Slime mould algorithm: A new method for stochastic optimization
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Summary
The proposed slime mould algorithm has several new features with a unique mathematical model that uses adaptive weights to simulate the process of producing positive and negative feedback of the propagation wave of slime mould based on bio-oscillator to form the optimal path for connecting food with excellent exploratory ability and exploitation propensity.
- Type
- article
- Published
- 2020-10-01
- Cited by
- 2,737
- References
- 97
- Access
- Open access
- OpenAlex
- https://openalex.org/W3014974411
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:216222888
Keywords
Computer science, SMA*, Slime mold, Mathematical optimization, Metaheuristic
References
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- Adaptive Response Surface Method Using Inherited Latin Hypercube Design Points
- Optimal Design of a Class of Welded Structures Using Geometric Programming
- Teaching-Learning-Based Optimization: An optimization method for continuous non-linear large scale problems
- The Ant Lion Optimizer
- Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems
- A novel heuristic optimization method: charged system search
- Handbook of Parametric and Nonparametric Statistical Procedures
- Bat algorithm for constrained optimization tasks
- Speed–accuracy trade-offs during foraging decisions in the acellular slime mould Physarum polycephalum
- Auto-tuning strategy for evolutionary algorithms: balancing between exploration and exploitation
- Slime mold inspired routing protocols for wireless sensor networks
- Optimization by Simulated Annealing
- Nonlinear Integer and Discrete Programming in Mechanical Design Optimization
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- Orthogonal learning harmonizing mutation-based fruit fly-inspired optimizers
- DUPLICATE: Advanced Orthogonal Moth Flame Optimization with Broyden–Fletcher–Goldfarb–Shanno Algorithm: Framework and Real-world Problems
- Levy-based antlion-inspired optimizers with orthogonal learning scheme
- Parameter Estimation of Induction Machine Single-Cage and Double-Cage Models Using a Hybrid Simulated Annealing–Evaporation Rate Water Cycle Algorithm
- Prediction Optimization of Cervical Hyperextension Injury: Kernel Extreme Learning Machines With Orthogonal Learning Butterfly Optimizer and Broyden- Fletcher-Goldfarb-Shanno Algorithms
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