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 …
Evolutionary algorithm with individual-distribution search strategy and regression-classification surrogates for expensive optimization
Surrogate-assisted evolutionary algorithms (SAEAs) with prescreening model management
strategies show great potential in handling expensive optimization problems (EOPs) …
strategies show great potential in handling expensive optimization problems (EOPs) …
Reference vector-assisted adaptive model management for surrogate-assisted many-objective optimization
Acquisition functions for surrogate-assisted many-objective optimization require a delicate
balance between convergence and diversity. However, the conflicting nature between many …
balance between convergence and diversity. However, the conflicting nature between many …
Dynamic niching particle swarm optimization with an external archive-guided mechanism for multimodal multi-objective optimization
Y Sun, Y Chang, S Yang, F Wang - Information Sciences, 2024 - Elsevier
Multimodal multi-objective optimization problems (MMOPs) contain multiple equivalent
Pareto optimal sets (PSs) corresponding to the same Pareto front (PF). However …
Pareto optimal sets (PSs) corresponding to the same Pareto front (PF). However …
Pareto improver: Learning improvement heuristics for multi-objective route planning
As a research hotspot across logistics, operations research, and artificial intelligence, route
planning has become a key technology for intelligent transportation systems. Recently, data …
planning has become a key technology for intelligent transportation systems. Recently, data …
Evolutionary Multitask Optimization with Lower Confidence Bound-based Solution Selection Strategy
Evolutionary multitasking (EMT) is an emerging research direction within the evolutionary
computation community, attempting to concurrently solve multiple optimization tasks by …
computation community, attempting to concurrently solve multiple optimization tasks by …
Batch subproblem coevolution with gaussian process-driven linear models for expensive multi-objective optimization
The efficacy of surrogate-assisted multi-objective evolutionary algorithms (SAMOEAs) in
addressing expensive multi-objective optimization problems (MOPs) is contingent upon the …
addressing expensive multi-objective optimization problems (MOPs) is contingent upon the …
[HTML][HTML] A parallel technique for multi-objective Bayesian global optimization: Using a batch selection of probability of improvement
Bayesian global optimization (BGO) is an efficient surrogate-assisted technique for problems
involving expensive evaluations. A parallel technique can be used to parallelly evaluate the …
involving expensive evaluations. A parallel technique can be used to parallelly evaluate the …
Multi-objective evolutionary multi-tasking band selection algorithm for hyperspectral image classification
Hyperspectral images (HSI) contain a great number of bands, which enable better
characterization of features. However, the huge dimension and information volume brought …
characterization of features. However, the huge dimension and information volume brought …
Key Technologies of Financial Digital Industry Innovation and Green Development Driven by Information Technology
Y Li - International Journal of Computational Intelligence …, 2023 - Springer
With the development and application of Internet technology, the world has entered a new
era of the Internet economy. Information resources worldwide are no longer limited by time …
era of the Internet economy. Information resources worldwide are no longer limited by time …