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TDSD: A New Evolutionary Algorithm Based on Triple Distinct Search Dynamics
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DOI：10.1109/ACCESS.2020.2989029.png)
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Abstract
En 中文
Spherical evolution is a recently proposed nature-inspired meta-heuristic algorithm which is proven to have nontrivial efficiency and effectiveness in solving complex optimization problems. However, it has some limitations caused by its inherent scale factor and dimension factor. Hypercube search and chaotic local search are two kinds of effective search mechanisms. To construct an algorithm which has better exploration and exploitation abilities, we propose a novel algorithm which contains triple distinct search dynamics (TDSD), i.e., spherical search, hypercube search and chaotic local search. Effective control among them enhances search performance of TDSD. It is verified on thirty CEC2017 benchmark functions and three real-world optimization problems.
Keywords:
Heuristic algorithms
Optimization
Hypercubes
Traveling salesman problems
Search problems
Evolutionary computation
Chaotic communication
Evolutionary computation
spherical evolution
hypercube search
chaotic local search
search dynamics
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