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Designing Benchmark Generator for Dynamic Optimization Algorithm
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AI
DOI：10.1109/ACCESS.2021.3138141.png)
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摘要
En 中文
Real-world problems always change over time. In the past two decades, many researchers have become increasingly interested in studying evolutionary optimization algorithms for dynamic problems owing to their great importance in real-world applications. Currently, there are no unified and mature test functions for dynamic problems, and the existing dynamic test functions are incapable of simulating a real dynamic environment. This study constructs a new benchmark test function generator for simulating dynamic environments that combine existing static benchmark functions and their related properties to form new dynamic test functions. It is simple to construct and conveniently control the dynamic properties by adjusting the corresponding parameters. Through a series of comparisons with the dynamic benchmark of the generalized dynamic benchmark generator (GDBG), the simulation results show that the dynamic test functions constructed by this generator can reflect different types of changing intensity.
Keyword:
Benchmark testing
Generators
Optimization
Heuristic algorithms
Noise measurement
Task analysis
Logistics
Dynamic environment
dynamic benchmark
dynamic optimization problems
changing intensity
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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