SAEA, mainly including regression-based surrogate-assisted evolutionary algorithms (SAEAs) and classification-based SAEAs, are promising for solving expensive multiobjective optimization problems (EMOPs). Regression-based SAEAs usually use complex ...

This work concerns evolutionary approaches to black-box noisy optimization where the problem is accessed via noisy function evaluations. An evolution strategy (ES) algorithm is proposed, which uses a Gaussian distribution to guide the search and requires ...

Data-driven evolutionary algorithms (DDEAs) have received increasing attention during the past decade, but most existing studies are dedicated to solving relatively small-scale problems. For large-scale optimization problems (LSOPs), special efforts must ...

Multiobjective optimization problems give rise to a set of Pareto-optimal (PO) solutions, each of which makes a tradeoff among the objectives. When multiple PO solutions are to be implemented for different applications as platform-based solutions, a ...

A variable reduction strategy (VRS) drives evolutionary algorithms (EAs) to evolve more effectively by simplifying optimization problems. To represent a decision space with the smallest set of variables via VRS, a variable reduction optimization problem (...

Evolutionary multitask optimization employs similarities among tasks via evolutionary algorithms (EAs) with knowledge transfer techniques to address multiple optimization tasks simultaneously. Although existing knowledge transfer techniques achieved ...

Air traffic flow management (ATFM) is the key driver of efficient aviation. It aims at balancing traffic demand against airspace capacity by scheduling aircraft, which is critical for air navigation service providers in delivering secure and sustainable ...

Cooperative coevolutionary algorithms are popular to solve large-scale dynamic optimization problems via divide-and-conquer mechanisms. Their performance depends on how decision variables are grouped and how changing optima are tracked. However, existing ...

By decomposing the problem into a series of low-dimensional subproblems, cooperative coevolution is an effective method for large-scale optimization problems. This work reveals that when constraints are introduced in decomposition-based methods, the ...

This article proposes a machine learning (ML)-based unified innovized progress (UIP) operator to simultaneously enhance the convergence and diversity capabilities of reference vector-based evolutionary multi- and many-objective optimization algorithms, ...

Some quality indicators have been proposed for benchmarking preference-based evolutionary multiobjective optimization (EMO) algorithms using a reference point. Although a systematic review and analysis of the quality indicators are helpful for both ...

Multitask genetic programming (GP) methods have been applied to various domains, such as classification, regression, and combinatorial optimization problems. Most existing multitask GP methods are designed based on tree-based structures, which are not ...

Evolutionary multitasking (EMT) has attracted much attention in the community of evolutionary computation recently. It intends to improve the performance of evolutionary optimization on multiple problems via knowledge learning and transfer across them ...

The nondominated sorting genetic algorithm II (NSGA-II) is one of the most prominent algorithms to solve multiobjective optimization problems. Despite numerous successful applications, several studies have shown that the NSGA-II is less effective for ...

Owing to the stochasticity of evolutionary multiobjective optimization (EMO) algorithms and an application with a limited budget of solution evaluations, a perfectly converged and uniformly distributed Pareto-optimal (PO) front cannot be always ...

Dynamic constrained multiobjective optimization problems (DCMOPs) abound in real-world applications and gain increasing attention in the evolutionary computation community. To evaluate the capability of an algorithm in solving dynamic constrained ...

Handling energy resource management energy resources management (ERM) in today’s energy systems is complex and challenging due to uncertainties arising from the high penetration of distributed energy resources. Such penetration introduces various ...

Many real-world problems are computationally costly and the objective functions evolve over time. Data-driven, a.k.a. surrogate-assisted, evolutionary optimization has been recognized as an effective approach to tackle expensive black-box optimization ...

Recent years have witnessed the repaid progress in developing and applying multiobjective evolutionary algorithms (MOEAs). However, as a major component of an MOEA, the offspring generator has been largely overlooked, lacking a principle to design ...

Robust optimization over time (ROOT) is the combination of robust optimization and dynamic optimization. In ROOT, frequent changes to deployed solutions are undesirable, which can be due to the high cost of switching between deployed solutions, ...