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作者： Stanovský, Peter Brno University of Technology

Úvodem práce přináší stručný přehled problematiky softcomputingu a řešení NP-úplných problémů. Zejména se věnuje evolučním algoritmům a jejich základní... 详细信息

Úvodem práce přináší stručný přehled problematiky softcomputingu a řešení NP-úplných problémů. Zejména se věnuje evolučním algoritmům a jejich základním typům. V další části je zpracována studie na kartézské genetické programování (CGP), které patří do oblasti evolučních algoritmů. CGP se používá zejména pro návrh číslicových obvodů, symbolickou regresi a jiné. Samostatná kapitola je věnována studii nové techniky Age layered population structure (ALPS), která se věnuje problému předčasné konvergence, přičemž navrhuje způsob rozdělení populace na subpopulace separovaných na základě věkového kriteria. Pomocí zajišťování dostatečné diverzity v populaci dosahuje významně lepších řešení oproti klasickým evolučním algoritmům. V práci jsou navrženy dva způsoby začlenění techniky ALPS do CGP. V další části je popsán postup implementace klasického CGP a jeho dvou variant s využitím techniky ALPS. V rámci práce byly vykonány testy na klasických testovacích úlohách s použitím a bez použití techniky ALPS, přičemž v části "Experimentálne výsledky" byl diskutován přínos použití techniky ALPS v CGP oproti klasickému CGP.

关键词： Evoluční algoritmus kartézské genetické programování age layered population structure kombinační obvod problém předčasné konvergence. evolution algorithm cartesian genetic programming age layered population structure digital circuit problem of pr

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An adaptive ensemble predictive strategy for multiple scale electrical energy usages forecasting

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SUSTAINABLE CITIES AND SOCIETY 2021年 66卷 102654-102654页

作者： Tian, Jing Li, Kangji Xue, Wenping Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Jiangsu Peoples R China

Electrical energy prediction plays an important role in energy management, power plant scheduling, peak demand and grid security conflict. To deal with prediction scenarios at multiple energy and time scales, an adaptive ensemble model strategy with wider applicability is proposed. The strategy consists of three parts. Firstly, the data preprocessing part is composed of expert knowledge, recursive feature elimination (RFE) and fuzzy c-means clustering (FCM). RFE and FCM methods are used for feature identification and data clustering for different energy usage patterns. Secondly, the primary prediction part consists of a model library containing five commonly used data-driven models. To improve their prediction accuracies, key model parameters are optimized by swarm evolution algorithms (EAs). An algorithm package containing three EAs is combined with the model library. The choice of algorithm for each model depends on the comparison of accuracy in specific prediction scenarios. In the third part, a linear regression model provides the final result based on primary predictors' outputs. Its weights are also optimized by EAs from the algorithm package. To verify the performance of this strategy, four case studies are carried out representing different energy and time scales' prediction scenarios. Case A is a benchmark case from the first energy prediction competition organized by American Society of Heating Refrigerating and Air-conditioning Engineer (ASHRAE). Case B perform hourly electrical load prediction of a whole building from University of Wyoming, USA. Case C is a city scale daily electrical load forecasting (Yizheng City, Jiangsu Province, China), and in Case D, national wide monthly electrical load prediction of the United States is carried out. Results of the four cases indicate that: (1) The proposed ensemble strategy performs best in all four case studies compared with five primary predictors. Compared to the best primary predictors in four case studies, t

关键词： Electrical energy prediction Ensemble learning Hybrid data-driven model evolution algorithm Recursive feature elimination Fuzzy c-means clustering Multiple scales

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High-Pressure Phases of SnO and PbO: A Density Functional Theory Combined with an evolutionary algorithm Approach

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MATERIALS 2021年第21期14卷 6552-6552页

作者： Long Truong Nguyen Makov, Guy Ben Gurion Univ Negev Fac Engn Sci Dept Mat Engn IL-84105 Beer Sheva Israel

Tin monoxide, SnO, and its analog, lead monoxide, PbO, have the same tetragonal P4/nmm structure, shaped by nonbonding dispersion forces and lone pairs. The high-pressure phases of SnO and PbO have been explored in several experimental and theoretical studies, with conflicting results. In this study, the high-pressure structures of SnO and PbO are investigated using density functional theory calculations combined with an evolutionary algorithm to identify novel high-pressure phases. We propose that the monoclinic P2(1)/m SnO and orthorhombic Pmmn PbO phases, which are metastable at 0 GPa, are a slight rearrangement of the tetragonal P4/nmm-layered structure. These orthorhombic (and their closely related monoclinic) phases become more favored than the tetragonal phase upon compression. In particular, the transition pressures to the orthorhombic gamma-phase Pmn2(1) of SnO/PbO and the monoclinic phase P2(1)/m of SnO are found to be consistent with experimental studies. Two new high-pressure SnO/PbO polymorphs are predicted: the orthorhombic Pbcm phase of SnO and the monoclinic C-2/m of PbO. These phases are stabilized in our calculations when P > 65 GPa and P > 50 GPa, respectively. The weakening of the lone pair localization and elastic instability are the main drivers of pressure-induced phase transitions. Modulations of the SnO/PbO electronic structure due to structural transitions upon compression are also discussed.

关键词： phase transition high-pressure density-functional theory evolution algorithm

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A nonlinear model and an algorithm for identifying cancer driver pathways

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APPLIED SOFT COMPUTING 2022年第0期129卷

作者： Wu, Jingli Chen, Xiaorong Li, Gaoshi Deng, Zheng Zhu, Kai Guangxi Normal Univ Guangxi Key Lab Multisource Informat Min & Secur Guilin Peoples R China Guangxi Normal Univ Coll Comp Sci & Engn Guilin Peoples R China

The identification of driver pathways has attracted considerable attention in recent years due to the high significance of driver pathways in cancer related studies. In most identification methods, the linear measurement is adopted for evaluating exclusivity, and some preset parameters are introduced to balance the contributions of different measurements. However, both the linear measurement and the preset parameters may exert negative effects on the applications of these methods. In this study, a nonlinear function is devised to measure exclusivity, and a parameter free nonlinear maximum weight submatrix (NMWS) identification model is proposed by considering coverage, exclusivity and connectivity. A competitive co-evolution algorithm (CCA) is also put forward for solving the presented NMWS model. Experimental results on both real biological data and simulated one were used compare the identification performance of the presented method with that of seven state of the art ones. The pathway detected by the presented method not only contains more genes enriched in a known signaling pathway, but also has a stronger connectivity in Protein-Protein Interaction (PPI) network. Simultaneously, the high efficiency of the presented method makes it practical in realistic applications. All of which have been confirmed through a large number of experiments. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Driver pathway Cancer PPI network Nonlinear model evolution algorithm

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An effective fruit fly optimization algorithm for the distributed permutation flowshop scheduling problem with total flowtime

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartB期123卷

作者： Guo, Heng-Wei Sang, Hong-Yan Zhang, Xu-Jin Duan, Peng Li, Jun-Qing Han, Yu-Yan Liaocheng Univ Sch Comp Sci Liaocheng Peoples R China

Distributed permutation flowshop scheduling problem (DPFSP) has always been a hot issue. The optimization goal of minimizing the total flowtime is of great significance to the environment of multi-factory. In this paper, a discrete fruit fly optimization algorithm (DFFO) is proposed to solve the DPFSP with the total flowtime criterion. In the proposed DFFO, an initialization method considering the population quality and diversity is adopted. In the smell-based search stage, three perturbation operators, the Shift-based operator, the Exchange-based operator and the Hybrid operator are designed respectively, and each fruit fly improves its state through a specific neighborhood strategy. In addition, we propose an improved reference local search (MRLS) method to enhance the exploitation ability of fruit flies. In the vision-based search stage, fruit flies use a well-designed combination update mechanism to lead fruit flies to more potential areas. In order to enhance the exploration ability, we use random reinforcement method for the population. The parameters are evaluated using an orthogonal experimental design to determine the appropriate values of the key parameters. In addition, we test the DFFO and the state-of-art algorithms from the literature on 720 large-scale instances. The experimental results show that DFFO is a very effective metaheuristic.

关键词： Fruit fly optimization algorithm Distributed scheduling Flowshop scheduling evolution algorithm Total flowtime

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Optimalizační metody pro knihovnu SIMLIB/C++

Optimalizační metody pro knihovnu SIMLIB/C++

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作者： Chlebík, Jakub Brno University of Technology

Práce se věnuje metodám optimalizace parametrů simulačních modelů. Seznamuje se základy matematické optimalizace a jejím využitím v operačním výzkumu. Dále navrhuje r... 详细信息

Práce se věnuje metodám optimalizace parametrů simulačních modelů. Seznamuje se základy matematické optimalizace a jejím využitím v operačním výzkumu. Dále navrhuje rozšíření knihovny SIMLIB/C++ modulem pro optimalizační metody. Několik vybraných metod teoreticky popisuje, implementuje v jazyce C++, demonstruje jejich použití na několika příkladech a zhodnocuje jejich úspěšnost.

关键词： Optimalizace SIMLIB/C++ simulované žíhání evoluční algoritmus metoda sdružených gradientů line search Nelder-Mead simplexová metoda optimalizace mravenčí kolonií Optimization SIMLIB/C++ simulated annealing evolution algorithm conjugate gradien

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Static Analysis and Optimization of Voltage and Reactive Power Regulation Systems in the HV/MV Substation with Electronic Transformer Tap-Changers

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ENERGIES 2022年第13期15卷 4773-4773页

作者： Korpikiewicz, Jaroslaw Mohamed-Seghir, Mostefa Gdynia Maritime Univ Fac Elect Engn Departamet Ship Automat PL-81225 Gdynia Poland

The quality of electricity is a very important indicator. The durability and reliable operation of all connected devices depend on the quality of the network voltage. Rapid changes in loads, changes in network connections and the presence of uncontrolled energy sources require the development of new voltage regulation systems. This requires voltage regulation systems capable of responding quickly to sudden voltage changes. In substations with control transformers, it is possible thanks to the use of semiconductor tap changers. Moreover, voltage regulation and reactive power compensation systems should be built as one system. This is due to the close dependence of voltage and reactive power in the network node. Therefore, it was proposed to use artificial intelligence methods to build a new voltage regulation and reactive power compensation system using all measurement voltages of network nodes. In the first stage of the research, active and reactive powers, as well as the voltage of the reference node, were selected for 6420 periods of the mains voltage. The simulation results were compared for the classic voltage regulation system with semiconductor tap changers and the evolution algorithm based on voltage measurements from the entire MV network. A significant improvement in the quality of voltage regulation with the use of an evolutionary algorithm was demonstrated. Then, a second set of input data with increased values of reactive power was generated. The results of the evolutionary algorithm after the application of the classic, independent reactive power compensation system and two-criteria optimization were compared. It has been shown that only the two-criteria optimization algorithm keeps both |tg phi| within the acceptable range and the quality of voltage regulation is the best. The article compares different working algorithms for semiconductor tap changers.

关键词： power system voltage control control tap-changer evolution algorithm multi-criteria optimization

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A Test Restoration Method based on Genetic algorithm for effective fault localization in multiple-fault programs

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JOURNAL OF SYSTEMS AND SOFTWARE 2021年 172卷 110861-110861页

作者： Yan Xiaobo Liu Bin Wang Shihai Beihang Univ Sci & Technol Reliabil & Environm Engn Lab Beijing Peoples R China Beihang Univ Sch Reliabil & Syst Engn Beijing 100191 Peoples R China

Automatic fault localization is essential for software engineering. However, fault localization suffers from the interactions among multiple faults. Our previous research revealed that the fault-coupling effect is responsible for the weakened fault localization performance in multiple-fault programs. On the basis of this finding, we propose a Test Case Restoration Method based on the Genetic algorithm (TRGA) to search potential coupling test cases and conduct a restoration process for eliminating the coupling effect. The major contributions of the current study are as follows: (1) the construction of a fitness function to measure the possibility of failed test cases becoming coupling test cases;(2) the development of a TRGA that searches potential coupling test cases;(3) and an evaluation of the TRGA efficiency across 14 open-source programs, three spectrum-based fault localizations, and two parallel debugging techniques. The results revealed the TRGA outperformed the original fault localization techniques in 74.28% and 78.57% of the scenarios in the best and worst cases, respectively. On average, the percentage improvement was 4.43% for the best case and 2% for the worst case. A detailed discussion of TRGA parameter settings is also provided. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Fault localization Software debugging Multiple-faults evolution algorithm Test restoration

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Automatically designing counterfactual regret minimization algorithms for solving imperfect-information games

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ARTIFICIAL INTELLIGENCE 2024年 337卷

作者： Li, Kai Xu, Hang Fu, Haobo Fu, Qiang Xing, Junliang Chinese Acad Sci Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Tencent AI Lab Shenzhen Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China

Strategic decision-making in imperfect-information games is an important problem in artificial intelligence. Counterfactual regret minimization (CFR), a family of iterative algorithms, has been the workhorse for solving these types of games since its inception. In recent years, a series of novel CFR variants have been proposed, significantly improving the convergence rate of vanilla CFR. However, most of these new variants are hand-designed by researchers through trial and error, often based on different motivations, which generally requires a tremendous amount of effort and insight. This work proposes AutoCFR, a systematic framework that meta-learns novel CFR algorithms through evolution, easing the burden of manual algorithm design. We first design a search language that is rich enough to represent various CFR variants. We then exploit a scalable regularized evolution algorithm with a set of acceleration techniques to efficiently search over the combinatorial space of algorithms defined by this language. The learned novel CFR algorithm can generalize to new imperfect-information games not seen during training and performs on par with or better than existing state-of-the-art CFR variants. In addition to superior empirical performance, we also theoretically show that the learned algorithm converges to an approximate Nash equilibrium. Extensive experiments across diverse imperfect-information games highlight the scalability, extensibility, and generalizability of AutoCFR, establishing it as a general-purpose framework for solving imperfect-information games.

关键词： Imperfect-information game Nash equilibrium Regret minimization evolution algorithm

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Distributed Whale Optimization algorithm based on MapReduce

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2019年第1期31卷 e4872-e4872页

作者： Khalil, Yasser Alshayeji, Mohammad Ahmad, Imtiaz Kuwait Univ Dept Comp Engn Kuwait Kuwait

Whale Optimization algorithm (WOA) is a recent swarm intelligence based meta-heuristic optimization algorithm, which simulates the natural behavior of bubble-net hunting strategy of humpback whales and has been successfully applied to solve complex optimization problems in a wide range of disciplines. However, when applied to large-size problems, its performance degrades due to the need of massive computational work load. Distributed computing is one of the effective ways to improve the scalability of WOA for solving large-scale problems. In this paper, we propose a simple and robust distributed implementation of WOA using Hadoop MapReduce named MR-WOA. MapReduce paradigm is adopted as the distribution model since it is one of the most mature technologies to develop parallel algorithms which automatically handles communication, load balancing, data locality, and fault tolerance. The design of MR-WOA is discussed in details using the MapReduce paradigm. Experiments are conducted for a set of well-known benchmarks for evaluating the quality, speedup, and scalability of MR-WOA. The conducted experiments reveal that our approach achieves a promising speedup. For some benchmarks, speedup scales linearly with increasing the number of computational nodes.

关键词： evolution algorithm Hadoop MapReduce meta-heuristic Whale Optimization algorithm (WOA)

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