Software engineers must make decisions that trade off competing goals (faster vs. cheaper, secure vs. usable, accurate vs. interpretable, etc.). Despite MSR’s proven techniques for exploring such goals, researchers still struggle with these trade-offs. Similarly, industrial practitioners deliver sub-optimal products since they lack the tools needed to explore these trade-offs. To address this, MOOT (http://tiny.cc/moot) is a repository of many SE multi-objective optimization tasks. MOOT’s 120+ tasks cover software configuration, cloud tuning, project health, process modeling, hyperparameter optimization, and more. Sample scripts for reading MOOT and generating baseline results are available– just clone the repository and run the sample rqx.sh files (from tiny.cc/moot0). To the best of our knowledge, MOOT is the largest and most varied collection of real multi-objective optimization tasks in SE. We note that MOOT’s novelty is infrastructural, not algorithmic—we contribute curated data and research enablement, not new optimization methods. MOOT enables harder and more credible research. MOOT lets us replace studies on toy problems (or just half a dozen hand-picked examples) with case studies on 120+ examples. Such studies could focus on stability, sample efficiency, failure modes, cross-domain generality, or many other questions (see list in this document).

Modern configurable systems provide tremendous opportunities for engineering future intelligent software systems. A key difficulty thereof is how to effectively self-adapt the configuration of a running system such that its performance (e.g., runtime and throughput) can be optimized under time-varying workloads. This unfortunately remains unaddressed in existing approaches as they either overlook the available past knowledge or rely on static exploitation of past knowledge without reasoning the usefulness of information when planning for self-adaptation. In this paper, we tackle this challenging problem by proposing DLiSA, a framework that self-adapts configurable systems. DLiSA comes with two properties: firstly, it supports lifelong planning, and thereby the planning process runs continuously throughout the lifetime of the system, allowing dynamic exploitation of the accumulated knowledge for rapid adaptation. Secondly, the planning for a newly emerged workload is boosted via distilled knowledge seeding, in which the knowledge is dynamically purified such that only useful past configurations are seeded when necessary, mitigating misleading information. Extensive experiments suggest that the proposed DLiSA significantly outperforms state-of-the-art approaches, demonstrating a performance improvement of up to 229 % and a resource acceleration of up to$2.22 \times$on generating promising adaptation configurations. All data and sources can be found at our repository: https://github.com/ideas-labo/dlisa.

Multiobjective multitasking evolutionary algorithms have shown promising performance for tackling a set of multiobjective optimization tasks simultaneously, as the optimization experience gained within one task can be transferred to accelerate the solving of others. However, most studies only select similar transfer tasks based on their designed metrics, which become less efficient when tackling a large number of optimization tasks, as their transferred knowledge may be insufficiently diversified. To alleviate this issue, this article proposes a multiobjective many-tasking evolutionary algorithm (MMaTEA) using Diversified Gaussian-based knowledge Transfer, named MMaTEA-DGT. In this algorithm, a diversified transfer selection strategy is presented to choose a number of similar and complementary source tasks for knowledge transfer. Then, based on the above diversified source tasks, a Gaussian-based transfer strategy is designed to transfer their various optimization knowledge. In this way, MMaTEA-DGT is more effective in transferring optimization knowledge to speed up the solving of many tasks. Experimental studies on both the benchmark suites and a real-world dynamic vaccine prioritization problem have indicated the superiority of MMaTEA-DGT over some recently proposed MMaTEAs.

In recent years, dynamic multiobjective evolutionary algorithms (DMOEAs) using the prediction strategy have shown promising performance for solving dynamic multiobjective optimization problems (DMOPs), as they can predict environmental changing trends in advance. However, most of them follow a regular change pattern and thus their performance is compromised when solving DMOPs with irregular change patterns (e.g., nonlinear correlations). To alleviate this challenge, this article proposes a DMOEA with a learnable prediction for tackling DMOPs. Specifically, a neural network is designed to effectively capture diverse change patterns of the environment. Based on the change patterns learned, a directional improvement prediction (DIP) is developed to guide the evolutionary search toward promising directions in the decision space. In this way, a superior initial population with good convergence and diversity is predicted by DIP, which can be more effective for solving various DMOPs. Comprehensive empirical studies show that the proposed DIP is effective and the proposed algorithm has some advantages over five competitive DMOEAs when solving three commonly used benchmarks and one real-world problem.

This article suggests a new dynamic multiobjective evolutionary algorithm (DMOEA) with Knowledge Transfer and Maintenance, called KTM-DMOEA, which aims to alleviate the negative transfer and enhance the optimization efficiency. Two strategies, i.e., knowledge transfer prediction (KTP) and knowledge maintenance sampling (KMS), are proposed to excavate useful knowledge from historical environments. Particularly, KTP is a discriminative predictor designed to reduce the feature and distribution divergences across distinct environments, which classifies high-quality solutions from a large number of randomly generated solutions in new environment. Moreover, KMS is a generative predictor by modeling the distribution of elitist solutions in last environment, which can sample superior solutions in new environment according to the dynamic change trends. In this way, the advantages of KTP and KMS strategies are combined to produce a superior initial population in new environment, which help to alleviate the negative transfer and resultantly enhance the overall performance of KTM-DMOEA. When compared to several recently reported DMOEAs, the experimental results validate the advantages of KTM-DMOEA in tackling most cases of benchmark and real-world problems.