1000/1000
Hot
Most Recent
The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace.
Algorithm Name | The Main Properties of the Algorithm | Ref. |
---|---|---|
Divide and Conquer Algorithm | The problem is decomposed into smaller, manageable sub-problems that are first independently solved in an approximate manner and then merged into the final solution. | ^{[80]} |
Hill Climbing | The algorithm explores the neighboring solutions and picks those with the best properties, so that the algorithm constantly “climbs” toward them. | ^{[81]} |
Greedy Algorithms | Immediate local improvements are prioritized without taking into account the effect on global optimization. The underlying assumption is that such “greedy” choices will result in an acceptable approximation. | ^{[82]} |
Approximation Algorithms | Solutions are searched for within provable limits around the optimal solution. The aim is to achieve the maximum efficiency. This is convenient for difficult nondeterministic polynomial time problems. | ^{[83]} |
Local Search Algorithms | An initial solution is assumed, and it is iteratively improved by exploring the immediate vicinity and making small local modifications. No completely new solutions are constructed. | ^{[84]} |
Constructive Algorithms | Solutions are built part-by-part from an empty set by adding one building block at a time. The procedure is iterative and uses heuristics for the choice of the building blocks. | ^{[85]} |
Constraint Satisfaction Algorithms | A set of constraints is defined at the beginning. The solution space is then searched locally, each time applying the constraints until all of them are satisfied. | ^{[86]} |
Branch And Bound Algorithm | The solution space is systematically divided into smaller sub-problems, the search space is bounded according to problem-specific criteria, and branches that result in suboptimal solutions are pruned and removed. | ^{[87]} |
Cutting Plane Algorithm | An optimization method solving linear programming problems. It finds the optimal solution by iteratively adding new, additional constraints (cutting planes), thus gradually tightening the region of possible solutions and converging towards the optimum. | ^{[88]} |
Iterative Improvement Algorithms | Here the goal is to iteratively improve an initially proposed problem solution. Thus, systematic adjustments and improvements are made to the initial set by targeting the predefined objectives. The values may be reordered, retuned or swapped until the desired optimization is complete. | ^{[89]} |
Algorithm Group | Algorithm name | abbr. | Ref. |
Evolutionary Algorithms | Genetic algorithm | GA | ^{[96]} |
Memetic Algorithm | MA | ^{[97]} | |
Differential Evolution | DE | ^{[98]} | |
Swarm Intelligence Algorithms | Particle Swarm Optimization | PSO | ^{[99]} |
Whale Optimization Algorithm | WOA | ^{[100]} | |
Gray Wolf Optimizer | GWO | ^{[101]} | |
Artificial Bee Colony Algorithm | ABCA | ^{[102]} | |
Ant Colony Optimization | ACO | ^{[103]} | |
Artificial Fish Swarm Algorithm | AFSA | ^{[104]} | |
Firefly Algorithm | FA | ^{[105]} | |
Fruit Fly Optimization Algorithm | FFOA | ^{[106]} | |
Cuckoo Search Algorithm | CS | ^{[107]} | |
Bat Algorithm | BA | ^{[108]} | |
Bacterial Foraging | BFA | ^{[109]} | |
Social Spider Optimization | SSO | ^{[110]} | |
Locust Search Algorithm | LS | ^{[111]} | |
Symbiotic Organisms Search | SOS | ^{[112]} | |
Moth-flame Optimization | MFOA | ^{[113]} | |
Honey Badger Algorithm | HBA | ^{[114]} | |
Elephant Herding Optimization | EHO | ^{[115]} | |
Grasshopper Algorithm | GOA | ^{[116]} | |
Harris Hawks Optimization | HHO | ^{[117]} | |
Orca Predation Algorithm | OPA | ^{[118]} | |
Starling Murmuration Optimizer | SMO | ^{[119]} | |
Serval Optimization Algorithm | SOA | ^{[120]} | |
Coral Reefs Optimization Algorithm | CROA | ^{[121]} | |
Krill Herd Algorithm | KH | ^{[122]} | |
Gazelle optimization algorithm | GOA | ^{[123]} | |
Algorithms Mimicking human or zoological physiological functions | Artificial Immune Systems | AIS | ^{[124]} |
Neural Network Algorithm | NNA | ^{[125]} | |
Human Mental Search | HMS | ^{[126]} | |
Anthropological algorithms (Mimicking human social behavior) | Imperialist Competitive Algorithm | ICA | ^{[127]} |
Anarchic Society Optimization | ASO | ^{[128]} | |
Teaching-Learning Base Optimization | TLBO | ^{[129]} | |
Society and Civilization Optimization | SC | ^{[130]} | |
League Championship algorithm | LCA | ^{[131]} | |
Volleyball Premier League algorithm | VPL | ^{[132]} | |
Duelist algorithm | DA | ^{[133]} | |
Tabu search | TS | ^{[134]} | |
Human urbanization algorithm | HUA | ^{[135]} | |
Political Optimizer | PO | ^{[136]} | |
Plant-Based Algorithms | Flower Pollination Algorithm | FPA | ^{[137]} |
Invasive Weed Optimization | IWO | ^{[138]} | |
Plant Propagation Algorithm | PPA | ^{[139]} | |
Plant Growth Optimization | PGO | ^{[140]} | |
Tree Seed Algorithm | TSA | ^{[141]} | |
Paddy Field Algorithm | PFA | ^{[142]} |