My Optimization Algorithms
Grey Wolf Optimizer
Whale Optimization Algorithm
Whale Optimization Algorithm
An algorithm that mimics the social hierarchy and navigation mechanism of grey wolves in nature to solve optimization problems.
Whale Optimization Algorithm
Whale Optimization Algorithm
Whale Optimization Algorithm
An optimization algorithm inspired from bubble-net foraging of humpback whales.
Ant Lion Optimizer
Whale Optimization Algorithm
Moth Flame Optimizer
The ALO algorithm mimics the hunting mechanism of antlions in nature. Five main steps of hunting prey such as the random walk of ants, building traps, entrapment of ants in traps, catching preys, and re-building traps are implemented.
Moth Flame Optimizer
Grasshopper Optimization Algorithm
Moth Flame Optimizer
The main inspiration of this optimizer is the navigation method of moths in nature called transverse orientation. Moths fly in night by maintaining a fixed angle with respect to the moon, a very effective mechanism for travelling in a straight line for long distances. However, these fancy insects are trapped in a useless/deadly spiral path around artificial lights. This paper mathematically models this behaviour to perform optimization.
Dragonfly Algorithm
Grasshopper Optimization Algorithm
Grasshopper Optimization Algorithm
The main inspiration of the DA algorithm originates from the static and dynamic swarming behaviors of dragonflies in nature. Two essential phases of optimization, exploration, and exploitation, are designed by modeling the social interaction of dragonflies in navigating, searching for foods, and avoiding enemies when swarming dynamically or statistically.
Grasshopper Optimization Algorithm
Grasshopper Optimization Algorithm
Grasshopper Optimization Algorithm
This algorithm mathematically models and mimics the behaviour of grasshopper swarms in nature for solving optimisation problems.
Multi-Verse Optimizer
Multi-Verse Optimizer
Multi-Verse Optimizer
The main inspirations of this algorithm are based on three concepts in cosmology: white hole, black hole, and wormhole. The mathematical models of these three concepts are developed to perform exploration, exploitation, and local search, respectively.
Sine Cosine Algorithm
Multi-Verse Optimizer
Multi-Verse Optimizer
This algorithm creates multiple initial random candidate solutions and requires them to fluctuate outwards or towards the best solution using a mathematical model based on sine and cosine functions. Several random and adaptive variables also are integrated to this algorithm to emphasize exploration and exploitation of the search space in different milestones of optimization.
Salp Swarm Algorithm
Multi-Verse Optimizer
Salp Swarm Algorithm
The main inspiration of this algorithm is the swarming behavior of salps when navigating and foraging in oceans.
Selected Algorithms with Collaborators
Equilibrium Optimizer
Henry Gas Solubility Optimization
Marine Predator Algorithm
This algorithm is inspired by control volume mass balance models used to estimate both dynamic and equilibrium states. In EO, each particle (solution) with its concentration (position) acts as a search agent.
Marine Predator Algorithm
Henry Gas Solubility Optimization
Marine Predator Algorithm
The main inspiration of MPA is the widespread foraging strategy namely Lévy and Brownian movements in ocean predators along with optimal encounter rate policy in biological interaction between predator and prey. MPA follows the rules that naturally govern in optimal foraging strategy and encounters rate policy between predator and prey in marine ecosystems.
Henry Gas Solubility Optimization
Henry Gas Solubility Optimization
Arithmetic Optimization Algorithm
This algorithm mimics the behavior governed by Henry’s law to solve challenging optimization problems. The HGSO algorithm imitates the huddling behavior of gas to balance exploitation and exploration in the search space and avoid local optima.
Arithmetic Optimization Algorithm
Arithmetic Optimization Algorithm
Arithmetic Optimization Algorithm
Arithmetic Optimization Algorithm (AOA) that utilizes the distribution behavior of the main arithmetic operators in mathematics including (Multiplication (M), Division (D), Subtraction (S), and Addition (A)). AOA is mathematically modeled and implemented to perform the optimization processes in a wide range of search spaces.
The GNDO algorithm
Arithmetic Optimization Algorithm
Flow Direction Algorithm
Generalized Normal Distribution Optimization utilizes the generalized normal distribution model. Each individual uses a generalized normal distribution curve to update its position during the optimization process.
Flow Direction Algorithm
Arithmetic Optimization Algorithm
Flow Direction Algorithm
This algorithm mimics the flow direction to the outlet point with the lowest height in a drainage basin. In other words, flow moves to a neighbor with the lowest high or best objective function.
The AVOA algorithm
Artificial Hummingbird Algorithm
The AVOA algorithm
This algorithm simulates African vultures’ foraging and navigation behaviours.
The AGTO algorithm
Artificial Hummingbird Algorithm
The AVOA algorithm
This new metaheuristic algorithm is inspired by gorilla troops' social intelligence in nature.
Artificial Hummingbird Algorithm
Artificial Hummingbird Algorithm
Artificial Hummingbird Algorithm
The AHA algorithm simulates the special flight skills and intelligent foraging strategies of hummingbirds in nature. Three kinds of flight skills utilized in foraging strategies, including axial, diagonal, and omnidirectional flights, are modeled. In addition, guided foraging, territorial foraging, and migrating foraging are implemented, and a visit table is constructed to model the memory function of hummingbirds for food sources.
Cheetah Optimizer
Cheetah Optimizer
Artificial Hummingbird Algorithm
CO mimics the hunting strategies of cheetahs.
Other Algorithms
Particle Swarm Optimization
Particle Swarm Optimization
Particle Swarm Optimization
The PSO algorithm mimics the swarming behaviour of bird flocks in nature.
Ant Colony Optimization
Particle Swarm Optimization
Particle Swarm Optimization
The ACO algorithm solves optimization problems in a similar manner that ants find the shortest path from a food source to their nest.
Genetic Algorithm
Particle Swarm Optimization
Genetic Algorithm
The GA algorithm inspires from Darwinian's survival of the fittest theory in nature.
Hill Climbing
Differential Evolution
Genetic Algorithm
This is a local search algorithm that attempts to find a better solution by making an incremental change to the solution.
Simulated Annealing
Differential Evolution
Differential Evolution
Simulated annealing is an improved version of hill climbing that mimics the annealing process in metallurgy.
Differential Evolution
Differential Evolution
Differential Evolution
Differential evolution is an optimization algorithm that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality.
Projects on Machine Learning
Silas
Silas a new high-performance machine learning tool, which is built to provide a more transparent, dependable and efficient data analytics service.