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My Optimization Algorithms

Grey Wolf Optimizer

Whale Optimization Algorithm

Whale Optimization Algorithm

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

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Whale Optimization Algorithm

Whale Optimization Algorithm

Whale Optimization Algorithm

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

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Ant Lion Optimizer

Whale Optimization Algorithm

Moth Flame Optimizer

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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.

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Moth Flame Optimizer

Grasshopper Optimization Algorithm

Moth Flame Optimizer

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 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. 

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Dragonfly Algorithm

Grasshopper Optimization Algorithm

Grasshopper Optimization Algorithm

The main inspiration of the DA algorithm originates from the static and dynamic swarming behaviours

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.

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Grasshopper Optimization Algorithm

Grasshopper Optimization Algorithm

Grasshopper Optimization Algorithm

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

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Multi-Verse Optimizer

Multi-Verse Optimizer

Multi-Verse Optimizer

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 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.  

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Sine Cosine Algorithm

Multi-Verse Optimizer

Multi-Verse Optimizer

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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.

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Salp Swarm Algorithm

Multi-Verse Optimizer

Salp Swarm Algorithm

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

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Other Algorithms

Particle Swarm Optimization

Particle Swarm Optimization

Particle Swarm Optimization

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

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Ant Colony Optimization

Particle Swarm Optimization

Particle Swarm Optimization

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

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Genetic Algorithm

Particle Swarm Optimization

Genetic Algorithm

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

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Hill Climbing

Differential Evolution

Genetic Algorithm

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

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Simulated Annealing

Differential Evolution

Differential Evolution

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

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Differential Evolution

Differential Evolution

Differential Evolution

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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.  

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Copyright © 2021 Seyedali Mirjalili - All Rights Reserved.

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