Overview of Swarm Intelligence Algorithms Inspired by the Animal Kingdom Part V

 

21. Artificial Fish Swarm Algorithm (AFSA)

Inspiration: Foraging and swarm behavior of fish.
Key Mechanism:

• Fish move toward high-food-density areas (fitness improvement).

• Behaviors include preying, swarming, following, and random search.

Applications:

• Data clustering

• PID controller tuning

• Traffic routing

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22. Salp Swarm Algorithm (SSA)

Inspiration: Chain-forming behavior of salps (marine organisms).
Key Mechanism:

• Leader salp guides the group, followers form chains.

• Balances exploration (chain movement) and exploitation (leader guidance).

Applications:

• Engineering design problems

• Feature selection

• Image segmentation

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23. Shark Smell Optimization (SSO)

Inspiration: Olfactory-based hunting of sharks.
Key Mechanism:

• Sharks follow scent gradients (fitness improvement).

• Combines random search with scent-driven movement.

Applications:

• Power system optimization

• Structural design

• Machine learning

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24. Moth-Flame Optimization (MFO)

Inspiration: Navigation of moths using celestial light (transverse orientation).
Key Mechanism:

• Moths spiral around light sources (solutions).

• Flames represent best solutions, guiding the swarm.

Applications:

• Clustering problems

• Neural network training

• Renewable energy systems

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25. Chicken Swarm Optimization (CSO)

Inspiration: Hierarchical behavior of chicken flocks.
Key Mechanism:

• Roosters lead, hens follow, chicks stay close to mothers.

• Mimics multi-level search strategies.

Applications:

• Combinatorial optimization

• Wireless sensor networks

• Feature selection

Summary Table of Algorithms & Best Use Cases

Algorithm	Best For	Key Advantage
Ant Colony Optimization	Routing, TSP, Scheduling	Efficient path optimization
Particle Swarm Optim.	Neural networks, Function optimization	Fast convergence
Artificial Bee Colony	Clustering, Feature selection	Good balance of exploration/exploitation
Bat Algorithm	Engineering design, Image processing	Dynamic frequency adjustment
Firefly Algorithm	Multimodal optimization	Attraction-based search
Cuckoo Search	Global optimization, ML training	Lévy flights enhance exploration
Grey Wolf Optimizer	Parameter tuning, Power systems	Social hierarchy guides search
Whale Optimization	Renewable energy, Structural design	Bubble-net hunting strategy
Cat Optimization	Feature selection, Image processing	Mimics cat hunting behavior
Wild Dog Optimizer	Multi-objective problems	Cooperative hunting strategy
Lion Optimization	Large-scale optimization	Pride-based hierarchical search
Dolphin Pod Optim.	Underwater networks, Robotics	Echolocation-based coordination
Artificial Fish Swarm	Clustering, Control systems	Simulates fish swarm behaviors
Salp Swarm Algorithm	Engineering design, Feature selection	Chain-based movement

Conclusion

Swarm intelligence algorithms cover a vast range of animal-inspired behaviors, each suited for different optimization challenges. Whether it’s ants optimizing paths, whales using bubble nets, or wild dogs hunting cooperatively, these algorithms provide robust solutions for engineering, AI, logistics, and more.