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Utilizing Automatic Query Reformulations as Genetic Operations to Improve Feature Location in Software Models

IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2022年第2期48卷 713-731页

作者： Perez, Francisca Ziadi, Tewfik Cetina, Carlos Univ San Jorge SVIT Res Grp Zaragoza 50830 Spain Sorbonne Univ LiP6 F-75252 Paris France

In the combination of Model-Driven Engineering (MDE) and Search-Based Software Engineering (SBSE), genetic operations are one of the key ingredients. Our work proposes a novel adaptation of automatic query reformulations as genetic operations that leverage the latent semantics of software models (the cornerstone artefact of MDE). We analyze the impact of these reformulation operations in a real-world industrial case study of feature location in models. As baselines, we use: 1) the widespread single-point crossover plus random mutation;and 2) mask crossover plus random mutation, which is the best performer for feature location in models. We also perform a statistical analysis to provide quantitative evidence of the impact of the results and to show that this impact is significant. Our reformulation operations improve the results of the best baseline by 37.73 percent in recall and 14.08 percent in precision. These results are relevant for the task of feature location in models (one of the main activities performed during software maintenance and evolution). Furthermore, given that the only requirement to apply our approach is term availability in models, our work opens a new research direction to improve more tasks in MDE such as bug location or requirements traceability.

关键词： Genetics Adaptation models Task analysis Software Semantics evolutionary computation Analytical models Model-driven engineering search-based software engineering automatic query reformulations

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Marine predator algorithm based PD-(1+PI) controller for frequency regulation in multi-microgrid system

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IET RENEWABLE POWER GENERATION 2022年第10期16卷 2136-2151页

作者： Padhy, Sasmita Sahu, Preeti Ranjan Panda, Sidhartha Padmanaban, Sanjeevikumar Guerrero, Josep M. Khan, Baseem Natl Inst Sci & Technol Autonomous Dept Elect Engn Berhampur Odisha India Veer Surendra Sai Univ Technol Dept Elect & Elect Engn Burla Odisha India KPR Inst Engn & Technol Dept Elect & Elect Engn Arasur Tamil Nadu India Aarhus Univ Dept Business Dev & Technol CTiF Global Capsule Aarhus Denmark Aalborg Univ AAU Energy Ctr Res Microgrids CROM Aalborg Denmark Hawassa Univ Dept Elect & Comp Engn Hawassa Ethiopia

Renewable generation uncertainty, dynamic load change, and system parameter variation play a significant role in the performance degradation of non-linear multi-microgrid (MMG) systems. As a result, intelligent control becomes the need of the hour for assisting superlative attribute- based consistent electric power. The application of marine predator algorithm (MPA)-based cascaded PD-(1+PI) controller for Automatic Generation Control (AGC) of MMG system is a novel work. A maiden attempt of the MPA is proposed to optimize the parameters of the cascaded PD-(1+PI) controller using the integral time absolute error criterion. To demonstrate its superiority, the proposed algorithm is compared to the genetic algorithm, differential evolution, and grey wolf optimization. MPA then applied to conventional controller PID, cascaded PD-PI controller and proposed PD-(1+PI) controller for frequency control in multi-microgrid system. The robustness of the suggested controller is verified over PID and PD-PI controller by taking step and random load perturbation and integrating the renewable sources like solar and wind with their uncertain nature. The simulation of the investigated interconnected microgrid is carried out in MATLAB/SIMULINK environment. Finally, detailed simulation and hardware in the loop experimental results are presented to confirm the practicality of the proposed approach.

关键词： Matlab marine predator algorithm based controller superlative attribute- based consistent electric power three-term control PI control power generation control distributed power generation MMG system grey wolf optimization cascaded PD-PI controller differential evolution genetic algorithm control system synthesis intelligent control AGC automatic generation control random load perturbation renewable generation uncertainty conventional controller PID frequency regulation nonlinear multimicrogrid systems dynamic load change frequency control PD-(1+PI) controller power system interconnection evolutionary computation multimicrogrid system system parameter variation interconnected microgrid genetic algorithms

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A Pattern Mining-Based evolutionary Algorithm for Large-Scale Sparse Multiobjective Optimization Problems

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第7期52卷 6784-6797页

作者： Tian, Ye Lu, Chang Zhang, Xingyi Cheng, Fan Jin, Yaochu Anhui Univ Inst Phys Sci Minist Educ Key Lab Intelligent Comp & Signal Proc Hefei 230601 Peoples R China Anhui Univ Inst Informat Technol Minist Educ Key Lab Intelligent Comp & Signal Proc Hefei 230601 Peoples R China Anhui Univ Sch Comp Sci & Technol Minist Educ Key Lab Intelligent Comp & Signal Proc Hefei 230601 Peoples R China Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England

In real-world applications, there exist a lot of multiobjective optimization problems whose Pareto-optimal solutions are sparse, that is, most variables of these solutions are 0. Generally, many sparse multiobjective optimization problems (SMOPs) contain a large number of variables, which pose grand challenges for evolutionary algorithms to find the optimal solutions efficiently. To address the curse of dimensionality, this article proposes an evolutionary algorithm for solving large-scale SMOPs, which aims to mine the sparse distribution of the Pareto-optimal solutions and, thus, considerably reduces the search space. More specifically, the proposed algorithm suggests an evolutionary pattern mining approach to detect the maximum and minimum candidate sets of the nonzero variables in the Pareto-optimal solutions, and uses them to limit the dimensions in generating offspring solutions. For further performance enhancement, a binary crossover operator and a binary mutation operator are designed to ensure the sparsity of solutions. According to the results on eight benchmark problems and four real-world problems, the proposed algorithm is superior over existing evolutionary algorithms in solving large-scale SMOPs.

关键词： Optimization evolutionary computation Dimensionality reduction Sociology Genetics Portfolios Cybernetics evolutionary algorithm genetic operator pattern mining sparse multiobjective optimization

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Morphological Development at the evolutionary Timescale: Robotic Developmental Evolution

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ARTIFICIAL LIFE 2022年第1期28卷 3-21页

作者： Benureau, Fabien C. Y. Tani, Jun Okinawa Inst Sci & Technol Onna Japan

Evolution and development operate at different timescales;generations for the one, a lifetime for the other. These two processes, the basis of much of life on earth, interact in many non-trivial ways, but their temporal hierarchy-evolution overarching development-is observed for most multicellular life forms. When designing robots, however, this tenet lifts: It becomes-however natural-a design choice. We propose to inverse this temporal hierarchy and design a developmental process happening at the phylogenetic timescale. Over a classic evolutionary search aimed at finding good gaits for tentacle 2D robots, we add a developmental process over the robots' morphologies. Within a generation, the morphology of the robots does not change. But from one generation to the next, the morphology develops. Much like we become bigger, stronger, and heavier as we age, our robots are bigger, stronger, and heavier with each passing generation. Our robots start with baby morphologies, and a few thousand generations later, end-up with adult ones. We show that this produces better and qualitatively different gaits than an evolutionary search with only adult robots, and that it prevents premature convergence by fostering exploration. In addition, we validate our method on voxel lattice 3D robots from the literature and compare it to a recent evolutionary developmental approach. Our method is conceptually simple, and it can be effective on small or large populations of robots, and intrinsic to the robot and its morphology, not the task or environment. Furthermore, by recasting the evolutionary search as a learning process, these results can be viewed in the context of developmental learning robotics.

关键词： Development evolution learning robotics evolutionary computation

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An ACO for energy-efficient and traffic-aware virtual machine placement in cloud computing

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SWARM AND evolutionary computation 2022年第0期68卷 101012-101012页

作者： Xing, Huanlai Zhu, Jing Qu, Rong Dai, Penglin Luo, Shouxi Iqbal, Muhammad Azhar Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Room 9439Teaching Bldg 9 Chengdu 611756 Peoples R China Univ Nottingham Sch Comp Sci COL Lab Nottingham England

This paper formulates a virtual machine placement (VMP) problem, where the total power consumption of physical machines (PMs) and switches and the total network bandwidth resource consumption among VMs are jointly minimized. To address the problem, we present an energy-and traffic-aware ant colony optimization (ETA-ACO) algorithm. Three novel schemes are introduced to enhance the performance of ETA-ACO, including an energy and bandwidth-aware PM selection scheme, a traffic-based VM ordering scheme, and a direct information exchange scheme. The first scheme consists of two steps when selecting a PM to host a given VM. In the first step, PMs with lower power consumption are preserved. In the second step, the one with the lowest bandwidth resource consumption is chosen to host the VM. In the second scheme, ETA-ACO places VMs in descending order by their traffic demands. The third scheme constructs new solutions by spreading the components of the best solution over a group of constructed solutions. Simulation results demonstrate that the three novel schemes are effective in adapting ETA-ACO for the VMP problem. Besides, ETA-ACO outperforms a number of state-of-the-art heuristics and metaheuristics in terms of solution quality.

关键词： Ant colony optimization Cloud computing evolutionary computation Virtual machine placement

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A Two-Stage evolutionary Algorithm With Balanced Convergence and Diversity for Many-Objective Optimization

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第10期52卷 6222-6234页

作者： Ming, Fei Gong, Wenyin Wang, Ling China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

Multiobjective optimization evolutionary algorithms (MOEAs) have received significant achievements in recent years. However, they encounter many difficulties in dealing with many-objective optimization problems (MaOPs) due to the weak selection pressure. One possible way to improve the ability of MOEAs for these MaOPs is to balance the convergence and diversity in the high-dimensional objective space. Based on this consideration, this article proposes a novel generic two-stage (TS) framework for MaOPs. The entire evolutionary search process is divided into two stages: in the first stage, a new subregion dominance and a modified subregion density-based mating selection mainly purse the convergence and in the second stage, a novel level-based Pareto dominance cooperates with the traditional Pareto dominance that mainly promotes diversity. Integrated into NSGA-II, the TS NSGA-II, referred to as TS-NSGA-II, is proposed. To extensively evaluate the performance of our approach, 29 benchmark problems were used as the test suite. The experimental results demonstrate our approach obtained superior or competitive performance compared with eight state-of-the-art many-objective optimization evolutionary algorithms. To study its generality, the proposed TS strategy was also combined with four other advanced methods for MaOPs. The results show that it can also improve the performance of these four methods in terms of convergence and diversity.

关键词： Convergence Optimization evolutionary computation Machine-to-machine communications Linear programming Indexes Visualization Convergence diversity evolutionary algorithm many-objective optimization two-stage (TS) strategy

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Evaluation of alternative design choices for evolutionary mutation testing by means of automated configuration

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SOFTWARE QUALITY JOURNAL 2022年第1期30卷 101-126页

作者： Delgado-Perez, Pedro Chicano, Francisco Univ Cadiz Escuela Super Ingn Dept Ingn Informat Cadiz Spain Univ Malaga Escuela Tecn Super Ingn Informat Malaga Spain

Mutation testing is a well-established but costly technique to assess and improve the fault detection ability of test suites. This technique consists of introducing subtle changes in the code of a program, which are expected to be detected by the designed test cases. Among the strategies conceived to reduce its cost, evolutionary mutation testing (EMT) has been revealed as a promising approach to select a subset of the whole set of mutants based on a genetic algorithm (GA). However, like any other metaheuristic approach, EMT's execution depends on a set of parameters (both classical of GAs and context-specific ones), so different configurations can greatly vary its performance. Currently, it is difficult to clarify what are the best values for those parameters by applying manual parameter tuning and whether new design choices could improve its effectiveness with other combinations of values. The experience carried out in this paper applying iterated racing, a well-known automated configuration algorithm, reveals that EMT's performance has been undervalued in previous studies;the new configuration found by iterated racing was able to enhance EMT's results in all C++ object-oriented programs used in the experiments. This study also confirms alternative design choices as convenient options to improve EMT in this context, namely, detecting and penalizing equivalent mutants by means of Trivial Compiler Equivalence, and learning which mutation operators produced live mutants in the past generations.

关键词： Mutation testing evolutionary computation Genetic algorithm Automated configuration Iterated racing Equivalent mutants

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Genetic-based adaptive momentum estimation for predicting mortality risk factors for COVID-19 patients using deep learning

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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2022年第2期32卷 614-628页

作者： Elghamrawy, Sally M. Hassanien, Aboul Ella Vasilakos, Athanasios V. MISR Higher Inst Engn & Technol Mansoura Egypt Cairo Univ Fac Comp & Artificial Intelligence Giza Egypt Lulea Univ Technol LTU Dept Comp Sci Lulea Sweden Univ Technol Sydney UTS Dept Elect & Data Engn Sydney NSW Australia

The mortality risk factors for coronavirus disease (COVID-19) must be early predicted, especially for severe cases, to provide intensive care before they develop to critically ill immediately. This paper aims to develop an optimized convolution neural network (CNN) for predicting mortality risk factors for COVID-19 patients. The proposed model supports two types of input data clinical variables and the computed tomography (CT) scans. The features are extracted from the optimized CNN phase and then applied to the classification phase. The CNN model's hyperparameters were optimized using a proposed genetic-based adaptive momentum estimation (GB-ADAM) algorithm. The GB-ADAM algorithm employs the genetic algorithm (GA) to optimize Adam optimizer's configuration parameters, consequently improving the classification accuracy. The model is validated using three recent cohorts from New York, Mexico, and Wuhan, consisting of 3055, 7497,504 patients, respectively. The results indicated that the most significant mortality risk factors are: CD 8+ T Lymphocyte (Count), D-dimer greater than 1 Ug/ml, high values of lactate dehydrogenase (LDH), C-reactive protein (CRP), hypertension, and diabetes. Early identification of these factors would help the clinicians in providing immediate care. The results also show that the most frequent COVID-19 signs in CT scans included ground-glass opacity (GGO), followed by crazy-paving pattern, consolidations, and the number of lobes. Moreover, the experimental results show encouraging performance for the proposed model compared with different predicting models.

关键词： artificial intelligence classification algorithms deep learning evolutionary computation genetic algorithms hybrid intelligent systems medical diagnostic predictive model

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An Adaptive Metaheuristic Framework for Changing Environments 13

An Adaptive Metaheuristic Framework for Changing Environment...

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13th IEEE Congress on evolutionary computation, CEC 2024

作者： Ahmed, Bestoun S. Karlstad University Department of Mathematics and Computer Science Karlstad65188 Sweden Czech Technical University in Prague Faculty of Electrical Engineering Department of Computer Science Prague16627 Czech Republic

ISBN: (纸本)9798350308365

The rapidly changing landscapes of modern optimization problems require algorithms that can be adapted in real-time. This paper introduces an Adaptive Metaheuristic Framework (AMF) designed for dynamic environments. It is capable of intelligently adapting to changes in the problem parameters. The AMF combines a dynamic representation of problems, a real-time sensing system, and adaptive techniques to navigate continuously changing optimization environments. Through a simulated dynamic optimization problem, the AMF's capability is demonstrated to detect environmental changes and proactively adjust its search strategy. This framework utilizes a differential evolution algorithm that is improved with an adaptation module that adjusts solutions in response to detected changes. The capability of the AMF to adjust is tested through a series of iterations, demonstrating its resilience and robustness in sustaining solution quality despite the problem's development. The effectiveness of AMF is demonstrated through a series of simulations on a dynamic optimization problem. Robustness and agility characterize the algorithm's performance, as evidenced by the presented fitness evolution and solution path visualizations. The findings show that AMF is a practical solution to dynamic optimization and a major step forward in the creation of algorithms that can handle the unpredictability of real-world problems. © 2024 IEEE.

关键词： Adaptive Metaheuristics Dynamic Optimization evolutionary computation Optimization in Changing Environments Real-Time Algorithm Adaptation

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A Novel Dual-Stage Dual-Population evolutionary Algorithm for Constrained Multiobjective Optimization

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IEEE TRANSACTIONS ON evolutionary computation 2022年第5期26卷 1129-1143页

作者： Ming, Mengjun Wang, Rui Ishibuchi, Hisao Zhang, Tao Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Natl Univ Def Technol Hunan Key Lab Multienergy Syst Intelligent Interc Changsha 410073 Peoples R China Southern Univ Sci & Technol Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China

In addition to the search for feasible solutions, the utilization of informative infeasible solutions is important for solving constrained multiobjective optimization problems (CMOPs). However, most of the existing constrained multiobjective evolutionary algorithms (CMOEAs) cannot effectively explore and exploit those solutions and, therefore, exhibit poor performance when facing problems with large infeasible regions. To address the issue, this article proposes a novel method, called DD-CMOEA, which features dual stages (i.e., exploration and exploitation) and dual populations. Specifically, the two populations, called mainPop and auxPop, first individually evolve with and without considering the constraints, responsible for exploring feasible and infeasible solutions, respectively. Then, in the exploitation stage, mainPop provides information about the location of feasible regions, which facilitates auxPop to find and exploit surrounding infeasible solutions. The promising infeasible solutions obtained by auxPop in turn help mainPop converge better toward the Pareto-optimal front. Extensive experiments on three well-known test suites and a real-world case study fully demonstrate that DD-CMOEA is more competitive than five state-of-the-art CMOEAs.

关键词： Statistics Sociology Optimization Search problems evolutionary computation Convergence Switches Coevolution constrained multiobjective optimization problems (CMOPs) exploitation exploration

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