Bio-inspired optimization algorithms for multi-objective problems

Orientador : Aurora Trinidad Ramirez PozoCoorientador : Roberto Santana HermidaTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa: Curitiba, 06/03/2017Inclui referências : f. 161-72Área de concentração : Computer ScienceResumo: Problemas multi-objetivo (MOPs) são caracterizados por terem duas ou mais funções objetivo a serem otimizadas simultaneamente. Nestes problemas, a meta é encontrar um conjunto de soluções não-dominadas geralmente chamado conjunto ótimo de Pareto cuja imagem no espaço de objetivos é chamada frente de Pareto. MOPs que apresentam mais de três funções objetivo a serem otimizadas são conhecidos como problemas com muitos objetivos (MaOPs) e vários estudos indicam que a capacidade de busca de algoritmos baseados em Pareto é severamente deteriorada nesses problemas. O desenvolvimento de otimizadores bio-inspirados para enfrentar MOPs e MaOPs é uma área que vem ganhando atenção na comunidade, no entanto, existem muitas oportunidades para inovar. O algoritmo de enxames de partículas multi-objetivo (MOPSO) é um dos algoritmos bio-inspirados adequados para ser modificado e melhorado, principalmente devido à sua simplicidade, flexibilidade e bons resultados. Para melhorar a capacidade de busca de MOPSOs, seguimos duas linhas de pesquisa diferentes: A primeira foca em métodos de líder e arquivamento. Trabalhos anteriores apontaram que esses componentes podem influenciar no desempenho do algoritmo, porém a seleção desses componentes pode ser dependente do problema. Uma alternativa para selecioná-los dinamicamente é empregando hiper-heurísticas. Ao combinar hiper-heurísticas e MOPSO, desenvolvemos um novo framework chamado H-MOPSO. A segunda linha de pesquisa também é baseada em trabalhos anteriores do grupo que focam em múltiplos enxames. Isso é feito selecionando como base o framework multi-enxame iterado (I-Multi), cujo procedimento de busca pode ser dividido em busca de diversidade e busca com múltiplos enxames, e a última usa agrupamento para dividir um enxame em vários sub-enxames. Para melhorar o desempenho do I-Multi, exploramos duas possibilidades: a primeira foi investigar o efeito de diferentes características do mecanismo de agrupamento do I-Multi. A segunda foi investigar alternativas para melhorar a convergência de cada sub-enxame, como hibridizá-lo com um algoritmo de estimativa de distribuição (EDA). Este trabalho com EDA aumentou nosso interesse nesta abordagem, portanto seguimos outra linha de pesquisa, investigando alternativas para criar versões multi-objetivo de um dos EDAs mais poderosos da literatura, chamado estratégia de evolução baseada na adaptação da matriz de covariância (CMA-ES). Para validar o nosso trabalho, vários estudos empíricos foram conduzidos para investigar a capacidade de busca das abordagens propostas. Em todos os estudos, nossos algoritmos investigados alcançaram resultados competitivos ou melhores do que algoritmos bem estabelecidos da literatura. Palavras-chave: multi-objetivo, algoritmo de estimativa de distribuição, otimização por enxame de partículas, multiplos enxames, híper-heuristicas.Abstract: Multi-Objective Problems (MOPs) are characterized by having two or more objective functions to be simultaneously optimized. In these problems, the goal is to find a set of non-dominated solutions usually called Pareto optimal set whose image in the objective space is called Pareto front. MOPs presenting more than three objective functions to be optimized are known as Many-Objective Problems (MaOPs) and several studies indicate that the search ability of Pareto-based algorithms is severely deteriorated in such problems. The development of bio-inspired optimizers to tackle MOPs and MaOPs is a field that has been gaining attention in the community, however there are many opportunities to innovate. Multi-objective Particle Swarm Optimization (MOPSO) is one of the bio-inspired algorithms suitable to be modified and improved, mostly due to its simplicity, flexibility and good results. To enhance the search ability of MOPSOs, we followed two different research lines: The first focus on leader and archiving methods. Previous works have pointed that these components can influence the algorithm performance, however the selection of these components can be problem-dependent. An alternative to dynamically select them is by employing hyper-heuristics. By combining hyper-heuristics and MOPSO, we developed a new framework called H-MOPSO. The second research line, is also based on previous works of the group that focus on multi-swarm. This is done by selecting as base framework the iterated multi swarm (I-Multi) algorithm, whose search procedure can be divided into diversity and multi-swarm searches, and the latter employs clustering to split a swarm into several sub-swarms. In order to improve the performance of I-Multi, we explored two possibilities: the first was to further investigate the effect of different characteristics of the clustering mechanism of I-Multi. The second was to investigate alternatives to improve the convergence of each sub-swarm, like hybridizing it to an Estimation of Distribution Algorithm (EDA). This work on EDA increased our interest in this approach, hence we followed another research line by investigating alternatives to create multi-objective versions of one of the most powerful EDAs from the literature, the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). In order to validate our work, several empirical studies were conducted to investigate the search ability of the approaches proposed. In all studies, our investigated algorithms have reached competitive or better results than well established algorithms from the literature. Keywords: multi-objective, estimation of distribution algorithms, particle swarm optimization, multi-swarm, hyper-heuristics

Learning How to Search: Generating Effective Test Cases Through Adaptive Fitness Function Selection

Search-based test generation is guided by feedback from one or more fitness functions— scoring functions that judge solution optimality. Choosing informative fitness functions is crucial to meeting the goals of a tester. Unfortunately, many goals—such as forcing the class-under-test to throw exceptions, increasing test suite diversity, and attaining Strong Mutation Coverage—do not have effective fitness function formulations. We propose that meeting such goals requires treating fitness function identification as a secondary optimization step. An adaptive algorithm that can vary the selection of fitness functions could adjust its selection throughout the generation process to maximize goal attainment, based on the current population of test suites. To test this hypothesis, we have implemented two reinforcement learning algorithms in the EvoSuite framework, and used these algorithms to dynamically set the fitness functions used during generation for the three goals identified above. We have evaluated our framework, EvoSuiteFIT, on a set of real Java faults. Evo- SuiteFIT techniques attain significant improvements for two of the three goals, and show small improvements on the third when the number of generations of evolution is fixed. For all goals, EvoSuiteFIT detects faults missed by the other techniques. The ability to adjust fitness functions allows EvoSuiteFIT to make strategic choices that efficiently produce more effective test suites, and examining its choices offers insight into how to attain our testing goals. We find that AFFS is a powerful technique to apply when an effective fitness function does not exist for a testing goal

Preference focussed many-objective evolutionary computation

Solving complex real-world problems often involves the simultaneous optimisation of multiple con icting performance criteria, these real-world problems occur in the elds of engineering, economics, chemistry, manufacturing, physics and many more. The optimisation process usually involves some design challenges in the form of the optimisation of a number of objectives and constraints. There exist many traditional optimisation methods (calculus based, random search, enumerative, etc...), however, these only o er a single solution in either adequate performance in a narrow problem domain or inadequate performance across a broad problem domain. Evolutionary Multi-objective Optimisation (EMO) algorithms are robust optimisers which are suitable for solving complex real-world multi-objective optimisation problems, as they are able to address each of the con icting objectives simultaneously. Typically, these EMO algorithms are run non-interactively with a Decision Maker (DM) setting the initial parameters of the algorithm and then analysing the results at the end of the optimisation process. When EMO is applied to real-world optimisation problems there is often a DM who is only interested in a portion of the Pareto-optimal front, however, incorporation of DM preferences is often neglected in the EMO literature. In this thesis, the incorporation of DM preferences into EMO search methods has been explored. This has been achieved through the review of EMO literature to identify a powerful method of variation, Covariance Matrix Adaptation (CMA), and its computationally infeasible EMO implementation, MO-CMA-ES. A CMA driven EMO algorithm, CMA-PAES, capable of optimisation in the presence of many objectives has been developed, benchmarked, and statistically veri ed to outperform MO-CMA-ES and MOEA/D-DRA on selected test suites. CMA-PAES and MOEA/D-DRA with the incorporation of the novel Weighted Z-score (WZ) preference articulation operator (supporting a priori, a posteriori or progressive incorporation) are then benchmarked on a range of synthetic and real-world problems. WZ-CMA-PAES is then successfully applied to a real-world problem regarding the optimisation of a classi er for concealed weapon detection, outperforming previously published classi er implementations

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

Cost-effective model-based test case generation and prioritization for software product line

In Software Product Line (SPL), testing is used to manage core assets that comprised variability and commonality in effective ways due to large sizes of products that continue to be developed. SPL testing requires a technique that is capable to manage SPL core assets. Model-based Testing (MBT) is a promising technique that offers automation and reusability in test cases generation. However, there are difficulties to ensure testing in MBT can achieve good test cases generation results based on cost (size of test suite, total execution time) and effectiveness (coverage criteria, fault detection rate) measures. This is due to lack of trade-off between cost and effectiveness in test cases generated in MBT for SPL. This study aims to increase quality of test cases based on cost and effectiveness by using generation and prioritization approaches for MBT in SPL. This study focuses on three parts to enhance quality of test cases. First, test model development based on traceability link. In order to improve test cases quality, this study focused on implementation of hybrid-based and hyper-heuristic based techniques to generate test cases. This is followed by Test Cases Prioritization (TCP) technique that is based on dissimilarity-based technique with string distance. These test cases generation and prioritization approaches are evaluated by using two benchmarks - one test object and one real object. The results are compared with other prominent approaches. The mapping approach showed 10.27% and 32.39% f-measure improvement against existing approach on e-shop object, respectively. For test cases generation using hybrid-based approach, the proposed approach outperformed existing approaches with 11.66% coverage, 17.78% average execution time, and 45.98% average size of test suite on vending machine object. The hyper-heuristic based approach NSGA-II-LHH outperformed other proposed low-level heuristic approaches with 12.00% improvement on coverage, 46.66% average execution time and 42.54% average size of test suite. Furthermore, evaluation of TCP approaches showed fault detection improvement of 21.60%, 10.40% and 12.20% and total execution time improvement of 48.00%, 22.70% and 31.80% in comparison with three existing approaches. The results revealed that proposed model transformations, test cases generation and prioritization approaches significantly improve cost and effectiveness measure in MBT for SPL

Evolutionary multi-objective optimization using neural-based estimation of distribution algorithms

Ph.DDOCTOR OF PHILOSOPH

Incorporando preferências do usuário em uma abordagem de teste de linha de produto de software baseada em otimização multiobjetivo

Orientadora: Profa. Dra. Silvia Regina VergilioDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 23/03/2018Inclui referências: p.85-91Área de concentração: Ciência da ComputaçãoResumo: Algoritmos evolutivos multi-objetivos (Evolutionary Multi-objective Algorithms - EMOAs) têm sido aplicados com sucesso para derivar um conjunto de produtos para o teste de variabilidades de Linha de Produto de Software (LPS). Esta tarefa é complexa, impactada por muitos fatores, como o número de produtos a serem testados, os critérios de cobertura a serem satisfeitos e a eficácia em revelar defeitos. Entretanto, implementações com estes algoritmos geralmente produzem muitas soluções que não são interessantes para o testador. Isso acontece porque os algoritmos de busca tradicionais não levam em consideração as preferências do usuário. Para facilitar a seleção das melhores soluções e evitar o esforço gerando soluções não interessantes, este trabalho introduz uma abordagem que aplica algoritmos multi-objetivos evolutivos baseados em preferência (Preference-Based Evolutionary Multi-objective Algorithm - PEMOAs) para resolver o problema. Dessa forma são considerados diferentes objetivos sendo o número produtos a serem testados, cobertura pairwise e escore de mutação. As preferências do testador são incorporadas antes do processo evolutivo iniciar (a priori) utilizando-se o método de ponto de referência (Reference Point - RP). São avaliados dois PEMOAs: R-NSGA-II e r-NSGA-II, usando duas formulações diferentes de objetivos e três tipos de RPs. É apresentada uma abordagem de hiper-heurística (HH) baseada em preferência para resolver esse problema. A abordagem implementa os dois PEMOAs, trabalhando com os métodos de seleção Choice Function, FRR-MAB, e um método aleatório. O objetivo da HH baseada em preferência é realizar a seleção automática de operadores genéticos, guiando a busca pela região de interesse (ROI) do testador. Os experimentos realizados mostram que os PEMOAs apresentam bons resultados, superando o algoritmo tradicional NSGA-II em relação à proximidade das soluções com o RP informado pelo testador e também ao número de soluções geradas dentro e fora da ROI. A utilização da HH baseada em preferência apresentou resultados ainda melhores, superando uma HH tradicional (NSGA-II-HH) encontrada na literatura. A utilização de HHs proporciona mais flexibilidade para o testador por permitir a seleção automática de operadores genéticos. O uso de PEMOAs reduz o esforço do testador na tarefa de selecionar o melhor conjunto de produtos para o teste de LPS. Palavras-chave: Linha de Produto de Software, Teste de Software, Algoritmo Evolutivo Multiobjetivo, Algoritmos baseados em preferência, hiper-heurística.Abstract: Evolutionary Multi-Objective Algorithms (EMOAs) have been successfully applied to derive a set of products for variability testing of Software Product Line (SPL). This task is complex, impacted by many factors such as the number of products to be tested, the coverage of some criteria to be satisfied, and the efficacy for revealing faults. However, implementations with these algorithms often produce many solutions that are not interesting to the tester. This is because traditional search algorithms do not take into account user preferences. To facilitate the selection of the best solutions and to avoid the effort generating non-interesting solutions, this work introduces an approach that applies Preference-based Evolutionary Multi-objective Algorithms (PEMOAs) to solve the problem. In this way, different objectives are considered being the number of products to be tested, pairwise coverage and mutation score. The preferences of the tester are incorporated before the evolutionary process (a priori), using the Reference Point (RP) method. Two PMOEAs are evaluated: R-NSGA-II and r-NSGA-II, using two different formulations of objectives and three types of RPs. In addition this work presents a preference-based hyper-heuristic (HH) approach to solve this problem. The approach implements both PMOEAs, working with the selection methods: Choice Function, FRR-MAB, and random. The purpose of a preference-based HH is to perform the automatic selection of genetic operators, guiding the search to a Region of Interest (ROI). The experiments show that the PMOEAs present good results, overcome the traditional NSGA-II algorithm in relation to the proximity of the solutions with RP informed by the tester and also the number of solutions generated inside and outside the ROI. Moreover, the use of the HH based on preference present even better results, better than a traditional HHs (NSGA-II-HH) found in the literature. Besides, the use of HH is more flexible and allow automatic selection of operators. The use of PEMOAs reduces tester's effort to select the best set of products for SPL testing. Keywords: Software Product Line, Software Testing, Multiobjective Evolutionary Algorithm, Preference-Based Algorithms, Hyper-Heuristic

A multi-armed bandit approach for enhancing test case prioritization in continuous integration environments

Orientador: Silvia Regina VergilioTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 10/12/2021Inclui referênciasÁrea de concentração: Ciência da ComputaçãoResumo: A Integração Contínua (do inglês Continuous Integration, CI) é uma prática comum e amplamente adotada na indústria que permite a integração frequente de mudanças de software, tornando a evolução do software mais rápida e econômica. Em ambientes que adotam CI, o Teste de Regressão (do inglês Regression Testing, RT) é fundamental para assegurar que mudanças realizadas não afetaram negativamente o comportamento do sistema. No entanto, RT é uma tarefa cara. Para reduzir os custos do RT, o uso de técnicas de priorização de casos de teste (do inglês Test Case Prioritization, TCP) desempenha um papel importante. Essas técnicas visam a identificar a ordem para os casos de teste que maximiza objetivos específicos, como a detecção antecipada de falhas. Recentemente, muitos estudos surgiram no contexto de TCP para ambientes de CI (do inglês Test Case Prioritization in Continuous Integration, TCPCI), mas poucos estudos consideram particularidades destes ambientes, tais como restrições de tempo e a volatilidade dos casos de teste, ou seja, eles não consideram o ambiente dinâmico do ciclo de vida do software no qual novos casos de teste podem ser adicionados ou removidos (descontinuados) de um ciclo para outro. A volatilidade de casos de teste está relacionada ao dilema de Exploração versus Intensificação (do inglês Exploration versus Exploitation, EvE). Para resolver este dilema uma abordagem precisa balancear: i) a diversidade do conjunto de testes; e ii) a quantidade de novos casos de teste e testes que possuem alta probabilidade de revelar defeitos. Para lidar com isso, a maioria das abordagens usa, além do histórico de falhas, outras métricas que consideram instrumentação de código ou necessitam de informações adicionais, tais como a cobertura de testes. Contudo, manter as informações atualizadas pode ser difícil e consumir tempo, e não ser escalável devido ao orçamento de teste do ambiente de CI. Neste contexto, e para lidar apropriadamente com o problema de TCPCI, este trabalho apresenta uma abordagem baseada em problemas Multi-Armed Bandit (MAB) chamada COLEMAN (Combinatorial VOlatiLE Multi-Armed BANdiT). Problemas MAB são uma classe de problemas de decisão sequencial que são intensamente estudados para resolver o dilema de EvE. O problema de TCPCI enquadra-se na categoria volátil e combinatorial, pois múltiplos braços (casos de teste) necessitam ser selecionados, e eles são adicionados ou removidos ao longos dos ciclos. COLEMAN foi avaliada em diferentes sistemas do mundo real, orçamentos de teste, funções de recompensa, e políticas MAB, em relação a diferentes abordagens da literatura, e também no contexto de Sistemas Altamente Configuráveis (do inglês Highly-Configurable Software, HCS). Diferentes indicadores de qualidade foram utilizados, englobando diferentes perspectivas tais como a eficácia da detecção de defeitos (com e sem considerar custo), rápida detecção de defeitos, redução do tempo de teste, tempo de priorização, e acurácia. Os resultados mostram que a abordagem COLEMAN é promissora e endossam sua aplicabilidade no problema de TCPCI. Em comparação com RETECS, uma abordagem do estado da arte baseada em Aprendizado por Reforço, COLEMAN apresenta uma melhor eficácia em detectar defeitos em ˜ 82% dos casos, e detecta-os mais rapidamente em 100% dos casos. COLEMAN gasta um tempo negligível, menos do que um segundo para executar, e é mais estável do que a abordagem RETECS, ou seja, melhor se adapta para lidar com os picos de defeitos. Quando comparada com uma abordagem baseada em busca, COLEMAN provê soluções próximas das ótimas em ˜ 90% dos casos, e soluções razoáveis em ˜ 92% dos casos em comparação com uma abordagem determinística. Portanto, a contribuição deste trabalho é introduzir uma abordagem eficiente e eficaz para o problema de TCPCI.Abstract: Continuous Integration (CI) is a practice commonly and widely adopted in the industry to allowfrequent integration of software changes, making software evolution faster and cost-effective. In CIenvironments, Regression Testing (RT) is fundamental to ensure that changes have not adverselyaffected existing features of the system. However, RT is an expensive task. To reduce RT costs,the use of Test Case Prioritization (TCP) techniques plays an important role. These techniquesattempt to identify the test case order that maximizes specific goals, such as early fault detection.Recently, many studies on TCP in CI environments (TCPCI) have arisen, but few pieces of workconsider CI particularities, such as the time constraint and the test case volatility, that is, they donot consider the dynamic environment of the software life-cycle in which new test cases can beadded or removed (discontinued) over time. The test case volatility is a characteristic related tothe Exploration versus Exploitation (EvE) dilemma. To solve such a dilemma an approach needsto balance: i) the diversity of the test suite; and ii) the quantity of new test cases and test casesthat are error-prone or that comprise high fault-detection capabilities. To deal with this, mostapproaches use, besides the failure-history, other measures that rely on code instrumentation orrequire additional information, such as testing coverage. However, maintaining this informationupdated can be difficult and time-consuming, not scalable due to the test budget of CI environments.In this context, and to properly deal with the TCPCI problem, this work presents an approachbased on Multi-Armed Bandit (MAB) called COLEMAN (Combinatorial VOlatiLE Multi-ArmedBANdiT). The MAB problems are a class of sequential decision problems that are intensivelystudied for solving the EvE dilemma. The TCPCI problem falls into the category of volatileand combinatorial MAB, because multiple arms (test cases) need to be selected, and they areadded or removed over the cycles. COLEMAN was evaluated under different real-world softwaresystems, time budgets, reward functions, and MAB policies, against different approaches fromthe literature, and also considering the Highly-Configurable Software context. Different qualityindicators were used to encompass different perspectives such as fault detection effectiveness (andwith cost consideration), early fault detection, test time reduction, prioritization time, and accuracy.The outcomes show that COLEMAN is promising and endorse its applicability for the TCPCIproblem. COLEMAN outperforms RETECS, a state-of-the-art approach based on ReinforcementLearning, and stands out mainly regarding fault detection effectiveness (in ~ 82% of the cases)and early fault detection (in 100%). COLEMAN spends a negligible time, less than one second toexecute, and is more stable than RETECS, that is, adapts better to deal with peak of faults. Whencompared with a search-based approach, COLEMAN provides near-optimal solutions in ~ 90% ofthe cases, and in comparison with a deterministic approach, provides reasonable solutions in 92%of the cases. Thus, the main contribution of this work is to provide an efficient and efficaciousMAB-based approach for the TCPCI problem

Performance evaluation metrics for multi-objective evolutionary algorithms in search-based software engineering: Systematic literature review

Many recent studies have shown that various multi-objective evolutionary algorithms have been widely applied in the field of search-based software engineering (SBSE) for optimal solutions. Most of them either focused on solving newly re-formulated problems or on proposing new approaches, while a number of studies performed reviews and comparative studies on the performance of proposed algorithms. To evaluate such performance, it is necessary to consider a number of performance metrics that play important roles during the evaluation and comparison of investigated algorithms based on their best-simulated results. While there are hundreds of performance metrics in the literature that can quantify in performing such tasks, there is a lack of systematic review conducted to provide evidence of using these performance metrics, particularly in the software engineering problem domain. In this paper, we aimed to review and quantify the type of performance metrics, number of objectives, and applied areas in software engineering that reported in primary studies-this will eventually lead to inspiring the SBSE community to further explore such approaches in depth. To perform this task, a formal systematic review protocol was applied for planning, searching, and extracting the desired elements from the studies. After considering all the relevant inclusion and exclusion criteria for the searching process, 105 relevant articles were identified from the targeted online databases as scientific evidence to answer the eight research questions. The preliminary results show that remarkable studies were reported without considering performance metrics for the purpose of algorithm evaluation. Based on the 27 performance metrics that were identified, hypervolume, inverted generational distance, generational distance, and hypercube-based diversity metrics appear to be widely adopted in most of the studies in software requirements engineering, software design, software project management, software testing, and software verification. Additionally, there are increasing interest in the community in re-formulating many objective problems with more than three objectives, yet, currently are dominated in re-formulating two to three objectives