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Dealing With Structure Constraints in evolutionary Pareto Set Learning

IEEE Transactions on evolutionary computation 2025年第3期29卷 616-630页

作者： Lin, Xi Zhang, Xiaoyuan Yang, Zhiyuan Zhang, Qingfu City University of Hong Kong Department of Computer Science Hong Kong Hong Kong

In the past few decades, many multiobjective evolutionary optimization algorithms (MOEAs) have been proposed to find a finite set of approximate Pareto solutions for a given problem in a single run. However, in many real-world applications, it could be desirable to have structure constraints on the entire optimal solution set, which define the patterns shared among all solutions. The current population-based MOEAs cannot properly handle such requirements. In this work, we make a first attempt to add structure constraints on the solution set. Specifically, we propose to model such a multiobjective optimization problem as a set optimization problem with structure constraints. The structure constraints define some patterns that all the solutions are required to share. Such patterns can be fixed components shared by all solutions, specific relations among decision variables, and the required shape of the Pareto set. In addition, we develop a simple yet efficient evolutionary stochastic optimization method to learn the set model, which only requires a low computational budget similar to classic MOEAs. With our proposed method, the decision-makers can easily trade off the Pareto optimality with preferred structures, which is not supported by other MOEAs. A set of experiments on benchmark test suites and real-world application problems demonstrates that our proposed method is effective. © 1997-2012 IEEE.

关键词： Optimization Pareto optimization Multitasking evolutionary computation Vectors Training Rockets Manufacturing Manifolds Linear programming

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A neo-cooperation search based evolutionary algorithm for multi-objective electric rope shovel production scheduling

FRONTIERS IN MECHANICAL ENGINEERING-SWITZERLAND

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FRONTIERS IN MECHANICAL ENGINEERING-SWITZERLAND 2025年 11卷

作者： Zhang, Jue Yue, Haifeng Wang, Yongpeng Guo, Ruhan Shao, Shuai State Key Lab Intelligent Min Equipment Technol Taiyuan 030024 Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei Peoples R China

In the manufacturing process of electric rope shovels, an extensive array of components need to be processed. Each component is subject to a distinct sequence of operations, with the number of operations varying by part. Moreover, each of these operations needs to be processed on specific machines within specific processing durations. Therefore, the electric rope shovel production scheduling problem turns out to be challenging for general optimizers, requiring to find the optimal operation sequence, make trade-offs between multiple conflicting objectives, and satisfy a series of strict constraints. To address this production scheduling problem, this paper proposes a neo-cooperation search based evolutionary algorithm. The proposed algorithm suggests a novel encoding scheme to represent a solution (i.e., the sequence of operations of multiple components) with a real decision vector and allocates computational resources to two cooperating populations for global search and local search, respectively. The proposed algorithm can effectively balance between exploration and exploitation, and is shown to outperform state-of-the-art evolutionary algorithms in the experiments.

关键词： evolutionary computation constrained optimization sequence optimization co-evolutionary algorithms multi-obj ective optimization problems

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Toward Practical Benchmarks of Ising Machines: A Case Study on the Quadratic Knapsack Problem

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IEEE ACCESS 2024年 12卷 97678-97690页

作者： Ohno, Kentaro Shirai, Tatsuhiko Togawa, Nozomu Nippon Telegraph & Tel Corp Tokyo 1808585 Japan Waseda Univ Dept Comp Sci & Commun Engn Tokyo 1698555 Japan Waseda Univ Waseda Inst Adv Study Tokyo 1690051 Japan

Combinatorial optimization has wide applications from industry to natural science. Ising machines bring an emerging computing paradigm for efficiently solving a combinatorial optimization problem by searching a ground state of a given Ising model. Current cutting-edge Ising machines achieve fast sampling of near-optimal solutions of the max-cut problem. However, for problems with additional constraint conditions, their advantages have been hardly shown due to difficulties in handling the constraints. In this work, we focus on benchmarks of Ising machines on the quadratic knapsack problem (QKP). To bring out their practical performance, we propose fast two-stage post-processing for Ising machines, which makes handling the constraint easier. Simulation based on simulated annealing shows that the proposed method substantially improves the solving performance of Ising machines and the improvement is robust to a choice of encoding of the constraint condition. Through evaluation using an Ising machine called Amplify Annealing Engine, the proposed method is shown to dramatically improve its solving performance on the QKP. These results are a crucial step toward showing advantages of Ising machines on practical problems involving various constraint conditions.

关键词： Maintenance engineering Encoding Benchmark testing Simulated annealing Stationary state Search problems evolutionary computation Combinatorial mathematics Optimization methods Quadratic programming Combinatorial optimization Ising machine quadratic knapsack problem

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Analysis and optimization of junction between cylindrical part and end dome of filament wound pressure vessels using data driven evolutionary algorithms

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2024年第6期238卷 2348-2361页

作者： Vondracek, Dominik Padovec, Zdenek Mares, Tomas Chakraborti, Nirupam Czech Tech Univ Fac Mech Engn Dept Mech Biomech & Mechatron Prague Czech Republic Czech Tech Univ Fac Mech Engn Dept Mech Biomech & Mechatron Tech 4 Prague 16000 Czech Republic

The junction between cylindrical part and end dome is the most critical place of the filament wound composite pressure vessels because the shear forces and bending moments occur in this area. The strength analyses of junction between cylindrical part and different types of known end domes based on influence coefficients method were performed for filament wound composite pressure vessels manufactured using glass-epoxy and carbon-epoxy composites. Four known end domes were considered that is, a spherical shell, a geodesic-isotensoid shell, a shell based on minimizing of the Tsai-Hill's strength criterion and shell designed in authors' previous work (Hoffman 1 shell). Moreover, the study is also dealing with design of optimal end dome shape which takes account to the junction area. Finding of optimal end dome shape was based on data-driven evolutionary algorithms. Using input dataset from the analytical simulation surrogate models were created using evolutionary Deep Neural Nets (EvoDN2) algorithm. The multicriterial optimization procedure was performed using Constrained Reference Vector Algorithm (cRVEA) optimization algorithm. Results indicates that the optimal end dome shape highly depend on material properties.

关键词： Pressure vessels influence coefficients end dome filament winding composite material evolutionary computation genetic algorithms data-driven modeling multi-objective optimization deep learning EvoDN2 cRVEA

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Analysis of Multiobjective evolutionary Algorithms on Fitness Function With Time-Linkage Property

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IEEE TRANSACTIONS ON evolutionary computation 2024年第3期28卷 837-843页

作者： Yang, Tianyi Zhou, Yuren Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China

The time-linkage property, which means that the optimization problem not only relies on the current solution but also on historical solutions, is common in real-world applications. Although the theoretical studies on multiobjective evolutionary algorithms (MOEAs) have been rapidly developed in decades, there exists no theoretical analyses for MOEAs on time-linkage problems. This letter aims to take the first step toward rigorously analyzing MOEAs on time-linkage problems. To be specific, we constructed a multiobjective optimization problem with the time-linkage property based on the benchmark COCZ problem, called COCZ(TL) . For this problem, we proved that GSEMO cannot find the Pareto front, but nondominated sorting genetic algorithm II can find it with a probability of at least Omega(1/n) . Furthermore, we proposed an algorithm with auxiliary populations called GSEMO/Ps that is based on GSEMO. The results show that GSEMO/Ps can find the Pareto front of COCZ(TL) and the expected runtime is at most O(n(3)) . This letter provides insight into understanding the optimization behaviors of MOEAs in the multiobjective optimization problems with the time-linkage property.

关键词： Statistics Sociology evolutionary computation Runtime Optimization Benchmark testing Couplings Multiobjectives evolutionary algorithm (MOEA) theoretical analysis time-linkage property

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Automatic software tailoring for Green Internet of Things

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INTERNET OF THINGS 2025年 30卷

作者： Aragon-Jurado, Jose Miguel de la Torre, Juan Carlos Ruiz, Patricia Dorronsoro, Bernabe Univ Cadiz Sch Engn Puerto Real Spain

The proliferation of low-capacity, interconnected Internet of Things devices has increased the need for energy efficient software. Optimizing software performance for specific hardware requires tailored code transformations, as universal compiler optimizations are insufficient. Moreover, the diversity of devices and the software running on them requires automating this process. This work presents a novel combinatorial optimization problem focused on minimizing software energy consumption for specific hardware, and a methodology for solving it that accounts for system uncertainty. Additionally, a novel device for measuring the energy consumption during run time is introduced. This meter synchronizes with the experiments, enabling the automatic optimization of software as a combinatorial optimization problem. Specifically, the problem involves finding an LLVM transformation sequence that minimizes energy consumption during software execution. In our experiments, we considered two different software benchmarks and two embedded devices, using a cellular genetic algorithm to optimize them, alongside five state-of-the-art approaches to manage uncertainty. Our results demonstrate that the proposed methodology successfully overcomes uncertainty, leading to greener solutions with improvements of up to 62.29% in run time and up to 58.21% in energy consumption, outperforming the best generic compilation flags by up to 32.12% in run time and 27.84% in energy consumption.

关键词： Green software Green Internet of Things LLVM Energy consumption Uncertainty Source code transformation evolutionary computation

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Evolution-guided value iteration for optimal tracking control

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NEUROCOMPUTING 2024年 593卷

作者： Huang, Haiming Wang, Ding Zhao, Mingming Hu, Qinna Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Univ Technol Beijing Key Lab Computat Intelligence & Intelligen Beijing 100124 Peoples R China Beijing Univ Technol Beijing Inst Artificial Intelligence Beijing 100124 Peoples R China Beijing Univ Technol Beijing Lab Smart Environm Protect Beijing 100124 Peoples R China

In this article, an evolution-guided value iteration (EGVI) algorithm is established to address optimal tracking problems for nonlinear nonaffine systems. Conventional adaptive dynamic programming algorithms rely on gradient information to improve the policy, which adheres to the first order necessity condition. Nonetheless, these methods encounter limitations when gradient information is intricate or system dynamics lack differentiability. In response to this challenge, evolutionary computation is leveraged by EGVI to search for the optimal policy without requiring an action network. The competition within the policy population serves as the driving force for policy improvement. Therefore, EGVI can effectively handle complex and non-differentiable systems. Additionally, this innovative method has the potential to enhance exploration efficiency and bolster the robustness of algorithms due to its population-based characteristics. Furthermore, the convergence of the algorithm and the stability of the policy are investigated based on the EGVI framework. Finally, the effectiveness of the established method is comprehensively demonstrated through two simulation experiments.

关键词： Adaptive critic designs Adaptive dynamic programming evolutionary computation Intelligent control Optimal tracking Reinforcement learning

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A Spatial Artificial Chemistry Implementation of a Gene Regulatory Network Aimed at Generating Protein Concentration Dynamics

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ARTIFICIAL LIFE 2024年第1期30卷 65-90页

作者： Miralavy, Iliya Banzhaf, Wolfgang Michigan State Univ BEACON Ctr Study Evolut Act Dept Comp Sci & Engn E Lansing MI 48824 USA

Gene regulatory networks are networks of interactions in organisms responsible for determining the production levels of proteins and peptides. Mathematical and computational models of gene regulatory networks have been proposed, some of them rather abstract and called artificial regulatory networks. In this contribution, a spatial model for gene regulatory networks is proposed that is biologically more realistic and incorporates an artificial chemistry to realize the interaction between regulatory proteins called the transcription factors and the regulatory sites of simulated genes. The result is a system that is quite robust while able to produce complex dynamics similar to what can be observed in nature. Here an analysis of the impact of the initial states of the system on the produced dynamics is performed, showing that such models are evolvable and can be directed toward producing desired protein dynamics.

关键词： Gene regulatory network artificial chemistry artificial regulatory network modeling complex network evolutionary computation

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An Interactive Differential Evolution Method With Human Auditory Perception for Sound Composition

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IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 2024年第3期16卷 1134-1146页

作者： Wang, Yanan Pei, Yan Shindo, Hayato Liu, Qing Ren, Hai-Peng Univ Aizu Grad Sch Comp Sci & Engn Aizu Wakamatsu Fukushima 9658580 Japan Univ Aizu Comp Sci Div Aizu Wakamatsu Fukushima 9658580 Japan Xian Univ Technol Shaanxi Key Lab Complex Syst Control & Intelligent Xian 710048 Shaanxi Peoples R China

Interactive evolutionary computation (IEC) finds diverse applications in the domain of sound. However, there is a lack of methods and operations for combining sound elements into a composite sound during the crossover phase of sound composition. To this end, we propose a novel composite crossover operation using paired comparison-based interactive differential evolution. This operation integrates the target and mutant vectors into a composite sound through linear rescaling, enabling users to synthesize and assess their preferred sounds during the evaluation phase. Moreover, an optimization-stopping mechanism is incorporated to allow users to halt the process when a satisfactory sound is produced. This helps to alleviate user fatigue by eliminating unnecessary candidate sounds and improving the efficiency of the composition process by reducing redundant generations and time consumption. The efficacy of this operation was demonstrated through comparative testing and sound quality analysis. Furthermore, this article presents an original analytical approach based on four fundamental attributes of sound for analyzing human auditory preferences in composite sounds. This method combines both quantitative and qualitative paradigms, achieved through principal component analysis and human subjective auditory analysis. These discoveries have significant implications for both sound composition and the analysis of human auditory perception.

关键词： IEC Fatigue Music evolutionary computation Testing Media IEC Standards Auditory perception human-centered computing interactive differential evolution (IDE) interactive evolutionary computation (IEC) preference analysis sound composition

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Engineering Quantum Error Correction Codes Using evolutionary Algorithms

IEEE TRANSACTIONS ON QUANTUM ENGINEERING

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IEEE TRANSACTIONS ON QUANTUM ENGINEERING 2025年第1期6卷 1-14页

作者： Webster, Mark A. Browne, Dan E. UCL Dept Phys & Astron London WC1E 6BT England

Quantum error correction and the use of quantum error correction codes are likely to be essential for the realization of practical quantum computing. Because the error models of quantum devices vary widely, quantum codes that are tailored for a particular error model may have much better performance. In this work, we present a novel evolutionary algorithm that searches for an optimal stabilizer code for a given error model, number of physical qubits, and number of encoded qubits. We demonstrate an efficient representation of stabilizer codes as binary strings, which allows for random generation of valid stabilizer codes as well as mutation and crossing of codes. Our algorithm finds stabilizer codes whose distance closely matches the best-known-distance codes of Grassl (2007) for n <= 20 physical qubits. We perform a search for optimal distance Calderbank-Steane-Shor codes and compare their distance to the best known codes. Finally, we show that the algorithm can be used to optimize stabilizer codes for biased error models, demonstrating a significant improvement in the undetectable error rate for [[12,1]](2) codes versus the best-known-distance code with the same parameters. As part of this work, we also introduce an evolutionary algorithm QDistEvol for finding the distance of quantum error correction codes.

关键词： Codes Qubit Generators Vectors evolutionary computation Error correction codes computational modeling Standards Error analysis Computers evolutionary algorithms quantum error correction stabilizer codes

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