Nature Inspired Algorithms and Techniques for Research
While solving the optimization problems, there are many types of solutions which can be used in different research areas.
The use of Nature Inspired Techniques and Algorithms are under research from a long time in the domain of optimization. Such techniques integrates the use of natural processes or phenomena used by the creatures, animals and other natural objects.
Nature Inspired Algorithms (NIA) can be used to have higher degree of optimization because the techniques which are given by the Nature to the earth and human being are considered as the most optimized techniques without any error rate.
These NIA approaches can be used to solve the problems for different domains of research including Computer Science, Information Technology, Cloud Computing, Grid Applications, Metallurgy, Mechanical Engineering, Production Engineering, Electrical Engineering, Power Systems, Electrical Load Balancing, Load Distribution and many others.
Following are the main Nature Inspired Algorithms which can be used for solving the research problems
- Altruism Algorithm
- Animal Migration Optimization (AMO) Algorithm
- Animals Friendship Algorithm
- Ant Colony Optimization Algorithm (ACO)
- Aritificial Chemical Process Algorithm
- Aritificial Chemical Reaction Optimization Algorithm
- Artificial Algae Algorithm (AAA)
- Artificial Bee Colony Algorithm
- Artificial Ecosystem Algorithm (AEA)
- Artificial Fish School Algorithm
- Artificial immune systems (AIS)
- Bacteria Chemotaxis (BC) Algorithm
- Bacterial Colony Optimization
- Bacterial Evolutionary Algorithm (BEA)
- Bacterial Foraging Optimization
- Bat Algorithm
- Bees Algorithm
- Biogeography-based Optimization (BBO)
- Bird Mating Optimizer
- Black Holes Algorithm
- Bull optimization algorithm
- Bumble Bees Mating Optimization (BBMO) Algorithm
- Central Force Optimization
- Collective Animal Behaviour (CAB) Algorithm
- Chemical Reaction Algorithm
- Cuckoo Search (CS)
- Cultural Algorithms (CA
- Cuttlefish Algorithm
- Cow Grazing Approach
- Differential Evolution (DE)
- Differential Search Algorithm (DS
- Eagle Strategy
- Elephant herding optimization (EHO)
- Firefly Algorithm (FA)
- Fireworks algorithm for optimization
- Flower pollination algorithm (FPA)
- Forest Optimization Algorithm
- Gases Brownian Motion Optimization
- Genetic Algorithm (GA)
- Glowworm Swarm Optimization (GSO)
- Gravitational Search Algorithm (GSA)
- Grey Wolf Optimizer
- Group Search Optimizer
- Harmony Search (HS)
- Honey-Bees Mating Optimization (HBMO) Algorithm
- Hunting Search Algorithm
- Intelligent Water Drops algorithm, or the IWD algorithm
- Invasive Weed Optimization (IWO)
- Krill Herd
- League Championship Algorithm (LCA)
- Lion Optimization Algorithm (LOA)
- Marriage in Honey Bees Optimization (MBO)
- Memetic Algorithm (MA)
- Mine blast algorithm
- Optics Inspired Optimization (OIO)
- Parliamentary optimization algorithm POA
- Particle Swarm Optimization (PSO)
- Plant Propagation Algorithm
- River Formation Dynamics
- Roach Infestation Optimization (RIO)
- Seed Based Plant Propagation Algorithm
- Self-propelled Particles
- Shuffled Frog Leaping Algorithm (SFLA)
- Simulated annealing (SA)
- Social Cognitive Optimization (SCO)
- Social Spider Optimization (SSO)
- Spider Monkey Optimization (SMO) algorithm
- Spiral Dynamic Algorithm (SDA)
- Strawberry Algorithm
- Tabu Search
- The Raven Roosting Optimization Algorithm
- Vortex Search Algorithm
- Water Cycle Algorithm
- Water Wave Optimization and many others
