1. Introduction Optimization algorithm which is called Cuttlefish Algorithm (CFA). The algorithm mimics the mechanism of color changing behavior of the cuttlefish to solve numerical global optimization problems [1]. The colors and patterns of the cuttlefish are produced by reflected light from three different layers of cells. Evolutionary algorithms are a part of artificial […]
Author: Decoderz
A Novel Numerical Optimization Algorithm Inspired from Salps: Salp Swarm Optimization Algorithm
1. Introduction Inspiration analysis Salp Swarm Algorithm (SSA) is one of meta-heuristic algorithms recently proposed by Mirjalili in 2017. A novel optimization algorithm called Salp Swarm Optimizer (SSA) is proposed. Multi-objective Salp Swarm Algorithm (MSSA) is proposed to solve multi-objective problems [1]. Swarm intelligence techniques mimic the intelligence of swarms, herds, schools, or flocks […]
Continue ReadingKrill Herd (KH) Algorithm to solve Numerical Optimization Problem
1. Introduction Based on the simulation of the herding behavior of krill individuals, Gandomi and Alavi proposed the krill herd algorithm (KH) in 2012. And KH algorithm is a novel biologically inspired algorithm to solve the optimization problems [1]. In KH algorithm, the objective function for the krill movement is determined by the minimum […]
Continue ReadingOwl Search Algorithm: A Novel Nature Inspired Metaheuristic Method
1. Introduction The metaheuristic optimization techniques have gained significant attention of researchers due to successful application of these techniques in a variety of complex optimization problems. These techniques are found more effective than conventional methods which use derivative information of function [1]. Two eminent features of any metaheuristic technique are exploration and exploitation. Exploration […]
Continue ReadingBehavior of Mouth Brooding Fish Algorithm (MBF) using Meta-heuristics method
1. Introduction In the past few decades, nature-inspired computation has attracted more attention. Many real-world engineering optimization problems are substantially very complicated and quite difficult to solve [1]. However, nature serves as a fertile source of concepts, principles, and mechanisms for designing artificial computation systems to tackle complicated computational problems. In nature, mouth brooding […]
Continue ReadingSpotted Hyena Optimization (SHO) Algorithm: Mimicked from Hunting Behavior of Hyena
1. Introduction Spotted Hyena Optimizer is a metaheuristic bio-inspired optimization algorithm developed by Dhiman et al. The fundamental concept of this algorithm is to simulate the social behaviors of spotted hyenas. The main steps of SHO algorithm are inspired by their hunting behavior. Further, the SHO algorithm is tested on real-life constrained engineering design […]
Continue ReadingBehavior of Squirrel Search Algorithm (SSA) using Meta-heuristics method
1. Introduction A novel nature-inspired optimization paradigm, named as squirrel search algorithm (SSA). This optimizer imitates the dynamic foraging behavior of southern flying squirrels and their efficient way of locomotion known as gliding. Gliding is an effective mechanism used by small mammals for travelling long distances [1]. The present work mathematically models this behavior […]
Continue ReadingA New Seagull Optimization Algorithm to Solve Numerical Optimization Problems
1. Introduction A novel bio-inspired algorithm called Seagull Optimization Algorithm (SOA) for solving computationally expensive problems. The main inspiration of this algorithm is the migration and attacking behaviors of a seagull in nature [1]. These behaviors are mathematically modeled and implemented to emphasize exploration and exploitation in a given search space. The performance of […]
Continue ReadingPigeon Inspired Optimization (PIO) Algorithm: A Novel method motivated from the behavior of Pigeons for Optimal Solution
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 […]
Continue ReadingA 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 […]
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