Variable surrogate model-based particle swarm optimization for high-dimensional expensive problems
Many industrial applications require time-consuming and resource-intensive evaluations of
suitable solutions within very limited time frames. Therefore, many surrogate-assisted …
suitable solutions within very limited time frames. Therefore, many surrogate-assisted …
Objective space-based population generation to accelerate evolutionary algorithms for large-scale many-objective optimization
The generation and updating of solutions, eg, crossover and mutation, of many existing
evolutionary algorithms directly operate on decision variables. The operators are very time …
evolutionary algorithms directly operate on decision variables. The operators are very time …
Evolutionary optimization methods for high-dimensional expensive problems: A survey
Evolutionary computation is a rapidly evolving field and the related algorithms have been
successfully used to solve various real-world optimization problems. The past decade has …
successfully used to solve various real-world optimization problems. The past decade has …
Robotic disassembly line balancing and sequencing problem considering energy-saving and high-profit for waste household appliances
Y Zeng, Z Zhang, T Yin, H Zheng - Journal of Cleaner Production, 2022 - Elsevier
Waste household appliances contain hazardous substances and valuable components. The
timely disposal of these waste products can avoid environmental pollution and improve the …
timely disposal of these waste products can avoid environmental pollution and improve the …
Self-adaptive bat algorithm with genetic operations
Swarm intelligence in a bat algorithm (BA) provides social learning. Genetic operations for
reproducing individuals in a genetic algorithm (GA) offer global search ability in solving …
reproducing individuals in a genetic algorithm (GA) offer global search ability in solving …
A surrogate-assisted differential evolution with knowledge transfer for expensive incremental optimization problems
In some real-world applications, the optimization problems may involve multiple design
stages. At each design stage, the objective is incrementally modified by incorporating more …
stages. At each design stage, the objective is incrementally modified by incorporating more …
Distributed and expensive evolutionary constrained optimization with on-demand evaluation
Expensive optimization problems (EOPs) are common in industry and surrogate-assisted
evolutionary algorithms (SAEAs) have been developed for solving them. However, many …
evolutionary algorithms (SAEAs) have been developed for solving them. However, many …
A length-adaptive non-dominated sorting genetic algorithm for Bi-objective high-dimensional feature selection
As a crucial data preprocessing method in data mining, feature selection (FS) can be
regarded as a bi-objective optimization problem that aims to maximize classification …
regarded as a bi-objective optimization problem that aims to maximize classification …
Domain adaptation multitask optimization
Multitask optimization (MTO) is a new optimization paradigm that leverages useful
information contained in multiple tasks to help solve each other. It attracts increasing …
information contained in multiple tasks to help solve each other. It attracts increasing …
Expensive optimization via surrogate-assisted and model-free evolutionary optimization
The surrogate-assisted evolutionary algorithm (SAEA) is one of the most efficient
approaches for solving expensive optimization problems. However, it still faces challenges …
approaches for solving expensive optimization problems. However, it still faces challenges …