What is he best known for?
The fields of study he is best known for:
- Mathematical optimization
- Algorithm
- Artificial intelligence
His primary scientific interests are in Mathematical optimization, Job shop scheduling, Flow shop scheduling, Algorithm and Local search.
His Mathematical optimization study integrates concerns from other disciplines, such as Robustness and Benchmark.
The concepts of his Job shop scheduling study are interwoven with issues in Artificial bee colony algorithm, Tabu search, Artificial intelligence and Search algorithm.
His Flow shop scheduling study combines topics in areas such as Simulated annealing and Multi-objective optimization.
His Algorithm research is multidisciplinary, incorporating perspectives in Evolutionary algorithm, Genetic algorithm and Pareto principle.
Ling Wang focuses mostly in the field of Local search, narrowing it down to topics relating to Crossover and, in certain cases, Probabilistic logic.
His most cited work include:
- Improved particle swarm optimization combined with chaos (723 citations)
- An effective co-evolutionary particle swarm optimization for constrained engineering design problems (652 citations)
- An Effective PSO-Based Memetic Algorithm for Flow Shop Scheduling (362 citations)
What are the main themes of his work throughout his whole career to date?
Ling Wang spends much of his time researching Mathematical optimization, Job shop scheduling, Flow shop scheduling, Local search and Algorithm.
Many of his studies on Mathematical optimization involve topics that are commonly interrelated, such as Crossover.
He has researched Job shop scheduling in several fields, including Scheduling, Critical path method and Heuristics.
His Flow shop scheduling study also includes fields such as
- Fair-share scheduling which connect with Dynamic priority scheduling,
- Premature convergence most often made with reference to Simulated annealing.
His Local search research includes elements of Multi-objective optimization, Hybrid algorithm and Benchmark.
Ling Wang combines subjects such as Genetic algorithm and Robustness with his study of Algorithm.
He most often published in these fields:
- Mathematical optimization (76.51%)
- Job shop scheduling (40.95%)
- Flow shop scheduling (27.94%)
What were the highlights of his more recent work (between 2019-2021)?
- Mathematical optimization (76.51%)
- Evolutionary algorithm (13.97%)
- Job shop scheduling (40.95%)
In recent papers he was focusing on the following fields of study:
His main research concerns Mathematical optimization, Evolutionary algorithm, Job shop scheduling, Local search and Differential evolution.
His research integrates issues of Crossover and Nonlinear system in his study of Mathematical optimization.
The various areas that Ling Wang examines in his Evolutionary algorithm study include Genetic algorithm, Particle swarm optimization, Evolutionary computation, Optimization problem and Integer programming.
His study in the field of Flow shop scheduling also crosses realms of Distributed manufacturing.
As part of the same scientific family, Ling Wang usually focuses on Flow shop scheduling, concentrating on Algorithm and intersecting with Dimension.
His Local search study combines topics from a wide range of disciplines, such as Tardiness, Heuristic and Encoding.
Between 2019 and 2021, his most popular works were:
- Behavior of crossover operators in NSGA-III for large-scale optimization problems (57 citations)
- Finding Multiple Roots of Nonlinear Equation Systems via a Repulsion-Based Adaptive Differential Evolution (40 citations)
- Hybrid Artificial Bee Colony Algorithm for a Parallel Batching Distributed Flow-Shop Problem With Deteriorating Jobs (36 citations)
In his most recent research, the most cited papers focused on:
- Mathematical optimization
- Artificial intelligence
- Algorithm
Ling Wang mostly deals with Mathematical optimization, Evolutionary algorithm, Differential evolution, Scheduling and Job shop scheduling.
Particularly relevant to Linear programming is his body of work in Mathematical optimization.
Ling Wang interconnects Evolutionary computation, Swarm intelligence and Optimization problem in the investigation of issues within Evolutionary algorithm.
His Differential evolution research incorporates elements of Ranking and Nonlinear system.
Ling Wang is involved in the study of Job shop scheduling that focuses on Flow shop scheduling in particular.
His study looks at the intersection of Flow shop scheduling and topics like Tardiness with Algorithm.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
