Multidiscipinary Optimization For Gas Turbines Design

State-of-the-art aeronautic Low Pressure gas Turbines (LPTs) are already characterized by high quality standards, thus they offer very narrow margins of improvement. Typical design process starts with a Concept Design (CD) phase, defined using mean-line 1D and other low-order tools, and evolves through a Preliminary Design (PD) phase, which allows the geometric definition in details. In this framework, multidisciplinary optimization is the only way to properly handle the complicated peculiarities of the design. The authors present different strategies and algorithms that have been implemented exploiting the PD phase as a real-like design benchmark to illustrate results. The purpose of this work is to describe the optimization techniques, their settings and how to implement them effectively in a multidisciplinary environment. Starting from a basic gradient method and a semi-random second order method, the authors have introduced an Artificial Bee Colony-like optimizer, a multi-objective Genetic Diversity Evolutionary Algorithm [1] and a multi-objective response surface approach based on Artificial Neural Network, parallelizing and customizing them for the gas turbine study. Moreover, speedup and improvement arrangements are embedded in different hybrid strategies with the aim at finding the best solutions for different kind of problems that arise in this field.Comment: 12 pages, 6 figures. Presented at the XXII Italian Association of Aeronautics and Astronautics Conference (2013

An Analysis on the Applicability of Meta-Heuristic Searching Techniques for Automated Test Data Generation in Automatic Programming Assessment

حظي تقييم البرمجة التلقائي (APA) بالكثير من الاهتمام بين الباحثين بشكل أساسي لدعم الدرجات الآلية ووضع علامات على المهامالمكلف بادائها الطلاب أو التدريبات بشكل منهجي. يتم تعريف APA بشكل شائع كطريقة يمكن أن تعزز الدقة والكفاءة والاتساق وكذلك تقديمملاحظات فورية لحلول للطلاب. في تحقيق APA ، تعد عملية إنشاء بيانات الاختبار مهمة للغاية وذلك لإجراء اختبار ديناميكي لمهمةالطلاب. في مجال اختبار البرمجيات ، أوضحت العديد من الأبحاث التي تركز على توليد بيانات الاختبار نجاح اعتماد تقنيات البحث الفوقية(MHST) من أجل تعزيز إجراءات استنباط بيانات الاختبار المناسبة للاختبار الفعال. ومع ذلك، فإن الأبحاث التي أجريت على APA حتىالآن لم تستغل بعد التقنيات المفيدة لتشمل تغطية اختبار جودة برنامج أفضل. لذلك ، أجرت هذه الدراسة تقييماً مقارنا لتحديد أي تقنية بحثفوقي قابلة للتطبيق لدعم كفاءة توليد بيانات الاختبار الآلي (ATDG) في تنفيذ اختبار وظيفي ديناميكي. في تقييم البرمجة التلقائي يتم تضمينالعديد من تقنيات البحث الفوقية الحديثة في التقييم المقارن الذي يجمع بين كل من خوارزميات البحث المحلية والعالمية من عام 2000 حتىعام 2018 .تشير نتيجة هذه الدراسة إلى أن تهجين Cuckoo Search مع Tabu Search و lévy flight كواحدة من طرق البحث الفوقية الواعدةليتم تطبيقها ، حيث أنه يتفوق على الطرق الفوقية الأخرى فيما يتعلق بعدد التكرارات ونطاق المدخلات.Automatic Programming Assessment (APA) has been gaining lots of attention among researchers mainly to support automated grading and marking of students’ programming assignments or exercises systematically. APA is commonly identified as a method that can enhance accuracy, efficiency and consistency as well as providing instant feedback on students’ programming solutions. In achieving APA, test data generation process is very important so as to perform a dynamic testing on students’ assignment. In software testing field, many researches that focus on test data generation have demonstrated the successful of adoption of Meta-Heuristic Search Techniques (MHST) so as to enhance the procedure of deriving adequate test data for efficient testing. Nonetheless, thus far the researches on APA have not yet usefully exploited the techniques accordingly to include a better quality program testing coverage. Therefore, this study has conducted a comparative evaluation to identify any applicable MHST to support efficient Automated Test Data Generation (ATDG) in executing a dynamic-functional testing in APA. Several recent MHST are included in the comparative evaluation combining both the local and global search algorithms ranging from the year of 2000 until 2018. Result of this study suggests that the hybridization of Cuckoo Search with Tabu Search and lévy flight as one of promising MHST to be applied, as it’s outperforms other MHST with regards to number of iterations and range of inputs

Taxonomy of Memory Usage in Swarm Intelligence-Based Metaheuristics

التجريبيات ) metaheuristics ( تحت فئة ذكاء السرب ) swarm intelligence ( اثبتت فعاليتها وأصبحت أساليب شائعة لحلمشاكل التحسين المختلفة. يمكن تصنيف التجريبيات، بناءً على استخدام الذاكرة ، الى خوارزميات مع ذاكرة وتلك بدون ذاكرة. يؤدي عدموجود ذاكرة في بعض التجريبيات إلى فقدان المعلومات التي تم الحصول عليها في التكرارات السابقة. تميل التجريبيات إلى الانحراف عنالمجالات الواعدة لمساحات البحث التي ستؤدي إلى حلول غير مثالية. تهدف هذه الورقة إلى مراجعة استخدام الذاكرة وتأثيرها على أداء أهمالتجريبيات المرتكزة على ذكاء السرب. تم إجراء التحقيق على التجريبيات المرتكزة على على ذكاء السرب ، واستخدام الذاكرة و التجريبياتبدون ذاكرة ، وخصائص الذاكرة والذاكرة في التجريبيات المرتكزة على ذكاء السرب. تم تحليل المعلومات والمراجع لاستخراج المعلوماتالأساسية وتعيينها في الأقسام الفرعية ذات الصلة. تم فحص ما مجموعه 50 مرجعًا تتعلق بدراسات استخدام الذاكرة من عام 2003 إلى عام2018 ، وتبين أن استخدام الذاكرة ضروري للغاية لزيادة فعالية التجريبيات من خلال الاستفادة من تجاربها السابقة الناجحة. لذلك تعتبرالذاكرة في التجريبيات واحدة من العناصر الأساسية الفعالة للتجريبيات المتقدمة. كما تم تسليط الضوء على مشاكل في استخدام الذاكرة. نتائجهذه المراجعة مفيدة للباحثين في تطوير تجريبيات فعالة ، من خلال الأخذ بنظر الاعتبار استخدام الذاكرة.Metaheuristics under the swarm intelligence (SI) class have proven to be efficient and have become popular methods for solving different optimization problems. Based on the usage of memory, metaheuristics can be classified into algorithms with memory and without memory (memory-less). The absence of memory in some metaheuristics will lead to the loss of the information gained in previous iterations. The metaheuristics tend to divert from promising areas of solutions search spaces which will lead to non-optimal solutions. This paper aims to review memory usage and its effect on the performance of the main SI-based metaheuristics. Investigation has been performed on SI metaheuristics, memory usage and memory-less metaheuristics, memory characteristics and memory in SI-based metaheuristics. The latest information and references have been further analyzed to extract key information and mapped into respective subsections. A total of 50 references related to memory usage studies from 2003 to 2018 have been investigated and show that the usage of memory is extremely necessary to increase effectiveness of metaheuristics by taking the advantages from their previous successful experiences. Therefore, in advanced metaheuristics, memory is considered as one of the fundamental elements of an efficient metaheuristic. Issues in memory usage have also been highlighted. The results of this review are beneficial to the researchers in developing efficient metaheuristics, by taking into consideration the usage of memory

A survey of swarm intelligence for dynamic optimization: algorithms and applications

Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given

Self-adaptive parameter and strategy based particle swarm optimization for large-scale feature selection problems with multiple classifiers

This work was partially supported by the National Natural Science Foundation of China (61403206, 61876089,61876185), the Natural Science Foundation of Jiangsu Province (BK20141005), the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (14KJB520025), the Engineering Research Center of Digital Forensics, Ministry of Education, and the Priority Academic Program Development of Jiangsu Higher Education Institutions.Peer reviewedPostprin

Adaptive multimodal continuous ant colony optimization

Seeking multiple optima simultaneously, which multimodal optimization aims at, has attracted increasing attention but remains challenging. Taking advantage of ant colony optimization algorithms in preserving high diversity, this paper intends to extend ant colony optimization algorithms to deal with multimodal optimization. First, combined with current niching methods, an adaptive multimodal continuous ant colony optimization algorithm is introduced. In this algorithm, an adaptive parameter adjustment is developed, which takes the difference among niches into consideration. Second, to accelerate convergence, a differential evolution mutation operator is alternatively utilized to build base vectors for ants to construct new solutions. Then, to enhance the exploitation, a local search scheme based on Gaussian distribution is self-adaptively performed around the seeds of niches. Together, the proposed algorithm affords a good balance between exploration and exploitation. Extensive experiments on 20 widely used benchmark multimodal functions are conducted to investigate the influence of each algorithmic component and results are compared with several state-of-the-art multimodal algorithms and winners of competitions on multimodal optimization. These comparisons demonstrate the competitive efficiency and effectiveness of the proposed algorithm, especially in dealing with complex problems with high numbers of local optima

A Survey of Evolutionary Continuous Dynamic Optimization Over Two Decades:Part B

Many real-world optimization problems are dynamic. The field of dynamic optimization deals with such problems where the search space changes over time. In this two-part paper, we present a comprehensive survey of the research in evolutionary dynamic optimization for single-objective unconstrained continuous problems over the last two decades. In Part A of this survey, we propose a new taxonomy for the components of dynamic optimization algorithms, namely, convergence detection, change detection, explicit archiving, diversity control, and population division and management. In comparison to the existing taxonomies, the proposed taxonomy covers some additional important components, such as convergence detection and computational resource allocation. Moreover, we significantly expand and improve the classifications of diversity control and multi-population methods, which are under-represented in the existing taxonomies. We then provide detailed technical descriptions and analysis of different components according to the suggested taxonomy. Part B of this survey provides an indepth analysis of the most commonly used benchmark problems, performance analysis methods, static optimization algorithms used as the optimization components in the dynamic optimization algorithms, and dynamic real-world applications. Finally, several opportunities for future work are pointed out

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms