1. Introduction Pigeon-inspired Optimization (PIO) algorithm is a novel swarm intelligence optimization algorithm, which was firstly invented by Duan in 2014. Population-based swarm intelligence algorithms have been widely accepted and successfully applied to solve many optimization problems. All the bio-inspired optimization algorithms are trying to simulate the natural ecosystem mechanisms, which have greatly improved […]
Tag: AI
A Bumble Bees Mating Optimization (BBMO) Algorithm to solve Numerical Optimization Problems
1. Introduction In computer science and operations research, the bee’s algorithm is a population-based search algorithm which was developed by Pham, Ghanbarzadeh et al. in 2005. It mimics the food foraging behaviour of honey bee colonies. In its basic version the algorithm performs a kind of neighborhood search combined with global search, and can […]
Continue ReadingDragonfly Algorithm (DA) to solve Numerical Optimization Problem
1. Introduction The main inspiration of the Dragonfly Algorithm (DA) algorithm proposed in 2015 originates from static and dynamic swarming behaviours. These two swarming behaviours are very similar to the two main phases of optimization using meta-heuristics: exploration and exploitation. Dragonflies create sub swarms and fly over different areas in a static swarm, which […]
Continue ReadingNew Nature Inspired Metaheuristic Algorithm for Elephants: Elephant Herding Optimization (EHO) Algorithm
1. Introduction EHO was inspired by social behavior of elephants in herds. It was proposed by Wang et al. Nature-Inspired methods are playing a vital role to solve various real-life problems, which may be very difficult or sometimes impossible to be solved using analytical methods [1]. So far, numerous optimization algorithms inspired by genetics, […]
Continue ReadingBehavior of Grey Wolf Optimization (GWO) Algorithm using Meta-heuristics method
1. Introduction The GWO algorithm mimics the leadership hierarchy and hunting mechanism of gray wolves in nature proposed by Mirjalili et al. in 2014. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In addition, three main steps of hunting, searching for prey, encircling […]
Continue ReadingAnt Colony Optimization (ACO) Algorithm to solve Numericcal Optimization Problem
1. Introduction In the 1990’s, Ant Colony Optimization was introduced as a novel nature-inspired method for the solution of hard combinatorial optimization problems. The Ant Colony Algorithms family, in swarm intelligence methods, and it constitutes some metaheuristic optimizations [1]. Initially proposed by Marco Dorigo in 1992 in his PhD thesis, the first algorithm was […]
Continue ReadingArtificial Bee Colony (ABC) Algorithm: A Novel Method Motivated from the Behavior of Bees for Optimal Solution
1. Introduction The Artificial Bee Colony (ABC) algorithm is a swarm based meta-heuristic algorithm that was introduced by Karaboga in 2005 for optimizing numerical problems. It was inspired by the intelligent foraging behavior of honey bees. The algorithm is specifically based on the model proposed by Tereshko and Loengarov (2005) for the foraging behavior […]
Continue ReadingAn Efficient Moth Flame Optimization (MFO) Algorithm for Solving Numerical Expressions
1. Introduction Moth-flame optimization algorithm is a new metaheuristic optimization method, which is proposed by Seyedali Mirjalili in 2015 and based on the simulation of the behavior of moths for their special navigation methods in night [1]. They utilize a mechanism called transverse orientation for navigation. In this method, a moth flies by maintaining […]
Continue ReadingBacterial Foraging Optimization Algorithm (BFOA): Inspired from Social foraging behavior of Escherichia coli
1. Introduction Bacterial Foraging Optimization Algorithm (BFOA), proposed by Passino (2002), is a new comer to the family of nature-inspired optimization algorithms. Natural selection tends to eliminate animals with poor “foraging strategies” (methods for locating, handling, and ingesting food) and favor the propagation of genes of those animals that have successful foraging strategies since […]
Continue ReadingNew Nature Inspired Metaheuristic Algorithm for Bats: Bat Search Algorithm (BSA)
1. Introduction The Bat algorithm is a metaheuristic algorithm for global optimization. It was inspired by the echolocation behavior of microbats, with varying pulse rates of emission and loudness. The Bat algorithm was developed by Xin-She Yang in 2010. Metaheuristic algorithms such as particle swarm optimization and simulated annealing are now becoming powerful methods […]
Continue Reading