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Containerization technology, such as Docker, is gaining in popularity in newly established software-defined vehicle architectures (SDVA). However, executing those containers can quickly become computationally expensive in constrained environments, given ...

de novo drug design (dnDD) aims to generate novel molecules that meet several conflicting objectives, positioning it as a quintessential many-objective optimization problem (MaOP), where more than three objectives must be simultaneously optimized. This ...

The NSGA-II was recently proven to have difficulties in many-objective optimization. In contrast, the literature experimentally shows a good performance of the SMS-EMOA, which can be seen as a steady-state NSGA-II that uses the hypervolume contribution ...

The NSGA-II is one of the most prominent algorithms to solve multi-objective optimization problems. Despite numerous successful applications, several studies have shown that the NSGA-II is less effective for larger numbers of objectives. In this work, we ...

In this paper a novel many-objective optimization algorithm called MaEA/DC is proposed. The proposed method utilizes the random objective division (ROD) strategy to decompose solutions across various objectives as a pivotal step, thereby enhancing search ...

In this paper we propose a many-objective optimization method tailored to efficiently address challenges posed by problems with high numbers of objectives. Our method aims to generate solutions that exhibit both convergence and diversity across the ...

Lexicase and ϵ-lexicase selection are state of the art parent selection techniques for problems featuring multiple selection criteria. Originally, lexicase selection was developed for cases where these selection criteria are unlikely to be in conflict ...

Genetic Programming (GP) is a well-known technique for generating dispatching rules for scheduling problems. A simple and cost-effective local search technique for many-objective combinatorial optimization problems is Pareto Local Search (PLS). ...

Real-world problems are often comprised of many objectives and require solutions that carefully trade-off between them. Current approaches to many-objective optimization often require challenging assumptions, like knowledge of the importance/...

Spatial mode division de-multiplexing of optical signals has many real-world applications, such as quantum computing and both classical and quantum optical communication. In this context, it is crucial to develop devices able to efficiently sort ...

The increasing number of objectives poses a great challenge upon many-objective optimization algorithms (MaOOAs) when solving many-objective optimization problems (MaOOPs), since it is rather difficult to obtain well-distributed solutions with tight ...

It is a common held assumption that problems with many objectives are harder to optimize than problems with two or three objectives. In this paper, we challenge this assumption and provide empirical evidence that increasing the number of objectives ...

Pareto-optimal sets of multiobjective optimization problems with black-box and computationally expensive objective functions are generally hard to locate within a limited computational budget, and this situation gets even worse when more than three ...

Decomposition-based evolutionary algorithms have recently become fairly popular for many-objective optimization owing to their excellent selection pressure. However, existing decomposition methods are still quite sensitive to the ...

Autonomous Driving Systems (ADSs) must satisfy multiple requirements. In some cases, satisfying all of them may not be possible due to environmental conditions. Therefore, ADSs usually make tradeoffs among the requirements, resulting in one or more ...

A key idea in many-objective optimization is to approximate the optimal Pareto front using a set of representative non-dominated solutions. The produced solution set should be close to the optimal front (convergence) and well-diversified (diversity). ...

Autonomous Driving Systems (ADSs) are complex systems that must satisfy multiple requirements such as safety, compliance to traffic rules, and comfortableness. However, satisfying all these requirements may not always be possible due to emerging ...

Search-Based Software Engineering (SBSE) approaches adopt search-based techniques to solve Software Engineering (SE) optimization problems. Among these techniques, evolutionary algorithms are the most popular and successfully used, such as multi-...

Hypervolume subset selection (HSS) aims to select a subset from a candidate solution set so that the hypervolume of the selected subset is maximized. Due to its NP-hardness nature, the greedy algorithms are the most efficient for solving HSS in many-...

Graphical user interfaces (GUIs) have gained primacy among the means of interacting with computing systems, thanks to the way they leverage human perceptual and motor capabilities. However, the design of GUIs has mostly been a manual activity. To design ...