- Design and Analysis of Fuel Cell and Battery With Grid Using PSO ControllerModel Description: Model is designed in MATLAB/Simulink working Platform version R2015a (64 bit version). System Requirements: Intel(R) Core(TM) i3-5005U CPU @ 2.00GHZ, 4GB RAM. In this video, we will be discussing about Fuel Cell & Battery With Grid Using PSO Controller Model MATLAB/Simulink design with Step by Step Execution process and Achieved Results.This model consists… Read more: Design and Analysis of Fuel Cell and Battery With Grid Using PSO Controller
- Decoderz #36 Grid Connected 2-PV & 1-FC Using PSO Controller ModelIn this video, we will be discussing about Grid Connected 2-PV & 1-FC Using PSO Controller Model MATLAB/Simulink design with Step by Step Execution process and Achieved Results. Watch this video till end and leave comment if you have any suggestions?. Subscribe our channel for more videos. Thanks for watching. Contact us: decoderzmt@gmail.com Blog: https://transpireonline.blog/
- Decoderz #35 |25 May 2021| Grid Integrated WECS With UPFC Using PSO Model based PQ ControllerIn this video, we will be discussing about Grid Integrated WECS With UPFC Using PSO Model based PQ Controller MATLAB/Simulink design with Step by Step Execution process and Achieved Results.Watch this video till end and leave comment if you have any suggestions?. Subscribe our channel for more videos. Thanks for watching. Contact us: decoderzmt@gmail.com Blog:… Read more: Decoderz #35 |25 May 2021| Grid Integrated WECS With UPFC Using PSO Model based PQ Controller
- Dual Kidney-Inspired Algorithm (Dual-KA) for Water Quality Prediction and Cancer Detection1. Introduction The Kidney-inspired Algorithm (KA) imitates the physiological process of the kidneys in the human body. The second kidney in the human body filters all the solutes if the other kidney fails. If the second kidney also gets damaged, dialysis can be performed as a treatment. The failure of a kidney is proved by… Read more: Dual Kidney-Inspired Algorithm (Dual-KA) for Water Quality Prediction and Cancer Detection
- Mayfly Optimization Algorithm (MA): A population-based approach used for the Application of flow-shop scheduling problem1. Introduction Mayflies are insects that belong to the order Ephemeroptera, which is part of an ancient group of insects called Palaeoptera. It is estimated that there are over 3000 species of mayflies worldwide. Their name derives from the fact that they appear mainly during May in the UK [1]. The Mayfly optimation algorithm (MA)… Read more: Mayfly Optimization Algorithm (MA): A population-based approach used for the Application of flow-shop scheduling problem
- A Novel Swarm-Intelligence based Optimization Algorithm: Rat Swarm Optimizer (RSO) for Solving the Challenging Optimization Problems1. Introduction Nature-inspired algorithms are becoming extremely popular for solving various optimization problems [1]. This is due to the fact that these algorithms search for the fittest solution based upon ‘trial and error’ criterion. Also, they are easy to implement due to their simple conceptual model and the least requirement of gradient information [2]. Broadly,… Read more: A Novel Swarm-Intelligence based Optimization Algorithm: Rat Swarm Optimizer (RSO) for Solving the Challenging Optimization Problems
- Political Optimizer (PO): Inspired by the Multi-Phased Political Process Efficiently Solving Classical Engineering Problems1. Introduction Politics in different contexts holds different meanings. In political optimizer algorithm PO, we use the political system of a country as a reference point and mimic the behavior of politicians to achieve the end goal of optimization [1]. Politics is about the governance of a region, state or country. The party-based political systems… Read more: Political Optimizer (PO): Inspired by the Multi-Phased Political Process Efficiently Solving Classical Engineering Problems
- Horse Optimization Algorithm (HOA): Representative in Engineering Problem Application on classification of the Smart Grid Stability1. Introduction Bio-inspired computing refers to a class of optimization algorithms which apply the intelligence of the nature and one of their main applications is the solving of the complex engineering optimization problems [1]. A novel algorithm inspired from the hierarchical organization of the horse herds obtained a Horse Optimization Algorithm (HOA). The modified version… Read more: Horse Optimization Algorithm (HOA): Representative in Engineering Problem Application on classification of the Smart Grid Stability
- Chaos Game Optimization (CGO): Useful for the Application of Fractal Theory such as Classification and Identification1. Introduction Game is a competitive activity involving skill, chance, or endurance on the part of two or more persons who play according to a set of rules, usually for their own amusement or for that of spectators Most of the design problems in the nature can be considered as optimization problems which request some… Read more: Chaos Game Optimization (CGO): Useful for the Application of Fractal Theory such as Classification and Identification
- Inspired by Plants Survival An New Optimization is presented: Fertile Field Algorithm for Continuous Nonlinear Optimization Problems1. Introduction Nature, as a rich source of solutions, can be an inspirational guide to answer scientific expectations [1]. Seed dispersal mechanism as one of the most common reproduction [2] method among the plants is a unique technique with millions of years of evolutionary history [3]. Inspired by plants survival, a novel method of optimization… Read more: Inspired by Plants Survival An New Optimization is presented: Fertile Field Algorithm for Continuous Nonlinear Optimization Problems
- Rain optimization algorithm (ROA): Inspired by the Raindrop’s behaviour used for the Application of Forecasting Rainfall using Machine Learning Strategies1. Introduction A new optimization algorithm called Rain optimization Algorithm (ROA) [1] is validated by applying on computationally expensive benchmark problems [2]. ROA mimics the natural behavior of raindrops trickling down a hill (high position) towards a valley (low position) [3]. 2. Inspiration of ROA The main inspiration of ROA is from raindrop behavior [4]. Raindrops naturally trickle down along… Read more: Rain optimization algorithm (ROA): Inspired by the Raindrop’s behaviour used for the Application of Forecasting Rainfall using Machine Learning Strategies
- Sandpiper Optimization Algorithm (SOA): A New Approach to Solve Challenging Real- Life Problems1. Introduction In different real-world problems there is a need to minimize production cost, risks, and maximize reliability etc [1]. Optimization is the process of determining the best value for a decision variable of a function so as to minimize or maximize an objective function [2]. The objective of the optimization process is to determine… Read more: Sandpiper Optimization Algorithm (SOA): A New Approach to Solve Challenging Real- Life Problems
- Vapour – Liquid Equilibrium (VLE): An Algorithm Inspired by the Chemical Phenomenon for Solving a New Patient Bed Assignment Problem1. Introduction In Chemistry, the vapor-liquid equilibrium process describes the distribution of chemical species combining two essential phases [1]: vapor phase and liquid phase. Using a binary system of compounds is possible to simulate a search process based on the equilibrium between both phases [2]. Understanding the distribution of chemical components in both the vapor and liquid phase, called vapor-liquid… Read more: Vapour – Liquid Equilibrium (VLE): An Algorithm Inspired by the Chemical Phenomenon for Solving a New Patient Bed Assignment Problem
- Border Collie Optimization (BOC): Algorithm mimicking the Sheep Herding Styles of Border Collie Dogs used for Solving Real World Optimization Problem1. Introduction The Border collie is often considered to be the brightest dog from other breeds that are considered to have higher cognitive skill levels [1]. It is known that dogs, like people, have different levels of intelligence [2]. Intelligence is determined by the ability of the dog to learn, which generally means the ability… Read more: Border Collie Optimization (BOC): Algorithm mimicking the Sheep Herding Styles of Border Collie Dogs used for Solving Real World Optimization Problem
- Vascular Invasive Tumor Growth Optimization (VITGO): The Tumor Growth Machanism to Encounter Optimization Problems in Scientific and Technological Research and Development1. Introduction Tumor is an abnormal growth of tissue [1]. The growth of tumor is a complex process, influenced by the interactions between tumor cells and their microenvironment, including their surrounding cells, as well as the extracellular matrix (ECM) [2], chemical signals, as well as metabolic substrates such as oxygen and glucose. Tumor tissue are… Read more: Vascular Invasive Tumor Growth Optimization (VITGO): The Tumor Growth Machanism to Encounter Optimization Problems in Scientific and Technological Research and Development
- A Novel Game Based Algorithm: Shell Game Optimization (SGO) Simulating the Rules of Game for Solving Optimization Problems in Different Fields of Science1. Introduction Shell Game Optimization (SGO) simulating the rules of a game known as shell game to design an algorithm for solving optimization problems in different fields of science [1]. The key idea of the SGO is to find the ball hidden under one of the three shells, which should be guessed by players [2].… Read more: A Novel Game Based Algorithm: Shell Game Optimization (SGO) Simulating the Rules of Game for Solving Optimization Problems in Different Fields of Science
- Groundwater Flow Algorithm (GWFA): Inspired by the Movement of Groundwater Flow Applied to Several Standard Engineering Problems1. Introduction Groundwater is one of the most valuable natural resources especially in arid regions due to negligible rainfall and the scarcity of surface water resources [1]. Groundwater models are computer models of groundwater flow systems, and are used by various types of numerical solutions like the finite difference method and the finite element method. 2. Optimization Approach Technique Optimization… Read more: Groundwater Flow Algorithm (GWFA): Inspired by the Movement of Groundwater Flow Applied to Several Standard Engineering Problems
- Corona Virus Optimization Algorithm (CVOA): A Successful Application in Hybrid Approaches to Find Parameters in Machine Learning Model Optimization1. Introduction The corona virus (COVID-19) is a new respiratory virus, firstly discovered in humans in December 2019 in Wuhan china that has spread worldwide, having been reported more than 1 million infected people so far [1]. Corona viruses are a large family of viruses which may cause illness in animals or humans [2]. In… Read more: Corona Virus Optimization Algorithm (CVOA): A Successful Application in Hybrid Approaches to Find Parameters in Machine Learning Model Optimization
- A Novel Parameter –free Optimization Algorithm for Solving Real Engineering Problems: Golden Ratio Optimization Method (GROM)1. Introduction A new meta-heuristic optimization algorithm known as golden ratio optimization method (GROM) that is based on natural growth [1]. The golden ratio is often found in nature and even in the human body while many of the most often examples of the golden ratio have been found in plants, animals, insects and other natural… Read more: A Novel Parameter –free Optimization Algorithm for Solving Real Engineering Problems: Golden Ratio Optimization Method (GROM)
- A Brand – New Metaheuristic Algorithm: Slime Mould Algorithm to Tackle the Optimization Problem1. Introduction The giant single-celled slime mould Physarum polycephalum is known to approximate a number of network problems via growth and adaptation of its protoplasmic transport network and can serve as an inspiration towards unconventional, material-based computation [1]. It is clear that humans cannot live without the rhythmic patterns of signals or material flows.Therfore a… Read more: A Brand – New Metaheuristic Algorithm: Slime Mould Algorithm to Tackle the Optimization Problem
- Shuffled Shepherd Optimization Algorithm (SSOA): To Find the Right Parameters for Each Problem1. Introduction In today’s extremely competitive world, everybody offers the maximum output or profit from a limited amount of available resources [1]. Optimization offers a suitable technique for finding maximum output or profit. That is why optimization techniques are becoming more popular. A new population-based meta-heuristic optimization algorithm of Shuffled shepherd optimization algorithm (SSOA) is… Read more: Shuffled Shepherd Optimization Algorithm (SSOA): To Find the Right Parameters for Each Problem
- A Novel Bio- Inspired Metaheuristic Algorithm: Tunicate Swarm Algorithm (TSA) for Optimizing Non –Linear Constrained Problems1. Introduction A novel bio-inspired metaheuristic algorithm, named as Tunicate Swarm Algorithm, is simplified for optimizing non-linear constrained problems [1]. It is inspired by the swarm behavior of tunicate to survive successfully find the location of food source in the depth of ocean [2]. This fact has motivated to develop a new population based metaheuristic… Read more: A Novel Bio- Inspired Metaheuristic Algorithm: Tunicate Swarm Algorithm (TSA) for Optimizing Non –Linear Constrained Problems
- Metaheuristic Anopheles Search Algorithm: An Natural Inspired Phenomena Utilized in Engineering Optimization Problems1. Introduction Nowadays, different optimization problems have been solved using various optimization techniques mainly try to obtain optimal solution in the proximity of initial point [1]. Due to increase in complexity of optimization problems and number of optimal points, efficiency of finding global optima has decreased [2]. Thus the new natural phenomena inspired algorithm known… Read more: Metaheuristic Anopheles Search Algorithm: An Natural Inspired Phenomena Utilized in Engineering Optimization Problems
- Manta Ray Foraging Optimization (MRFO): An Effective Bio-Inspired Optimization Technique for addressing Engineering Applications1. Introduction Many real-world optimization problems are increasingly becoming challenging [1]. Therefore a new bio-inspired optimization technique, named Manta Ray Foraging Optimization (MRFO) algorithm, is presented, aiming to providing a novel algorithm that provides an alternate optimization approach for addressing real-world engineering issues [2]. The contribution of this algorithm is the foraging behaviors of manta […]
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- The Feedback Artificial Tree Algorithm (FAT): Great Potential to Solve Wide Range of Practical Optimization Problems1. Introduction The Feedback Artificial Tree Algorithm employs a tree-based recovery mechanism that defined to maintains reachability information to three generations of family in the tree [1]. The entire material exchange process means that both of the transfer of organic matters and the feedback of moistures are taken into account. Meanwhile, with the moisture feedback… Read more: The Feedback Artificial Tree Algorithm (FAT): Great Potential to Solve Wide Range of Practical Optimization Problems
- A Novel Hunting based Algorithm: Chimp Optimization Algorithm (ChOA) Utilizing to tackle different Optimization problems in different Industrial tasks.1. Introduction The chimpanzee, also known as the common chimpanzee, robust chimpanzee, or simply “chimp”, is a species of native to the forests of tropical Africa [1]. The chimpanzee is covered in coarse black hair, but has a bare face, fingers, toes, palms of the hands, and soles of the feet. They are as much as the closest to the humans… Read more: A Novel Hunting based Algorithm: Chimp Optimization Algorithm (ChOA) Utilizing to tackle different Optimization problems in different Industrial tasks.
- Butterfly Optimization Algorithm(BOA) : To Solve Engineering Problems1.Introduction Butterfly optimization algorithm(BOA) is a population based natural inspired algorithm. BOA algorithm is first introduced by Aroa and Sing in 2019[1]. The real-world biological or physical phenomena of the food/flowersare solved using BOA optimization problems. The butterflies food searching capability is the main inspiration of this algorithm. Butterflies have a several senses such as… Read more: Butterfly Optimization Algorithm(BOA) : To Solve Engineering Problems
- A Novel Physics based Optimization Algorithm: Equilibrium Optimizer (EO)1. Introduction Equilibrium is a state in which opposing forces are balanced. Optimization and equilibrium models are used routinely in environmental engineering and science [1]. This allows solving different kinds of equilibrium problems, such as multi-objective optimization problems etc, the equilibrium optimizer of each particle solution with its concentration position acts as a search agent… Read more: A Novel Physics based Optimization Algorithm: Equilibrium Optimizer (EO)
- A New Nature Inspired Search Algorithm: Woodpecker Mating Algorithm (WMA) applies it to Challenging problems in Structural Optimization1. Introduction The Woodpecker Mating Algorithm (WMA) is the nature inspired search population-based metaheuristic algorithm that imitates the mating behavior of woodpeckers [1]. Woodpeckers are wonderful birds and there are nearly 200 various species of them. They use an effective strategy of communication known as drumming to attract the other gender for the process of… Read more: A New Nature Inspired Search Algorithm: Woodpecker Mating Algorithm (WMA) applies it to Challenging problems in Structural Optimization
- A Population Based Metaheuristic Art Inspired Algorithm: Color Harmony Algorithm (CHA) for Solving Real World Optimization Problems1. Introduction Colors, usually perceived a major role in the world for the numerous number of multicolored combinations to the objects. It is known that the relationship between colors and human emotions has a stable inﬂuence on how we perceive our environment [1]. A population-based metaheuristic method algorithm known as the Color Harmony Algorithm is… Read more: A Population Based Metaheuristic Art Inspired Algorithm: Color Harmony Algorithm (CHA) for Solving Real World Optimization Problems
- A Dynamic Polymorphic Population based Algorithm: Side – Blotched Lizard Algorithm (SBLA) a reliable Algorithm to use in Real – World Problems1. Introduction The Side- Blotched Lizard Algorithm (SBLA) is a subpopulation-based optimization algorithm is to define the total and partial populations of lizards, taking into consideration how the distribution of each morph gradually changes as time goes by. Such lizard’s analysis with three morphs connected with the particular mating strategies [1]. The strategy takes into… Read more: A Dynamic Polymorphic Population based Algorithm: Side – Blotched Lizard Algorithm (SBLA) a reliable Algorithm to use in Real – World Problems
- DEER HUNTING OPTIMIZATION (DHO) ALGORITHM: HUNTING BEHAVIOR OF HUMANS TOWARD DEER1. INTRODUCTION A novel meta-heuristic algorithm, named DHOA, proposed by Brammya et al. in 2019 which is inspired by the hunting behavior of humans toward deer. Even though the activities of the hunters differ, the way of attacking the buck/deer is based on the hunting strategy they develop. Due to the special abilities of deer,… Read more: DEER HUNTING OPTIMIZATION (DHO) ALGORITHM: HUNTING BEHAVIOR OF HUMANS TOWARD DEER
- Group Teaching Optimization Algorithm (GTOA): Inspired by group teaching mechanism for the application of solving global optimization problems1. Introduction Group Teaching Optimization Algorithm (GTOA) is aimed at improving the knowledge of the whole class by simulating the group teaching mechanism. Group teaching is a teaching approach widely applied in many educational programmers on an international level [1]. It takes various formats as a teaching method, such as ability grouping, mixed-ability grouping, mixed-age grouping etc. Education aims to… Read more: Group Teaching Optimization Algorithm (GTOA): Inspired by group teaching mechanism for the application of solving global optimization problems
- Sparrow Search Algorithm (SSA): A Swarm Intelligence Optimization Algorithm for the Application to Solve Practical Engineering Examples1. Introduction The sparrow search algorithm (SSA) is an effective optimization technique, which simulates the group wisdom foraging and anti-predation behaviors of sparrows. Searching is the process which is to look into or over carefully or thoroughly in an effort to find or discover something [1]. In sparrow search is the simple act of gathering… Read more: Sparrow Search Algorithm (SSA): A Swarm Intelligence Optimization Algorithm for the Application to Solve Practical Engineering Examples
- Sunflower optimization algorithm (SFO): population-based nature inspired algorithm mimicking the sunflowers movement towards the sun1. Introduction The SFO is a new meta-heuristic algorithm which is stimulated by the moving of sunflowers towards the sunlight by considering the pollination between adjacent sunflowers. Also, the SFO is stimulated by the inverse square law radiation and it is a technique is a population-based iterative heuristic global [1] optimization algorithm for multi-modal problems.… Read more: Sunflower optimization algorithm (SFO): population-based nature inspired algorithm mimicking the sunflowers movement towards the sun
- Inspired from dynamic behavior of bear based on sense of smell mechanism: Bear Smell Search Algorithm (BSSA)Introduction The bear smell search algorithm (BSSA) imitates both dynamic behaviors of bear based on sense of smell mechanism and the way bear moves in the search of food in thousand miles farther. According to the comprehensive study of animal’s behavior and their senses in nature it can be concluded that the bear’s sense of… Read more: Inspired from dynamic behavior of bear based on sense of smell mechanism: Bear Smell Search Algorithm (BSSA)
- A Bio Inspired Algorithm: Emperor Penguin Optimizer1.Introduction The Emperor Penguin Optimizer Algorithm (EPO) is based on the huddling behavior of emperor penguins. The main process of EPO is to generate the huddle boundary, compute temperature around the huddle, calculate the distance, and ﬁnd the eﬀective mover. These steps are mathematically modeled and implemented on 44 well-known benchmark test functions [1]. Emperor… Read more: A Bio Inspired Algorithm: Emperor Penguin Optimizer
- Black Widow Optimization (BWO) Algorithm: Mating Behaviour of Black Widow Spider for Solving Engineering Optimization Problems1. Introduction A new metaheuristic optimization algorithm based on mating behavior of the black widow spiders, first proposed by V. Hayyolalam and A. Pourhaji Kazem in 2020, has been used to solve different engineering and scientific problems owing to their easiness and flexibility. The Black Widow Optimization Algorithm (BWO) is inspired by the unique mating… Read more: Black Widow Optimization (BWO) Algorithm: Mating Behaviour of Black Widow Spider for Solving Engineering Optimization Problems
- A New Optimization Algorithm Inspired from the Mating Behavior of Barnacles: Barnacles Mating Optimizer Algorithms (BMO)1. Introduction Optimization is the process of finding the best combination of variables or parameters that fulfill the constraints to achieve the objective function whether for minimization or maximization purposes. The objective function is normally formulated based on applications or problems to be solved and it can be in terms of cost, efficiency, profits etc.… Read more: A New Optimization Algorithm Inspired from the Mating Behavior of Barnacles: Barnacles Mating Optimizer Algorithms (BMO)
- An Efficient Harris Hawks Optimization (HHO) Algorithm for Solving Numerical Expressions1. Introduction The Harris hawks optimization (HHO) algorithm is a new swarm intelligence optimization paradigm proposed by Ali Asghar Heidari et al. in 2019, which is inspired by the team behaviors and chasing patterns of Harris’s hawk in nature called surprise pounce [1] The Harris’s hawk is notable for its behavior of hunting cooperatively… Read more: An Efficient Harris Hawks Optimization (HHO) Algorithm for Solving Numerical Expressions
- Honey Bee Mating Optimization (HBMO) Algorithm: Propelled Behavior of Bees Mating for Solving Optimization Problems1. Introduction A new optimization algorithm based on honey bee mating, first proposed by Afshar et al. (2007), has been used to solve difficult optimization problems such as optimal reservoir operation (Haddad et al, 2007) and clustering (Fathian et al. 2007) [1]. Honey Bee mating Optimization Algorithm (HBMO) under different context by various researchers… Read more: Honey Bee Mating Optimization (HBMO) Algorithm: Propelled Behavior of Bees Mating for Solving Optimization Problems
- Flower Pollination Algorithm (FPA): A Novel Method Motivated from the Behavior of Flowers for Optimal Solution1. Introduction Flower Pollination Algorithm (FPA) is a nature inspired population based algorithm proposed by Xin-She Yang (2012). The main objective of the flower pollination is to produce the optimal reproduction of plants by surviving the fittest flowers in the flowering plants. In fact this is an optimization process of plants in species. FPA… Read more: Flower Pollination Algorithm (FPA): A Novel Method Motivated from the Behavior of Flowers for Optimal Solution
- Behavior of Artificial Feeding Birds (AFB) using Meta-heuristics method1. Introduction Artificial Feeding Birds (AFB), a new metaheuristic inspired by the very trivial behavior of birds searching for food. AFB is very simple, yet efficient, and can be easily adapted to various optimization problems. Many algorithms have been inspired by nature [1]. More recently, researchers inspired themselves from the behavior of animals for… Read more: Behavior of Artificial Feeding Birds (AFB) using Meta-heuristics method
- Dolphin Echolocation Algorithm (DEA): A Novel Method Motivated from the Behavior of Dolphin for Optimal Solution1. Introduction Dolphin Echolocation Algorithm is mimics strategies used by dolphins for their hunting process. Dolphins produce a kind of voice called sonar to locate the target, doing this dolphin change sonar to modify the target and its location. In order to implement the optimization task an improved dolphin echolocation meta-heuristic algorithm is proposed.… Read more: Dolphin Echolocation Algorithm (DEA): A Novel Method Motivated from the Behavior of Dolphin for Optimal Solution
- Sailfish Optimizer (SFO): A Novel Method Motivated from the Behavior of Sailfish for Optimal Solution1. Introduction Optimization can be used essentially in a variety of fields from engineering design to economics or holiday planning to Internet routing. Metaheuristic algorithms can provide appropriate technique to solve optimization problems through mathematical modeling of social political evolution. These algorithms are using methods that find solutions close to the optimum with an… Read more: Sailfish Optimizer (SFO): A Novel Method Motivated from the Behavior of Sailfish for Optimal Solution
- Cuttlefish Algorithm (CFA): Propelled from Color Changing Behavior of Cuttlefishes for Solving Optimization Problems1. 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… Read more: Cuttlefish Algorithm (CFA): Propelled from Color Changing Behavior of Cuttlefishes for Solving Optimization Problems
- A Novel Numerical Optimization Algorithm Inspired from Salps: Salp Swarm Optimization Algorithm1. 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… Read more: A Novel Numerical Optimization Algorithm Inspired from Salps: Salp Swarm Optimization Algorithm
- Krill Herd (KH) Algorithm to solve Numerical Optimization Problem1. 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… Read more: Krill Herd (KH) Algorithm to solve Numerical Optimization Problem
- Owl Search Algorithm: A Novel Nature Inspired Metaheuristic Method1. 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… Read more: Owl Search Algorithm: A Novel Nature Inspired Metaheuristic Method
- Behavior of Mouth Brooding Fish Algorithm (MBF) using Meta-heuristics method1. 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… Read more: Behavior of Mouth Brooding Fish Algorithm (MBF) using Meta-heuristics method
- Spotted Hyena Optimization (SHO) Algorithm: Mimicked from Hunting Behavior of Hyena1. 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… Read more: Spotted Hyena Optimization (SHO) Algorithm: Mimicked from Hunting Behavior of Hyena
- Behavior of Squirrel Search Algorithm (SSA) using Meta-heuristics method1. 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… Read more: Behavior of Squirrel Search Algorithm (SSA) using Meta-heuristics method
- A New Seagull Optimization Algorithm to Solve Numerical Optimization Problems1. 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… Read more: A New Seagull Optimization Algorithm to Solve Numerical Optimization Problems
- Pigeon Inspired Optimization (PIO) Algorithm: A Novel method motivated from the behavior of Pigeons for Optimal Solution1. 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… Read more: Pigeon Inspired Optimization (PIO) Algorithm: A Novel method motivated from the behavior of Pigeons for Optimal Solution
- A Bumble Bees Mating Optimization (BBMO) Algorithm to solve Numerical Optimization Problems1. 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… Read more: A Bumble Bees Mating Optimization (BBMO) Algorithm to solve Numerical Optimization Problems
- Dragonfly Algorithm (DA) to solve Numerical Optimization Problem1. 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… Read more: Dragonfly Algorithm (DA) to solve Numerical Optimization Problem
- New Nature Inspired Metaheuristic Algorithm for Elephants: Elephant Herding Optimization (EHO) Algorithm1. 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,… Read more: New Nature Inspired Metaheuristic Algorithm for Elephants: Elephant Herding Optimization (EHO) Algorithm
- Behavior of Grey Wolf Optimization (GWO) Algorithm using Meta-heuristics method1. 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… Read more: Behavior of Grey Wolf Optimization (GWO) Algorithm using Meta-heuristics method
- Ant Colony Optimization (ACO) Algorithm to solve Numericcal Optimization Problem1. 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… Read more: Ant Colony Optimization (ACO) Algorithm to solve Numericcal Optimization Problem
- Artificial Bee Colony (ABC) Algorithm: A Novel Method Motivated from the Behavior of Bees for Optimal Solution1. 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… Read more: Artificial Bee Colony (ABC) Algorithm: A Novel Method Motivated from the Behavior of Bees for Optimal Solution
- An Efficient Moth Flame Optimization (MFO) Algorithm for Solving Numerical Expressions1. 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… Read more: An Efficient Moth Flame Optimization (MFO) Algorithm for Solving Numerical Expressions
- Bacterial Foraging Optimization Algorithm (BFOA): Inspired from Social foraging behavior of Escherichia coli1. 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… Read more: Bacterial Foraging Optimization Algorithm (BFOA): Inspired from Social foraging behavior of Escherichia coli
- Grasshopper Optimization Algorithm (GOA): Propelled from Swarming Behavior of Grasshoppers for Solving Optimization Problems1. Introduction Grasshopper Optimization Algorithm (GOA) is an optimization technique introduced by Saremi, Mirjalili and Lewis in 2017. It includes both social interaction between ordinary agents (grasshoppers) and the attraction of the best individual. Initial experiments performed by authors demonstrated promising exploration abilities of the GOA and they will be further examined in the… Read more: Grasshopper Optimization Algorithm (GOA): Propelled from Swarming Behavior of Grasshoppers for Solving Optimization Problems
- New 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… Read more: New Nature Inspired Metaheuristic Algorithm for Bats: Bat Search Algorithm (BSA)
- Cuckoo’s Search Algorithm to solve Structural optimization problem1. Introduction Cuckoo search is an optimization algorithm developed by Xin-she Yang and Suash Deb in 2009. It was inspired by the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of other host birds (of other species)[1]. Some host birds can engage direct conflict with the intruding cuckoos.… Read more: Cuckoo’s Search Algorithm to solve Structural optimization problem
- Gravitational Search Algorithm (GSA) in view of Newton’s Law of Gravitation1. Introduction Gravitational Search Algorithm (GSA) is a population search algorithm proposed by Rashedi et al. in 2009. A gravitational search algorithm is based on the law of gravity and the notion of mass interactions. The GSA algorithm uses the theory of Newtonian physics and its searcher agents are the collection of masses. In… Read more: Gravitational Search Algorithm (GSA) in view of Newton’s Law of Gravitation
- Firefly Algorithm (FA): A Novel method motivated from the behavior of Fireflies for Optimal Solution1. Introduction Firefly algorithm (FA) was first developed by Yang in 2007 (Yang, 2008, 2009) which was based on the flashing patterns and behavior of fireflies. Firefly algorithm is classified as swarm intelligent, metaheuristic and nature-inspired, and it is developed by Yang in 2008 by animating the characteristic behaviors of fireflies [1]. In fact,… Read more: Firefly Algorithm (FA): A Novel method motivated from the behavior of Fireflies for Optimal Solution
- Behavior of FFO using Meta-heuristics method1. Introduction Fruit fly Optimization Algorithm has been invented by Pan in 2011 and it is based on the food search behavior of fruit flies. Fruit fly optimization (FFO) is one of the latest meta-heuristic methods presented in the literature. FFO simulates the intelligent foraging behavior of fruit flies or vinegar flies in finding… Read more: Behavior of FFO using Meta-heuristics method
- A Novel Numerical Optimization Algorithm Inspired from Particles: Particle Swarm Optimization1. Introduction Particle swarm optimization (PSO) is a population based stochastic optimization technique developed by Dr.Eberhart and Dr.Kennedy in 1995,inspired by social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms(GA).Incomputational science,particle swarm optimization (PSO) [1] is a computational method thatoptimizes a problem by iteratively… Read more: A Novel Numerical Optimization Algorithm Inspired from Particles: Particle Swarm Optimization
- Theory and Applications of Genetic Algorithms: Darwin’s Theory of Evolutions1. Introduction Genetic Algorithm (GA) is developed in 1975 by Prof. John Holland was inspired by Darwin’s theory of evolutions which states that the survival of an organism is affected by rule “the strongest species that survives”. Darwin also stated that the survival of an organism can be maintained through the process of reproduction,… Read more: Theory and Applications of Genetic Algorithms: Darwin’s Theory of Evolutions