Evolutionary multi-mode slime mold optimization: a hyper-heuristic algorithm inspired by slime mold foraging behaviors

This paper proposes a novel hyper-heuristic algorithm termed evolutionary multi-mode slime mold optimization (EMSMO) for addressing continuous optimization problems. The architecture of a typical hyper-heuristic algorithm comprises two main components: the high-level component and the low-level comp...

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Vydané v:The Journal of supercomputing Ročník 80; číslo 9; s. 12186 - 12217
Hlavní autori: Zhong, Rui, Zhang, Enzhi, Munetomo, Masaharu
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.06.2024
Springer Nature B.V
Predmet:
Brain research
Compilers
Computer Science
Evolutionary algorithms
Experiments
Food
Foraging behavior
Genetic algorithms
Heuristic
Heuristic methods
Interpreters
Mathematical models
Mold
Nutrition
Optimization
Performance evaluation
Processor Architectures
Programming Languages
Scheduling
Search methods
Slime
Statistical analysis
Improvement-based probabilistic selection function
Hyper-heuristics
Continuous optimization
Bio-inspired optimization algorithm
ISSN:0920-8542, 1573-0484
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Shrnutí:This paper proposes a novel hyper-heuristic algorithm termed evolutionary multi-mode slime mold optimization (EMSMO) for addressing continuous optimization problems. The architecture of a typical hyper-heuristic algorithm comprises two main components: the high-level component and the low-level component. The low-level component contains a set of low-level heuristics (LLHs) and intrinsic problem attributes, while the high-level component manipulates the LLHs to construct the sequence of heuristics. Inspired by the foraging behaviors of slime mold, we designed four easy-implemented search strategies including the search for food, approach food, wrap food, and re-initialization as the LLHs for the low-level component. In the high-level component, we adopt an improvement-based probabilistic selection function that contains two metrics: (1) the probability of improvement and (2) the normalized improvement. The selection function cooperates with the roulette wheel strategy to construct the optimization sequence. To evaluate the performance of our proposal, we implement comprehensive numerical experiments on CEC2013 benchmark functions and three engineering optimization problems. Six classic or advanced evolutionary algorithms and three hyper-heuristic algorithms are applied as competitor algorithms to evaluate the competitiveness of EMSMO. Experimental and statistical results show that EMSMO has broad prospects for solving continuous optimization problems.
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ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-024-05909-0