An algorithm that mimics the social hierarchy and navigation mechanism of grey wolves in nature to solve optimization problems.

An optimization algorithm inspired from bubble-net foraging of humpback whales.

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.

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.

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.

This algorithm mathematically models and mimics the behaviour of grasshopper swarms in nature for solving optimisation problems.

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.

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.

The main inspiration of this algorithm is the swarming behavior of salps when navigating and foraging in oceans.

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.

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.

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 (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.

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.

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.

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This algorithm simulates African vultures’ foraging and navigation behaviours.** **

This new metaheuristic algorithm is inspired by gorilla troops' social intelligence in nature.

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.

Driven by the hunting tactics employed by cheetahs, this research introduces a nature-inspired algorithm termed the Cheetah Optimizer (CO). Cheetahs typically employ three primary strategies during their hunting endeavors, namely, searching, sitting-and-waiting, and attacking. This study incorporates these strategies into its framework. Furthermore, the strategy of leaving the prey and returning to the habitat is also integrated into the hunting process to enhance the population diversification, convergence performance, and robustness of the proposed framework.

In this algorithm, gazelles' hierarchical and social life is formulated mathematically and used to develop an optimization algorithm.

The algorithm emulates the geometric mean operator in mathematics. It introduces the Dual-Fitness Index (DFI), a new index that evaluates both fitness and diversity of search agents. This helps select competent guides without needing to know the optimization process phase. It solves the problem of delineating exploration and exploitation phases, which varies across problems. Other advantages include using unique guides for search agents, applying Gaussian mutation, and not requiring parameter tuning for reliable results.

The project introduces the Crayfish Optimization Algorithm (COA), a meta-heuristic optimization method inspired by crayfish behavior. COA mimics three key behaviors of crayfish - summer resort behavior, competition behavior, and foraging behavior, each corresponding to different algorithmic stages. These stages aim to balance exploration and exploitation. During the "summer resort" stage, COA explores solutions. The "competition" and "foraging" stages represent the exploitation phase. Temperature control guides the transition between these stages, with high temperatures prompting crayfish to seek shelter or compete for caves, while suitable temperatures dictate foraging strategies based on food size. Temperature regulation enhances COA's randomness and global optimization capabilities.

MEALPY is the largest Python library for most of the cutting-edge nature-inspired meta-heuristic algorithms (population-based). Population meta-heuristic algorithms (PMA) are the most popular algorithms in the field of approximate optimization.

The PSO algorithm mimics the swarming behaviour of bird flocks in nature.

The ACO algorithm solves optimization problems in a similar manner that ants find the shortest path from a food source to their nest.

The GA algorithm inspires from Darwinian's survival of the fittest theory in nature.

This is a local search algorithm that attempts to find a better solution by making an incremental change to the solution.

Simulated annealing is an improved version of hill climbing that mimics the annealing process in metallurgy.

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.

Silas a new high-performance machine learning tool, which is built to provide a more transparent, dependable and efficient data analytics service.

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