ESO: An enhanced snake optimizer for real-world engineering problems
L Yao, P Yuan, CY Tsai, T Zhang, Y Lu… - Expert Systems with …, 2023 - Elsevier
Meta-heuristic algorithms are an essential way to solve realistic optimization problems.
Developing effective, accurate, and stable meta-heuristic algorithms has become the goal of …
Developing effective, accurate, and stable meta-heuristic algorithms has become the goal of …
Benchmarking continuous dynamic optimization: Survey and generalized test suite
Dynamic changes are an important and inescapable aspect of many real-world optimization
problems. Designing algorithms to find and track desirable solutions while facing challenges …
problems. Designing algorithms to find and track desirable solutions while facing challenges …
[HTML][HTML] Self-organizing migrating algorithm: review, improvements and comparison
L Skanderova - Artificial Intelligence Review, 2023 - Springer
The self-organizing migrating algorithm (SOMA) is a population-based meta-heuristic that
belongs to swarm intelligence. In the last 20 years, we can observe two main streams in the …
belongs to swarm intelligence. In the last 20 years, we can observe two main streams in the …
[HTML][HTML] Meta-heuristic techniques in microgrid management: A survey
As a small energy system, microgrid plays an important role in utilizing distributed energy
resources, improving traditional energy networks, and building intelligent integrated energy …
resources, improving traditional energy networks, and building intelligent integrated energy …
Multi-population evolution based dynamic constrained multiobjective optimization under diverse changing environments
Dynamic constrained multiobjective optimization involves irregular changes in the
distribution of the true Pareto-optimal fronts, drastic changes in the feasible region caused …
distribution of the true Pareto-optimal fronts, drastic changes in the feasible region caused …
Dynamic constrained evolutionary optimization based on deep Q-network
Dynamic constrained optimization problems (DCOPs) are common and important
optimization problems in real-world, which have great difficulty to solve. Dynamic …
optimization problems in real-world, which have great difficulty to solve. Dynamic …
Adaptive multilevel prediction method for dynamic multimodal optimization
This study develops an adaptive multilevel prediction (AMLP) method to detect and track
multiple global optima over time. First, it formulates a multilevel prediction approach in which …
multiple global optima over time. First, it formulates a multilevel prediction approach in which …
[HTML][HTML] Improved nsga-iii with second-order difference random strategy for dynamic multi-objective optimization
Most real-world problems that have two or three objectives are dynamic, and the
environment of the problems may change as time goes on. For the purpose of solving …
environment of the problems may change as time goes on. For the purpose of solving …
[HTML][HTML] Evolutionary approach for dynamic constrained optimization problems
The number of research works on dynamic constrained optimization problems has been
increasing rapidly over the past two decades. In this domain, many real-life decision …
increasing rapidly over the past two decades. In this domain, many real-life decision …
Dynamic optimization in fast-changing environments via offline evolutionary search
Dynamic optimization, for which the objective functions change over time, has attracted
intensive investigations due to the inherent uncertainty associated with many real-world …
intensive investigations due to the inherent uncertainty associated with many real-world …