[HTML][HTML] Large-scale evolutionary optimization: A review and comparative study
Large-scale global optimization (LSGO) problems have widely appeared in various real-
world applications. However, their inherent complexity, coupled with the curse of …
world applications. However, their inherent complexity, coupled with the curse of …
A novel Q-learning based variable neighborhood iterative search algorithm for solving disassembly line scheduling problems
This paper addresses disassembly line scheduling problems (DLSP) to minimize the
smoothing index with the workstation number threshold. First, a mathematical model is …
smoothing index with the workstation number threshold. First, a mathematical model is …
Multiobjective combinatorial optimization using a single deep reinforcement learning model
This article proposes utilizing a single deep reinforcement learning model to solve
combinatorial multiobjective optimization problems. We use the well-known multiobjective …
combinatorial multiobjective optimization problems. We use the well-known multiobjective …
Surrogate-assisted evolutionary algorithm with model and infill criterion auto-configuration
Surrogate-assisted evolutionary algorithms (SAEAs) have proven to be effective in solving
computationally expensive optimization problems (EOPs). However, the performance of …
computationally expensive optimization problems (EOPs). However, the performance of …
Large language model for multi-objective evolutionary optimization
Multiobjective evolutionary algorithms (MOEAs) are major methods for solving multiobjective
optimization problems (MOPs). Many MOEAs have been proposed in the past decades, of …
optimization problems (MOPs). Many MOEAs have been proposed in the past decades, of …
Integrated scheduling problems of open shop and vehicle routing using an ensemble of group teaching optimization and simulated annealing
Production and distribution are two parts of importance in supply chains. Yet, seldom
attention is given to an integrated production and distribution in the case of open shop …
attention is given to an integrated production and distribution in the case of open shop …
The Future of Process Industry: A Cyber–Physical–Social System Perspective
The process industry is an industrial field of interdisciplinary nature involving electrical
engineering, energy, petroleum, chemical, and metallurgy, which play a key role in the …
engineering, energy, petroleum, chemical, and metallurgy, which play a key role in the …
Multiobjective optimization algorithm with dynamic operator selection for feature selection in high-dimensional classification
Feature selection (FS) is an important technique in data preprocessing that aims to reduce
the number of features for training while maintaining a high accuracy for classification. In …
the number of features for training while maintaining a high accuracy for classification. In …
One-to-one ensemble mechanism for decomposition-based multi-objective optimization
Multi-objective evolutionary algorithms based on decomposition (MOEA/Ds) have been
generally recognized as competitive techniques for solving multi-objective optimization …
generally recognized as competitive techniques for solving multi-objective optimization …
Reservoir operation management with new multi-objective (MOEPO) and metaheuristic (EPO) algorithms
Dam reservoir operation plays a fundamental role in water management studies and
planning. This study examined three policies to improve the performance of reservoirs …
planning. This study examined three policies to improve the performance of reservoirs …