scholarly journals The evolutionary maintenance of Lévy flight foraging: a numerical simulation.

2021 ◽  
Author(s):  
Winston John Neil Armstrong Campeau ◽  
Andrew M Simons ◽  
Brett Stevens
Keyword(s):  
Geometric Mean ◽  
Academic Disciplines ◽  
Theoretical Research ◽  
Human Populations ◽  
Lévy Flight ◽  
Slime Molds ◽  
Levy Flight ◽  
Ample Evidence ◽  
Lévy Walks ◽  
Extant Species

Lévy flight is a type of random walk that models the behaviour of many phenomena across a multiplicity of academic disciplines; within biology specifically, the behaviour of fish, birds, insects, mollusks, bacteria, plants, slime molds, t-cells, and human populations. The Lévy flight foraging hypothesis states that because Lévy flights can maximize an organism’s search efficiency, natural selection should result in Lévy-like behaviour. Empirical and theoretical research has provided ample evidence of Lévy walks in both extinct and extant species, and its efficiency across models with a diversity of resource distributions. However, no model has addressed the maintenance of Lévy flight foraging through evolutionary processes, and existing models lack ecological breadth. We use numerical simulations, including lineage-based models of evolution with a distribution of move lengths as a variable and heritable trait, to test the Lévy flight foraging hypothesis. We include biological and ecological contexts such as population size, searching costs, lifespan, resource distribution, speed, and consider both energy accumulated at the end of a lifespan and averaged over a lifespan. We demonstrate that selection often results in Lévy-like behaviour, although conditional; smaller populations, longer searches, and low searching costs increase the fitness of Lévy-like behaviour relative to Brownian behaviour. Interestingly, our results also evidence a bet-hedging strategy; Lévy-like behaviour reduces fitness variance, thus maximizing geometric mean fitness over multiple generations.

scholarly journals Extending Lévy search theory from one to higher dimensions: Lévy walking favours the blind

2015 ◽  
Vol 471 (2179) ◽  
pp. 20150123 ◽  
Author(s):  
A. M. Reynolds
Keyword(s):  
T Cells ◽  
Three Dimensional ◽  
Search Theory ◽  
Three Dimensions ◽  
Lévy Flight ◽  
Lévy Flights ◽  
Levy Flight ◽  
Diverse Range ◽  
Lévy Walks ◽  
Levy Flights

A diverse range of organisms, including T cells, E. coli , honeybees, sharks, turtles, bony fish, jellyfish, wandering albatrosses and even human hunter–gatherers have movement patterns that can be approximated by Lévy walks (LW; sometimes called Lévy flights in the biological and ecological literature). These observations lend support to the ‘Lévy flight foraging hypothesis’ which asserts that natural selection should have led to adaptations for Lévy flight foraging, because Lévy flights can optimize search efficiencies. The hypothesis stems from a rigorous theory of one-dimensional searching and from simulation data for two-dimensional searching. The potential effectiveness of three-dimensional Lévy searches has not been examined but is central to a proper understanding of marine predators and T cells which have provided the most compelling empirical evidence for LW. Here I extend Lévy search theory from one to three dimensions. The new theory predicts that three-dimensional Lévy searching can be advantageous but only when targets are large compared with the perceptual range of the searchers, i.e. only when foragers are effectively blind and need to come into contact with a target to establish its presence. This may explain why effective blindness is a common factor among three-dimensional Lévy walkers.

Feature Selection for Histopathological Image Classification using levy Flight Salp Swarm Optimizer

2019 ◽  
Vol 12 (4) ◽  
pp. 329-337 ◽  
Author(s):  
Venubabu Rachapudi ◽  
Golagani Lavanya Devi
Keyword(s):  
Feature Selection ◽  
Image Classification ◽  
Feature Selection Method ◽  
Selection Method ◽  
Lévy Flight ◽  
Levy Flight ◽  
Local Optima ◽  
Histopathological Image ◽  
Surf Features ◽  
Histopathological Image Classification

Background: An efficient feature selection method for Histopathological image classification plays an important role to eliminate irrelevant and redundant features. Therefore, this paper proposes a new levy flight salp swarm optimizer based feature selection method. Methods: The proposed levy flight salp swarm optimizer based feature selection method uses the levy flight steps for each follower salp to deviate them from local optima. The best solution returns the relevant and non-redundant features, which are fed to different classifiers for efficient and robust image classification. Results: The efficiency of the proposed levy flight salp swarm optimizer has been verified on 20 benchmark functions. The anticipated scheme beats the other considered meta-heuristic approaches. Furthermore, the anticipated feature selection method has shown better reduction in SURF features than other considered methods and performed well for histopathological image classification. Conclusion: This paper proposes an efficient levy flight salp Swarm Optimizer by modifying the step size of follower salp. The proposed modification reduces the chances of sticking into local optima. Furthermore, levy flight salp Swarm Optimizer has been utilized in the selection of optimum features from SURF features for the histopathological image classification. The simulation results validate that proposed method provides optimal values and high classification performance in comparison to other methods.

Salp swarm algorithm with crossover scheme and Lévy flight for global optimization

2021 ◽  
pp. 1-12
Author(s):  
Heming Jia ◽  
Chunbo Lang
Keyword(s):  
Global Optimization ◽  
Heuristic Algorithm ◽  
Experimental Results ◽  
High Dimensional ◽  
Lévy Flight ◽  
Test Functions ◽  
Levy Flight ◽  
Salp Swarm Algorithm ◽  
Swarm Algorithm ◽  
Accuracy And Stability

Salp swarm algorithm (SSA) is a meta-heuristic algorithm proposed in recent years, which shows certain advantages in solving some optimization tasks. However, with the increasing difficulty of solving the problem (e.g. multi-modal, high-dimensional), the convergence accuracy and stability of SSA algorithm decrease. In order to overcome the drawbacks, salp swarm algorithm with crossover scheme and Lévy flight (SSACL) is proposed. The crossover scheme and Lévy flight strategy are used to improve the movement patterns of salp leader and followers, respectively. Experiments have been conducted on various test functions, including unimodal, multimodal, and composite functions. The experimental results indicate that the proposed SSACL algorithm outperforms other advanced algorithms in terms of precision, stability, and efficiency. Furthermore, the Wilcoxon’s rank sum test illustrates the advantages of proposed method in a statistical and meaningful way.

Optimal placement of distributed generation based on DISCO’s additional benefit using self adaptive levy flight based black widow optimization

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Naga Lakshmi Gubbala Venkata ◽  
Jaya Laxmi Askani ◽  
Venkataramana Veeramsetty
Keyword(s):  
Distributed Generation ◽  
Distribution Network ◽  
Additional Benefit ◽  
Operating Conditions ◽  
Optimal Placement ◽  
Lévy Flight ◽  
Black Widow ◽  
Levy Flight ◽  
Simulation Performance ◽  
Self Adaptive

Abstract Optimal placement of Distributed Generation (DG) is a crucial challenge for Distribution Companies (DISCO’s) to run the distribution network in good operating conditions. Optimal positioning of DG units is an optimization issue where maximization of DISCO’s additional benefit due to the installation of DG units in the network is considered to be an objective function. In this article, the self adaptive levy flight based black widow optimization algorithm is used as an optimization strategy to find the optimum position and size of the DG units. The proposed algorithm is implemented in the IEEE 15 and PG & E 69 bus management systems in the MATLAB environment. Based on the simulation performance, it has been found that with the correct location and size of the DG modules, the distribution network can be run with maximum DISCO’s additional benefit.

scholarly journals Modified Levy Flight Optimization for a Maximum Power Point Tracking Algorithm under Partial Shading

2021 ◽  
Vol 11 (3) ◽  
pp. 992
Author(s):  
Chanuri Charin ◽  
Dahaman Ishak ◽  
Muhammad Ammirrul Atiqi Mohd Zainuri ◽  
Baharuddin Ismail
Keyword(s):  
High Efficiency ◽  
Energy System ◽  
Fast Response ◽  
Lévy Flight ◽  
Partial Shading ◽  
Levy Flight ◽  
Point Tracking ◽  
Power Point Tracking ◽  
Steady State Oscillation ◽  
Power Point

This paper presents a novel modified Levy flight optimization for a photovoltaic PV solar energy system. Conventionally, the Perturb and Observe (P&O) algorithm has been widely deployed in most applications due to its simplicity and ease of implementation. However, P&O suffers from steady-state oscillation and stability, besides its failure in tracking the optimum power under partial shading conditions and fast irradiance changes. Therefore, a modified Levy flight optimization is proposed by incorporating a global search of beta parameters, which can significantly improve the tracking capability in local and global searches compared to the conventional methods. The proposed modified Levy flight optimization is verified with simulations and experiments under uniform, non-uniform, and dynamic conditions. All results prove the advantages of the proposed modified Levy flight optimization in extracting the optimal power with a fast response and high efficiency from the PV arrays.

Design and application of an optimally tuned PID controller for DC motor speed regulation via a novel hybrid Lévy flight distribution and Nelder–Mead algorithm

2021 ◽  
pp. 014233122110196
Author(s):  
Davut Izci
Keyword(s):  
Pid Controller ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Absolute Error ◽  
Dc Motor ◽  
Cuckoo Search Algorithm ◽  
Lévy Flight ◽  
Motor Speed ◽  
Levy Flight ◽  
Speed Regulation

This paper deals with the design of an optimally performed proportional–integral–derivative (PID) controller utilized for speed control of a direct current (DC) motor. To do so, a novel hybrid algorithm was proposed which employs a recent metaheuristic approach, named Lévy flight distribution (LFD) algorithm, and a simplex search method known as Nelder–Mead (NM) algorithm. The proposed algorithm (LFDNM) combines both LFD and NM algorithms in such a way that the good explorative behaviour of LFD and excellent local search capability of NM help to form a novel hybridized version that is well balanced in terms of exploration and exploitation. The promise of the proposed structure was observed through employment of a DC motor with PID controller. Optimum values for PID gains were obtained with the aid of an integral of time multiplied absolute error objective function. To verify the effectiveness of the proposed algorithm, comparative simulations were carried out using cuckoo search algorithm, genetic algorithm and original LFD algorithm. The system behaviour was assessed through analysing the results for statistical and non-parametric tests, transient and frequency responses, robustness, load disturbance, energy and maximum control signals. The respective evaluations showed better performance of the proposed approach. In addition, the better performance of the proposed approach was also demonstrated through experimental verification. Further evaluation to demonstrate better capability was performed by comparing the LFDNM-based PID controller with other state-of-the-art algorithms-based PID controllers with the same system parameters, which have also confirmed the superiority of the proposed approach.

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