GOOSE Algorithm: A Powerful Optimization Tool for Real-World Engineering Challenges and Beyond

This study proposes the GOOSE algorithm as a novel metaheuristic algorithm based on the goose's behavior during rest and foraging. The goose stands on one leg and keeps his balance to guard and protect other individuals in the flock. The GOOSE algorithm is benchmarked on 19 well-known benchmark test functions, and the results are verified by a comparative study with genetic algorithm (GA), particle swarm optimization (PSO), dragonfly algorithm (DA), and fitness dependent optimizer (FDO). In addition, the proposed algorithm is tested on 10 modern benchmark functions, and the gained results are compared with three recent algorithms, such as the dragonfly algorithm, whale optimization algorithm (WOA), and salp swarm algorithm (SSA). Moreover, the GOOSE algorithm is tested on 5 classical benchmark functions, and the obtained results are evaluated with six algorithms, such as fitness dependent optimizer (FDO), FOX optimizer, butterfly optimization algorithm (BOA), whale optimization algorithm, dragonfly algorithm, and chimp optimization algorithm (ChOA). The achieved findings attest to the proposed algorithm's superior performance compared to the other algorithms that were utilized in the current study. The technique is then used to optimize Welded beam design and Economic Load Dispatch Problem, three renowned real-world engineering challenges, and the Pathological IgG Fraction in the Nervous System. The outcomes of the engineering case studies illustrate how well the suggested approach can optimize issues that arise in the real-world

Metaheuristic algorithms for damage identification in real sized structures

107 σ.Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.)Ο σκοπός αυτής της εργασίας είναι η εφαρμογή μεταευρετικών αλγορίθμων για την αναγνώριση ζημιών σε ρεαλιστικές, όσον αφορά το μέγεθος , την απόκριση των μελών και τον τρόπο προσέγγισης ιδιοτιμών, ( μια περίπτωση ενός διώροφου μεταλλικού κτιρίου εξετάζεται προσεγγίζοντας τις ιδιοσυχνότητες με τη μέθοδο των υποφορέων) κατασκευές πολιτικού μηχανικού καθώς και να επανεξετάσει τις βασικές θεωρίες και υποθέσεις. Οι δύο τεχνικές για την αναγνώριση ζημιών προτείνονται. Το πρόβλημα της αναγνώρισης ζημιών αποτελεί αντίστροφο πρόβλημα , όπου μπορεί κανείς να αναμένει πολλαπλές λύσεις. Προτείνεται , ένας αλγόριθμος διακριτών τιμών ώστε να ελέγχεται ο μέγιστος αριθμός βλαμμένων στοιχείων για την αναζήτηση . Όταν το μέγεθος ή / και ο αριθμός των ζημιών αυξάνει οι υπάρχουσες μέθοδοι ( κυρίως ευαισθησίας μεθόδων που απορρέουν από την πρώτη θεωρία διαταραχών) παράγουν περισσότερες ζημιές , από αυτές που υπετέθησαν . Μια τεχνική χρησιμοποιώντας τον χώρο του πυρήνα του πίνακα ευαισθησίας (ο οποίος θεωρείται συνάρτηση των συντελεστών ζημιάς) προτείνεται έτσι ώστε να μπορεί κανείς να παρακολουθεί τις πολλαπλές λύσεις βρίσκοντας σενάρια με λιγότερα βλαμμένα στοιχεία .The scope of this thesis is to apply metaheuristic algorithms for damage identification in realistic regarding size, member response and eigenvalue approximation (a case of a two-storey steel frame building is examined approximating the eigenvalues via substructuring) civil engineer structures as well as reviewing some of the basic theories and assumptions made. Two techniques for damage identification are proposed. The problem of damage identification is an inverse problem where one may expect multiple solutions. A discrete value algorithm is proposed in order to control the maximum number of damaged elements for the search. When size and/or number of damages increases the existing methods (mainly sensitivity methods derived from first order perturbation theory) produce more damages then the ones alleged. A technique using the null space of the sensitivity matrix (which is considered a function of the damage factors) is proposed so one can track the multiple solutions finding cases with fewer damaged elements.Σταύρος Ε. Χατζηελευθερίο

Model of human collective decision-making in complex environments

A continuous-time Markov process is proposed to analyze how a group of humans solves a complex task, consisting in the search of the optimal set of decisions on a fitness landscape. Individuals change their opinions driven by two different forces: (i) the self-interest, which pushes them to increase their own fitness values, and (ii) the social interactions, which push individuals to reduce the diversity of their opinions in order to reach consensus. Results show that the performance of the group is strongly affected by the strength of social interactions and by the level of knowledge of the individuals. Increasing the strength of social interactions improves the performance of the team. However, too strong social interactions slow down the search of the optimal solution and worsen the performance of the group. In particular, we find that the threshold value of the social interaction strength, which leads to the emergence of a superior intelligence of the group, is just the critical threshold at which the consensus among the members sets in. We also prove that a moderate level of knowledge is already enough to guarantee high performance of the group in making decisions.Comment: 12 pages, 8 figues in European Physical Journal B, 201

A phased array applicator based on open ridged-waveguide antenna for microwave hyperthermia

Radiative hyperthermia is a clinically applied cancer treatment modality where antenna design is crucial to achieving therapeutic goals. Serving as the building block of a phased-array configuration, antennas are typically arranged in a cylindrical or elliptical array called applicator. This short communication proposes an elliptical phased array applicator based on a compact, UWB design from the category of double-ridged horn antennas customized for hyperthermia systems. The performance of the antenna, named open ridged-waveguide, has been experimentally assessed based on the quality metrics of the hyperthermic community. The proposed design achieves an ultra-wideband range of operation from 400 to 800 MHz with an aperture size of 3 by 4 cm. Moreover, thanks to the shielding provided by the metallic housing, the design proves good isolation better than -30 dB throughout the band. The power deposition capability of the proposed applicator followed by the thermal analysis is also investigated for a realistic headand neck patient model. The results indicate very good quality metrics achieved in the treatment planning of the patient

Leo: Lagrange Elementary Optimization

Global optimization problems are frequently solved using the practical and efficient method of evolutionary sophistication. But as the original problem becomes more complex, so does its efficacy and expandability. Thus, the purpose of this research is to introduce the Lagrange Elementary Optimization (Leo) as an evolutionary method, which is self-adaptive inspired by the remarkable accuracy of vaccinations using the albumin quotient of human blood. They develop intelligent agents using their fitness function value after gene crossing. These genes direct the search agents during both exploration and exploitation. The main objective of the Leo algorithm is presented in this paper along with the inspiration and motivation for the concept. To demonstrate its precision, the proposed algorithm is validated against a variety of test functions, including 19 traditional benchmark functions and the CECC06 2019 test functions. The results of Leo for 19 classic benchmark test functions are evaluated against DA, PSO, and GA separately, and then two other recent algorithms such as FDO and LPB are also included in the evaluation. In addition, the Leo is tested by ten functions on CECC06 2019 with DA, WOA, SSA, FDO, LPB, and FOX algorithms distinctly. The cumulative outcomes demonstrate Leo's capacity to increase the starting population and move toward the global optimum. Different standard measurements are used to verify and prove the stability of Leo in both the exploration and exploitation phases. Moreover, Statistical analysis supports the findings results of the proposed research. Finally, novel applications in the real world are introduced to demonstrate the practicality of Leo.Comment: 28 page

A new interaction potential for swarming models

We consider a self-propelled particle system which has been used to describe certain types of collective motion of animals, such as fish schools and bird flocks. Interactions between particles are specified by means of a pairwise potential, repulsive at short ranges and attractive at longer ranges. The exponentially decaying Morse potential is a typical choice, and is known to reproduce certain types of collective motion observed in nature, particularly aligned flocks and rotating mills. We introduce a class of interaction potentials, that we call Quasi-Morse, for which flock and rotating mills states are also observed numerically, however in that case the corresponding macroscopic equations allow for explicit solutions in terms of special functions, with coefficients that can be obtained numerically without solving the particle evolution. We compare thus obtained solutions with long-time dynamics of the particle systems and find a close agreement for several types of flock and mill solutions.Comment: 23 pages, 8 figure

합성곱 신경망 기반 프록시 모델을 이용한 유정배치 최적화

학위논문(석사) -- 서울대학교대학원 : 공과대학 에너지시스템공학부, 2022.2. 최종근.Well placement is important for economic development of an oil field. It is affected by various decision variables like the number of wells, well types, and its location. Therefore, optimization algorithms are conventionally used with a reservoir simulation for decision making. However, it requires a large amount of computation time. In this regard, researches on proxy models to replace a reservoir simulation are actively conducted in these days. Optimization with a proxy model has an ability to provide desirable solutions. However, it is not always successful due to limited training data and inaccuracy of a proxy model. Although re-training of a proxy model throughout an optimization process is suggested for enhancing its accuracy, the re-training procedure degrade the advantage of the proxy model in time efficiency. At the same time, it is difficult to figure out the proper number and time of re-training. Therefore, in this research, an initial sampling scheme, which is effective for a proxy model training, is proposed to make initial samples to include optimal solutions. Uniform sampling method is firstly tried to spread wells evenly over a whole reservoir area. This allows to obtain overall dynamic data that reflects the behavior of the reservoir for the proxy model training. As a result, the proxy model shows improved accuracy in predicting net present value (NPV) of the optimized solution in general by including optimal solutions. However, the accuracy tends to decrease in higher NPV values. Next, 2-stage sampling method is suggested to overcome the limitation of the uniform sampling. It is tried to enhance the performance of the proxy model by more sampling of high NPV value areas. This method includes the optimal solutions successfully by extracting samples over two times based on well configuration probability of samples in the range of high NPV. Therefore, it is proposed as an adequate initial sampling scheme as it significantly improves the prediction accuracy of the proxy model. Validation for the proposed method is conducted with different reservoir models. Three trials of the optimization are attempted for each reservoir model. As the average of the coefficients of determination are all higher than 0.9, the stability of the 2-stage sampling method is demonstrated. Therefore, it is concluded that the proxy model with the 2-stage sampling method is reliable for replacing the reservoir simulation.유정의 배치는 사업의 경제성을 좌우하므로 그 개수와 종류, 위치 등을 최적화 해야한다. 유정배치 최적화에는 전통적으로 최적화 알고리즘이 저류층 시뮬레이션과 결합하여 사용된다. 그러나 이는 많은 계산을 필요로 하기 때문에 저류층 시뮬레이션을 대체하기 위한 프록시 모델에 관한 연구가 최근에 활발히 수행되고 있다. 저류층 시뮬레이션 대신 사용되는 프록시 모델은 학습 자료와 프록시 모델의 한계로 인해 그 정확성이 떨어질 수 있다. 프록시 모델의 정확성을 향상시키기 위해 최적화 도중 재학습을 사용하는 방법이 알려져 있긴 하지만, 이는 계산 시간이 빠르다는 프록시 모델의 강점을 둔화시킨다. 뿐만 아니라, 적절한 재학습의 횟수 및 시기를 결정하는 근거가 불분명하여 이를 결정하는 것이 어렵다는 한계점이 있다. 따라서 본 연구에서는 프록시 모델의 학습에 사용되는 초기 샘플이 최적해를 포함할 수 있도록 하는 효과적인 초기 샘플링 기법을 제안하였다. 먼저, 저류층의 전 영역에 걸쳐 유정을 배치시키는 균일 샘플링 기법을 시도하였다. 이는 프록시 모델이 저류층의 전반적인 거동을 반영할 수 있게 하였고 따라서 순현재가치의 예측 정확도가 상승하였다. 그러나 높은 순현재가치를 갖는 구간에서의 예측 정확도는 타구간에 비해 다소 낮았다. 균일 샘플링의 한계를 보완하기 위해 2단계 샘플링 기법이 시도되었다. 높은 순현재가치를 갖는 샘플의 유정 개수 분포와 유정이 각 격자에 배치될 확률을 분석하여 이를 기반으로 2단계로 샘플을 추출하였다. 제안된 2단계 샘플링은 최적해를 효과적으로 포함시켰고 프록시 모델의 정확도 역시 향상시켰다. 제안 기법의 안정성을 보기 위해 2개의 저류층 모델에 대해 추가적인 검증이 수행되었다. 각 모델에 총 3번씩의 최적화를 수행하여 검증을 시도하였고 평균 결정계수가 모두 0.9 이상으로 높은 결과를 보이며 2단계 샘플링의 안정성이 확인되었다.Abstract i Table of Contents iii List of Tables iv List of Figures v Chapter 1. Introduction 1 1.1 Importance of well placement 1 1.2 Well placement optimization 3 Chapter 2. Theoretical Backgrounds 9 2.1 Proxy model 9 2.2 Time of flight 11 2.3 Convolutional neural network 14 2.4 Particle swarm optimization 19 Chapter 3. Methodology 22 3.1 Sampling methods 22 3.2 Model construction 37 3.3 Optimization with proxy models 43 Chapter 4. Results 46 4.1 Results of the uniform sampling 46 4.2 Results of the 2-stage sampling 53 Chapter 5. Conclusions 59 Bibliography 62 국문초록 66석

Development of an Oxy-Fuel Combustion System in a Compression-Ignition Engine for Ultra-Low Emissions Powerplants Using CFD and Evolutionary Algorithms

[EN] This study uses an optimization approach for developing a combustion system in a compression-ignition engine that is able to operate under oxy-fuel conditions, and produces mainly CO2 and H2O as exhaust gases. This is achieved because the combustion concept uses pure oxygen as an oxidizer, instead of air, avoiding the presence of nitrogen. The O-2 for the combustion system can be obtained by using a mixed ionic-electronic conducting membrane (MIEC), which separates the oxygen from the air onboard. The optimization method employed maximizes the energy conversion of the system, reducing pollutant emissions (CxHy, particulate matter, and carbon monoxides) to levels near zero. The methodology follows a novel approach that couples computational fluid dynamics (CFD) and particle swarm optimization (PSO) algorithms to optimize the complete combustion system in terms of engine performance and pollutant generation. The study involves the evaluation of several inputs that govern the combustion system design in order to fulfill the thermo-mechanical constraints. The parameters analyzed are the piston bowl geometry, fuel injector characteristics, air motion, and engine settings variables. Results evince the relevance of the optimization procedure, achieving very low levels of gaseous pollutants (CxHy and CO) in the optimum configuration. The emissions of CO were reduced by more than 10% while maintaining the maximum in-cylinder pressure within the limit imposed for the engine. However, indicated efficiency levels are compromised if they are compared with an equivalent condition operating under conventional diesel combustion.This research work has been supported by Grant PDC2021-120821-I00 funded by MCIN/AEI/10.13039/501100011033 and by EuropeanUnion NextGenerationEU/PRTR. This research was partially supported by Agencia Valenciana de la Innovacio (AVI) through the project "Demostrador de un motor de oxicombustion con captura de CO2" (INNVA1/2021/38).Serrano, J.; Bracho Leon, G.; Gómez-Soriano, J.; Spohr-Fernandes, C. (2022). Development of an Oxy-Fuel Combustion System in a Compression-Ignition Engine for Ultra-Low Emissions Powerplants Using CFD and Evolutionary Algorithms. Applied Sciences. 12(14):1-27. https://doi.org/10.3390/app12147104127121