italy

Quantum computers promise to revolutionize the world of computing thanks to some features of quantum mechanics that can enable massive parallelism in computation. This benefit may be particularly relevant in the design of evolutionary algorithms, where the quantum paradigm could support the exploration of multiple regions of the search space in a concurrent way. Although some efforts in this research field are ongoing, the potential of quantum computing is not yet fully expressed due to the limited number of qubits of current quantum processors. This limitation is even more acute when one wants to deal with continuous optimization problems, where the search space is potentially infinite. The goal of this paper is to address this limitation by introducing a hybrid and granular approach to quantum algorithm design, specifically designed for genetic optimization. This approach is defined as hybrid, because it uses a digital computer to evaluate fitness functions, and a quantum processor to evolve the genetic population;moreover, it uses granular computing to hierarchically reduce the size of the search space of a problem, so that good near-optimal solutions can be identified even on small quantum computers. As shown in the experiments, where IBM Q family processors are used, the usage of a granular computation scheme statistically enhances the performance of the state-of-the-art evolutionary algorithm implemented on quantum computers, when it is run to optimize well-known benchmark continuous functions.

关键词： evolutionary computation Granular computing Quantum computing Continuous optimization

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Performance of NSGA-III on Multi-objective Combinatorial Optimization Problems Heavily Depends on Its Implementations

Performance of NSGA-III on Multi-objective Combinatorial Opt...

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Genetic and evolutionary computation Conference (GECCO)

作者： Gong, Cheng Nan, Yang Pang, Lie Meng Zhang, Qingfu Ishibuchi, Hisao City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Southern Univ Sci & Technol Shenzhen 518055 Peoples R China City Univ Hong Kong Shenzhen Res Inst Shenzhen Peoples R China

ISBN: (纸本)9798400704949

Newly proposed many-objective algorithms have been almost always compared with NSGA-III for performance evaluation. Since the authors of the NSGA-III paper have not provided any source code, researchers usually use an available implementation in popular optimization platforms. This can lead to unreliable comparison results if different performance of NSGA-III is obtained depending on the choice of a platform. In this paper, we show that the implementations of NSGA-III are slightly different between the two most frequently used EMO optimization platforms: PlatEMO and pymoo. Then, we examine the effect of the implementation difference on the performance of NSGA-III in each platform. Our experimental results show that almost the same results are obtained from the two implementations on the frequently-used DTLZ test problems. However, our experimental results also show that clearly different results are obtained from the two implementations on multi-objective combinatorial optimization problems. Finally, we demonstrate that the weaker performance of the PlatEMO implementation of NSGA-III can be improved by replacing its normalization mechanism with the corresponding mechanism in Pymoo. That is, our experimental results show that small differences in the normalization mechanisms of the two implementations lead to large differences in their performance on multi-objective combinatorial optimization problems.

关键词： evolutionary computation EMO algorithms Combinatorial Optimization Multi-objective Optimization.

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Design Considerations for Pixelated Antennas Regarding Pixel Size and Symmetry

Design Considerations for Pixelated Antennas Regarding Pixel...

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International Symposium on Electromagnetic Compatibility (EMC Europe)

作者： Mair, Dominik Renzler, Michael Rueland, Raphael Univ Innsbruck Dept Mechatron Innsbruck Austria

ISBN: (纸本)9798350307351;9798350343045

Pixelated structures offer a unique possibility to design and optimize antennas tailor-made for very specific and challenging application scenarios, where conventional antenna design may lead to unsatisfactory results. Several different optimization techniques can be applied to these structures, depending on the desired goals and applications, making pixelated antennas very versatile fundamental structures. In this study, we investigate the optimization of pixelated antennas with a focus on the effects of pixel size and asymmetrical design approaches on gain. By simulation techniques and optimization algorithms, including genetic algorithms and multi-objective frameworks, we analyze how these design parameters influence the antenna's performance in terms of gain. Our findings reveal that smaller pixel sizes enhance the gain of large antennas, while asymmetrical designs offer advantages over symmetrical configurations.

关键词： Antennas Genetic algorithm Particle swarm optimization evolutionary computation Fragmented

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Route Planning through Genetic Algorithm for Multi-Axis Motion Control 2

Route Planning through Genetic Algorithm for Multi-Axis Moti...

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2nd IEEE Conference on Artificial Intelligence (CAI)

作者： Lee, Ting-Ching Tee, Jia-He Ting, Chuan-Kang Natl Tsing Hua Univ Dept Power Mech Engn Hsinchu 30013 Taiwan Natl Tsing Hua Univ Dept Comp Sci Hsinchu 30013 Taiwan

ISBN: (纸本)9798350354102;9798350354096

Route planning is crucial in the manufacturing industry, especially in cutting systems. Multi-axis motion control is pivotal to the laser cutting process in that it substantially enhances the efficiency by moving the source device along two axes simultaneously to target the workpiece. However, optimizing the routes for multi-axis motion control remains a complex challenge due to the need for coordination and efficiency. This study formulates it as a close-enough traveling salesman problem (CETSP) and designs a genetic algorithm (GA) for solving this routing problem. First, the line simplification algorithm is employed to convert the cutting pattern into a series of points, serving as the city points in the CETSP. Second, a structural approach is presented to address the difficulty in determining the turning points. For the GA, we developed crossover and mutation operators to improve its effectiveness and efficiency. The experimental results show that the proposed GA reduces route lengths by 17-28% and improves the variable speed in the route by 47-69% on two test cutting patterns.

关键词： Route planning cutting process evolutionary computation genetic algorithm multi-axis motion control line simplification algorithm close-enough traveling salesman problem

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A Study of Fitness Gains in Evolving Finite State Machines 36th

A Study of Fitness Gains in Evolving Finite State Machines

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36th Australasian Joint Conference on Artificial Intelligence (AI)

作者： Zoltai, Gabor Xie, Yue Neumann, Frank Univ Adelaide Sch Comp & Math Sci Optimisat & Logist Adelaide SA Australia Univ Cambridge Dept Engn Bio Inspired Robot Lab Cambridge England

ISBN: (纸本)9789819983902;9789819983919

Among the wide variety of evolutionary computing models, Finite State Machines (FSMs) have several attractions for fundamental research. They are easy to understand in concept and can be visualised clearly in simple cases. They have a ready fitness criterion through their relationship with Regular Languages. They have also been shown to be tractably evolvable, even up to exhibiting evidence of open-ended evolution in specific scenarios. In addition to theoretical attraction, they also have industrial applications, as a paradigm of both automated and user-initiated control. Improving the understanding of the factors affecting FSM evolution has relevance to both computer science and practical optimisation of control. We investigate an evolutionary scenario of FSMs adapting to recognise one of a family of Regular Languages by categorising positive and negative samples, while also being under a counteracting selection pressure that favours fewer states. The results appear to indicate that longer strings provided as samples reduce the speed of fitness gain, when fitness is measured against a fixed number of sample strings. We draw the inference that additional information from longer strings is not sufficient to compensate for sparser coverage of the combinatorial space of positive and negative sample strings.

关键词： evolutionary computation finite state machines regular languages

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Quantum Predator -Prey Brain Storm Optimization for Robust Optimal Allocation of D-STATCOM Devices 22

Quantum Predator -Prey Brain Storm Optimization for Robust O...

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22nd International Conference on Intelligent Systems Applications to Power Systems

作者： Kawauchi, Yusuke Someya, Naohisa Mori, Hiroyuki Chiang, Hsiao-Dong Meiji Univ Dept Network Design Tokyo Japan J Power Transmiss Network Co Ltd Hashimoto Branch Hashimoto Wakayama Japan Cornel Univ Dept Elect & Comp Engn Ithaca NY 14853 USA

ISBN: (纸本)9798331531768;9798331531751

This paper presents a groundbreaking solution to minimize distribution network losses by optimizing D-STATCOM ( Distribution Static Compensator) device allocation. The proposed method uses Quantum Predator-Prey Brain Storm Optimization (QPPBSO), which integrates Predator-Prey and Quantum -Computing superposition strategies to Brain Storm Optimization (BSO). The optimization problem is expressed as a mixed integer nonlinear programming (MINLP) problem, where the location and output variables of D-FACTS are optimized. With the rise of renewable energy, uncertainties in active distribution networks need to be considered in optimization problems. To address this issue, the proposed method employs Robust Optimization (RO), which ensures that the optimal allocation of D-STATCOM devices is robust to uncertainties. The simulation results indicate the effectiveness of the proposed method through testing it on the 69 -node distribution system.

关键词： D-FACTS loss minimization distribution networks evolutionary computation Predator-Prey Brain Storm Optimization robust optimization quantum computing

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Integrating Conjugate Gradients Into evolutionary Algorithms for Large-Scale Continuous Multi-Objective Optimization

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IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1801-1817页

作者： Ye Tian Haowen Chen Haiping Ma Xingyi Zhang Kay Chen Tan Yaochu Jin the Information Materials and Intelligent Sensing Laboratory of Anhui Province Institutes of Physical Science and Information TechnologyAnhui UniversityHefei 230601China the Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230601China IEEE the Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial IntelligenceAnhui UniversityHefei 230601China the Department of Computing The Hong Kong Polytechnic UniversityHong Kong SARChina the Faculty of Technology Bielefeld UniversityBielefeld 33619Germany

Large-scale multi-objective optimization problems(LSMOPs)pose challenges to existing optimizers since a set of well-converged and diverse solutions should be found in huge search *** evolutionary algorithms are good at solving small-scale multi-objective optimization problems,they are criticized for low efficiency in converging to the optimums of *** contrast,mathematical programming methods offer fast convergence speed on large-scale single-objective optimization problems,but they have difficulties in finding diverse solutions for ***,how to integrate evolutionary algorithms with mathematical programming methods to solve LSMOPs remains *** this paper,a hybrid algorithm is tailored for LSMOPs by coupling differential evolution and a conjugate gradient *** the one hand,conjugate gradients and differential evolution are used to update different decision variables of a set of solutions,where the former drives the solutions to quickly converge towards the Pareto front and the latter promotes the diversity of the solutions to cover the whole Pareto *** the other hand,objective decomposition strategy of evolutionary multi-objective optimization is used to differentiate the conjugate gradients of solutions,and the line search strategy of mathematical programming is used to ensure the higher quality of each offspring than its *** comparison with state-of-the-art evolutionary algorithms,mathematical programming methods,and hybrid algorithms,the proposed algorithm exhibits better convergence and diversity performance on a variety of benchmark and real-world LSMOPs.

关键词： Conjugate gradient differential evolution evolutionary computation large-scale multi-objective optimization mathematical programming

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Guest Editorial Special Issue on Large-Scale evolutionary Multiobjective Optimization and Its Practical Applications

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IEEE TRANSACTIONS ON evolutionary computation 2023年第3期27卷 398-400页

作者： Zhang, Xingyi Cheng, Ran Jin, Yaochu Sendhoff, Bernhard Anhui Univ Sch Artificial Intelligence Hefei 230601 Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518005 Peoples R China Bielefeld Univ Fac Technol D-33619 Bielefeld Germany Honda Res Inst Europe GmbH D-63073 Offenbach Germany

Complex optimization problems with hundreds or even thousands of decision variables and dozens of conflicting objectives are not uncommon in the real world. In the past five years, increased research efforts have been dedicated to large-scale multiobjective optimization problems (LSMOPs) by using a variety of search strategies, including variable grouping, variable analysis, problem transformation, dimensionality reduction, and novel recombination operators. Despite the success of these efforts in solving some general LSMOPs, there still remains a big gap between the LSMOPs that have been addressed and those encountered in real life, such as sparse, highly constrained, dynamic, and expensive LMOPs, as well as very large-scale and many-objective optimization problems that are widely seen and of paramount importance for solving scientific and engineering problems. Due to the significant practical importance of large-scale multiobjective optimization, there is a high demand for computationally efficient and effective evolutionary algorithms for solving LSMOPs.

关键词： Special issues and sections Optimization evolutionary computation Dimensionality reduction computational efficiency Search methods

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NEA: Revolutionizing Vehicle Classification with Optimal Optimization through Neuro-evolutionary Algorithm 3

NEA: Revolutionizing Vehicle Classification with Optimal Opt...

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International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)

作者： Bhuvaneswari, M. Dr MGR Educ & Res Inst Chennai Tamil Nadu India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

Recent advancements in artificial intelligence (AI) and machine learning (ML) have significantly enhanced various fields, including intelligent transportation systems (ITS). A notable area of progress is vehicle classification, crucial for improving road safety and traffic management. Traditional vehicle classification methods often struggle with accuracy and efficiency in complex conditions. This study introduces a novel method using Neuro-evolutionary Algorithms (NEAs) to optimize vehicle classification, combining neural networks with evolutionary computation for robust framework design and parameter optimization. NEAs adaptively refine neural network architectures, particularly enhancing their performance in diverse driving scenarios. Implemented in Google Colab, our NEA-optimized models demonstrated a remarkable classification accuracy of 98.35%, outperforming traditional and contemporary methods. This approach not only advances vehicle classification accuracy but also sets the stage for future developments in ITS, promoting safer, more efficient mobility.

关键词： Driving Behavior Classification Neuro-evolutionary Algorithms Vehicle Classification Intelligent Transportation Systems Optimization Machine Learning evolutionary computation Neural Networks

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A Discrete Predator-Prey Brain Storm Optimization Technique for Optimal Allocation of Micro-PMUs in Distribution System State Estimation 12

A Discrete Predator-Prey Brain Storm Optimization Technique ...

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12th IFAC Symposium on Control of Power and Energy Systems (CPES)

作者： Ito, Akio Mori, Hiroyuki Chiang, Hsiao-Dong Meiji Univ Dept Network Design Tokyo 1648525 Japan Cornell Univ Dept Elect & Comp Engn Ithaca NY 14853 USA

This paper proposes a practical method for determining the optimal allocation of micro-Phasor Measurement Units (tPMUs) for distribution system state estimation (DSSE). The proposed method formulates the state estimation based on the nest structure of DistFlow, which is used for power flow calculation in distribution systems due to the redundancy of less than 1. The use of mu PMUs can significantly improve the accuracy of estimates. However, Distribution System Operators (DSOs) need to consider optimization constraints before deciding on the optimal location of mu PMUs. To tackle this issue, this paper proposes the use of DPPBSO (Discrete Predator-Prey Brain Storm Optimization) of evolutionary computation (EC) to optimize the location of mu PMUs. PPBSO is an extension method that applies the Predator-Prey strategy to Brain Storm Optimization (BSO), and DPPSO is the discrete version of PPBSO used in solving combinatorial optimization problems. The Predator-Prey strategy is critical in improving the solution candidates by intensifying and diversifying solution searches in EC. Simulation results demonstrate the effectiveness of the proposed method in the IEEE 69-node distribution system. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： distribution network state estimation distribution automation evolutionary computation micro-PMU smart grids intelligent control of power systems

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