Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

Artificial intelligence in the cyber domain: Offense and defense

Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.Web of Science123art. no. 41

A Framework for Meta-heuristic Parameter Performance Prediction Using Fitness Landscape Analysis and Machine Learning

The behaviour of an optimization algorithm when attempting to solve a problem depends on the values assigned to its control parameters. For an algorithm to obtain desirable performance, its control parameter values must be chosen based on the current problem. Despite being necessary for optimal performance, selecting appropriate control parameter values is time-consuming, computationally expensive, and challenging. As the quantity of control parameters increases, so does the time complexity associated with searching for practical values, which often overshadows addressing the problem at hand, limiting the efficiency of an algorithm. As primarily recognized by the no free lunch theorem, there is no one-size-fits-all to problem-solving; hence from understanding a problem, a tailored approach can substantially help solve it. To predict the performance of control parameter configurations in unseen environments, this thesis crafts an intelligent generalizable framework leveraging machine learning classification and quantitative characteristics about the problem in question. The proposed parameter performance classifier (PPC) framework is extensively explored by training 84 high-accuracy classifiers comprised of multiple sampling methods, fitness types, and binning strategies. Furthermore, the novel framework is utilized in constructing a new parameter-free particle swarm optimization (PSO) variant called PPC-PSO that effectively eliminates the computational cost of parameter tuning, yields competitive performance amongst other leading methodologies across 99 benchmark functions, and is highly accessible to researchers and practitioners. The success of PPC-PSO shows excellent promise for the applicability of the PPC framework in making many more robust parameter-free meta-heuristic algorithms in the future with incredible generalization capabilities

메타 휴리스틱 최적화를 이용한 기계학습 기반 선박 건조 공정 리드 타임 예측

학위논문(석사) -- 서울대학교대학원 : 공과대학 조선해양공학과, 2021.8. 하오유주.In the shipbuilding industry, each production process has a respective lead time; that is, the duration between start and finish times. Lead time is basic data that is necessary for high-efficiency production planning and systematic production management. Therefore, lead time must be accurate. However, the traditional method of lead time management is not scientific because it mostly makes the plan by calculating the average lead times derived from historical data. Therefore, to understand the complex relationship between lead time and other influencing factors, this study proposes to use machine learning (ML) algorithms, support vector machine (SVM) and artificial neural network (ANN), which are frequently applied in prediction fields. Moreover, to improve prediction accuracy, this study proposes to apply meta-heuristic algorithms to optimize the parameters of the ML models. This thesis builds hybrid models, including meta-heuristic-ANN, meta-heuristic-SVM models. In addition, this study compares model’s performance with each other. In searching for the ML model’s parameters, the results point out that the new self-organizing hierarchical particle swarm optimization (PSO) with jumping time-varying acceleration coefficients (NHPSO-JTVAC) algorithm is superior in terms of performance. More importantly, the test results demonstrate that the integrated models, based on NHPSO-JTVAC, have the smallest mean absolute percentage error (MAPE) test error in the three shipyard block process data sets, 11.79%, 16.03% and 16.45%, respectively. The results also demonstrate that the built models based on NHPSO-JTVAC can achieve further meaningful enhancements in terms of prediction accuracy. Overall, the NHPSO–JTVAC-SVM, NHPSO–JTVAC-ANN models are feasible for predicting the lead time in shipbuilding.조선 산업에서 각 공정은 리드 타임을 가진다. 리드 타임이란 공정 시작과 종료 간에 시간으로, 고효율의 생산계획과 체계적 생산관리를 위해 매우 중요한 지표이다. 특히, 생산 계획 단계에서 정확한 리드타임 예측은 납기 준수를 위한 계획 수립을 위해 매우 중요하다. 그러나 기존의 예측 방법은 과거 데이터의 평균값을 사용했기 때문에 정확도가 매우 떨어졌다. 따라서 본 연구에서는 리드 타임과 다른 영향 요인 간의 복잡한 관계를 이해하기 위해 예측 분야에서 자주 적용되는 머신 러닝 (ML) 모델인 서포트 벡터 머신 (SVM) 및 인공 신경망 (ANN) 적용을 제안한다. 또한, 기계학습 모델 예측 정확도를 향상시키기 위해 메타 휴리스틱 알고리즘을 적용하여 모델의 파라미터를 최적화하고자 한다. 본 연구는 meta-heuristics-ANN, meta-heuristics-SVM 모델을 포함하는 하이브리드 모델을 구축한다. 더불어, 본 연구는 메타 휴리스틱 알고리즘 기반으로 최적화된 기계학습 모델의 성능을 서로 비교한다. 연구 결과를 통해, ML 모델의 파라미터를 탐색하는 과정에서 particle swam optimization (PSO)의 enhanced 버전인 NHPSO-JTVAC 알고리즘이 탐색 성능 면에서 다른 알고리즘보다 우수하다는 것을 알 수 있다. 뿐만 아니라 테스트 결과를 살펴보면 NHPSO-JTVAC에 기반한 하이브리드 모델이 조선소 세 개의 블록 공정 데이터에서 (각각 11.79%, 16.03% 및 16.45%) 가장 작은 MAPE 테스트 오차임을 알 수 있다. 이것은 NHPSO-JTVAC를 기반으로 구축된 모델이 예측 정확도 측면에서 의미 있는 향상을 더 달성할 수 있음을 보여준다. 전반적으로 NHPSO-JTVAC-SVM, NHPSO-JTVAC-ANN 모델은 조선소 블록 공정의 리드 타임을 예측하는 데 적합하다는 것을 확인할 수 있다.Chapter 1 Introduction 1 1.1 Background and Motivation 1 1.2 Related Works 3 1.2.1 Related Works for Lead Time Prediction 3 1.2.2 Related Works for Hybrid Predictive Model 4 1.3 Thesis Organization 6 Chapter 2 Machine Learning 7 2.1 Support Vector Machine 7 2.1.1 Support Vector Machine Algorithm 7 2.1.2 Hyperparameter Optimization for SVM 10 2.2 Artificial Neural Network 11 2.2.1 Artificial Neural Network Algorithm 11 2.2.2 Hyperparameter Optimization for ANN 15 Chapter 3 Meta-heuristic Optimization Algorithms 17 3.1 Particle Swarm Optimization 17 3.2 NHPSO-JTVAC: An Advanced Version of PSO 18 3.3 Bat Algorithm 19 3.4 Firefly Algorithm 21 3.5 Grasshopper Optimization Algorithm 22 3.6 Moth Search Algorithm 24 Chapter 4 Hybrid Artificial Intelligence Models 27 4.1 Hybrid Meta-heuristic-SVM Models 27 4.1.1 Hybrid PSO-SVM Model 29 4.1.2 Hybrid NHPSO-JTVAC-SVM Model 30 4.1.3 Hybrid BA-SVM Model 31 4.1.4 Hybrid FA-SVM Model 33 4.1.5 Hybrid GOA-SVM Model 34 4.1.6 Hybrid MSA-SVM Model 35 4.2 Hybrid Meta-heuristic-ANN Models 36 4.2.1 Hybrid PSO-ANN Model 38 4.2.2 Hybrid NHPSO-JTVAC-ANN Model 39 4.2.3 Hybrid BA-ANN Model 40 4.2.4 Hybrid FA-ANN Model 41 4.2.5 Hybrid GOA-ANN Model 42 4.2.6 Hybrid MSA-ANN Model 43 Chapter 5 Lead Time Prediction Based on Hybrid AI Models 44 5.1 Data and Preparation 44 5.1.1 Data Normalization 45 5.1.2 Feature Selection 45 5.2 Lead Time Prediction 46 5.3 Performance Metrics 47 Chapter 6 Experimental Results 49 6.1 Results Based on Hybrid SVM-based Models 49 6.2 Results Based on Hybrid ANN-based Models 55 6.3 Overall Results 60 Chapter 7 Conclusions and Future Works 62 Bibliography 63 Appendix A 68 Abstract in Korean 69석