My Optimization Algorithms

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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. 

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.