Related papers: Fuzzy Hunter Optimizer: An Bio-Metaheuristic Algor…
The paper proposes a novel nature-inspired technique of optimization. It mimics the perching nature of eagles and uses mathematical formulations to introduce a new addition to metaheuristic algorithms. The nature of the proposed algorithm…
Many studies on animal and human movement patterns report the existence of scaling laws and power-law distributions. Whereas a number of random walk models have been proposed to explain observations, in many situations individuals actually…
This discusses a case study on Fitness Dependent Optimizer or so-called FDO and adapting its parameters to the Internet of Things (IoT) healthcare. The reproductive way is sparked by the bee swarm and the collaborative decision-making of…
Metaheuristic algorithms such as particle swarm optimization, firefly algorithm and harmony search are now becoming powerful methods for solving many tough optimization problems. In this paper, we propose a new metaheuristic method, the Bat…
Nature-inspired metaheuristic algorithms are important components of artificial intelligence, and are increasingly used across disciplines to tackle various types of challenging optimization problems. This paper demonstrates the usefulness…
In order to better understand and analyze the currently widely used population-based metaheuristic optimization algorithms, , this paper proposes a novel computational intelligence algorithm called bare bones grey wolf optimizer (BBGWO)…
Grey wolf optimizer (GWO) is a nature-inspired stochastic meta-heuristic of the swarm intelligence field that mimics the hunting behavior of grey wolves. Differential evolution (DE) is a popular stochastic algorithm of the evolutionary…
The foraging behavior of animals is a paradigm of target search in nature. Understanding which foraging strategies are optimal and how animals learn them are central challenges in modeling animal foraging. While the question of optimality…
In this study, we present a novel hybrid algorithm, combining Levy Flight (LF) and Particle Swarm Optimization (PSO) (LF-PSO), tailored for efficient multi-robot exploration in unknown environments with limited communication and no global…
Recent analysis of empirical data [F. Radicchi, A. Baronchelli & L.A.N. Amaral. PloS ONE 7, e029910 (2012)] showed that humans adopt L\'evy flight strategies when exploring the bid space in on-line auctions. A game theoretical model proved…
This paper proposes the multi objective variant of the recently introduced fitness dependent optimizer (FDO). The algorithm is called a Multi objective Fitness Dependent Optimizer (MOFDO) and is equipped with all five types of knowledge…
This book chapter introduces to the problem to which extent search strategies of foraging biological organisms can be identified by statistical data analysis and mathematical modeling. A famous paradigm in this field is the Levy Flight…
Several adaptation techniques have been investigated to optimize fuzzy inference systems. Neural network learning algorithms have been used to determine the parameters of fuzzy inference system. Such models are often called as integrated…
In this paper, a novel bio-inspired optimization algorithm is proposed, called Bombardier Beetle Optimizer (BBO). This type of species is very intelligent, which has an ability to defense and escape from predators. The principles of the…
This paper proposes a novel nature-inspired meta-heuristic algorithm called the Golden Tortoise Beetle Optimizer (GTBO) to solve optimization problems. It mimics golden tortoise beetle's behavior of changing colors to attract opposite sex…
With the widespread application of Unmanned Aerial Vehicles (UAVs) in domains like military reconnaissance, emergency rescue, and logistics delivery, efficiently planning the shortest flight path has become a critical challenge. Traditional…
The Grey Wolf Optimizer (GWO) is a swarm intelligence meta-heuristic algorithm inspired by the hunting behaviour and social hierarchy of grey wolves in nature. This paper analyses the use of chaos theory in this algorithm to improve its…
Levy flights are known to be optimal search strategies in the particular case of revisitable targets. In the relevant situation of non revisitable targets, we propose an alternative model of bidimensional search processes, which explicitly…
Optimization aims at selecting a feasible set of parameters in an attempt to solve a particular problem, being applied in a wide range of applications, such as operations research, machine learning fine-tuning, and control engineering,…
Due to the fast-growing volume of text documents and reviews in recent years, current analyzing techniques are not competent enough to meet the users' needs. Using feature selection techniques not only support to understand data better but…