scholarly journals Improved Biogeography-Based Optimization Algorithm by Hierarchical Tissue-Like P System with Triggering Ablation Rules

2021 ◽  
Vol 2021 ◽  
pp. 1-24
Author(s):  
Xiao Sang ◽  
Xiyu Liu ◽  
Zhe Zhang ◽  
Lin Wang
Keyword(s):  
Computational Models ◽  
Membrane Computing ◽  
Optimization Problems ◽  
Natural Computing ◽  
P System ◽  
Cell Groups ◽  
Speed Up ◽  
Gaussian Mutation Operator ◽  
And Function ◽  
Metaheuristic Optimization Algorithms

BBO is one of the new metaheuristic optimization algorithms, which is based on the science of biogeography. It can be used to solve optimization problems through the migration and drift of species between habitats. Many improved BBO algorithms have been proposed, but there were still many shortcomings in global optimization, convergence speed, and algorithm complexity. In response to the above problems, this paper proposes an improved BBO algorithm (DCGBBO) by hierarchical tissue-like P system with triggering ablation rules. Membrane computing is a branch of natural computing that aims to abstract computational models (P system) from the structure and function of biological cells and from the collaboration of cell groups such as organs and tissues. In this paper, firstly, a dynamic crossover migration operator is generated to improve the global search ability and also increase the species diversity. Secondly, a dynamic Gaussian mutation operator is introduced to speed up convergence and improve local search capabilities. To guarantee the correctness and feasibility of the mutation, a unified maximum mutation rate is designed. Finally, a hierarchical tissue-like P system with triggering ablation rules is combined with the DCGBBO algorithm, making use of evolution rules and communication rules to achieve migration and mutation of solutions and reduce computational complexity. In the experiments, eight classic benchmark functions and CEC 2017 benchmark functions are applied to demonstrate the effect of our algorithm. We apply the proposed algorithm to segment four colour pictures, and the results proved to be better compared to other algorithms.

scholarly journals A P-Based Hybrid Evolutionary Algorithm for Vehicle Routing Problem with Time Windows

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Yunyun Niu ◽  
Juanjuan He ◽  
Zhongyu Wang ◽  
Jianhua Xiao
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Membrane Computing ◽  
Optimization Problems ◽  
Time Windows ◽  
Genetic Operators ◽  
Improvement Strategy ◽  
Solution Quality ◽  
Routing Problem ◽  
Speed Up

The ability to solve optimization problems using membrane algorithms is an important application of membrane computing. This work combines membrane systems and genetic operators to build an approximated algorithm for the vehicle routing problem with time windows. The algorithm is based on a tissue-like membrane structure combined with cell separation rules and communication rules; during such processes membranes collect and disperse information. Genetic operators are used as the system's subalgorithms. We also design a special improvement strategy to speed up the search process in subsystems. The experimental results show that the solution quality from the proposed algorithm is competitive with other heuristic or metaheuristic algorithms in the literature.

scholarly journals A Survey of Nature-Inspired Computing

2021 ◽  
Vol 54 (1) ◽  
pp. 1-31
Author(s):  
Bosheng Song ◽  
Kenli Li ◽  
David Orellana-Martín ◽  
Mario J. Pérez-Jiménez ◽  
Ignacio PéRez-Hurtado
Keyword(s):  
Membrane Computing ◽  
P Systems ◽  
Natural Computing ◽  
P System ◽  
Open Problems ◽  
Membrane Function ◽  
Natural Systems ◽  
Computing Paradigm ◽  
Complete Problems ◽  
Nature Inspired Computing

Nature-inspired computing is a type of human-designed computing motivated by nature, which is based on the employ of paradigms, mechanisms, and principles underlying natural systems. In this article, a versatile and vigorous bio-inspired branch of natural computing, named membrane computing is discussed. This computing paradigm is aroused by the internal membrane function and the structure of biological cells. We first introduce some basic concepts and formalisms of membrane computing, and then some basic types or variants of P systems (also named membrane systems ) are presented. The state-of-the-art computability theory and a pioneering computational complexity theory are presented with P system frameworks and numerous solutions to hard computational problems (especially NP -complete problems) via P systems with membrane division are reported. Finally, a number of applications and open problems of P systems are briefly described.

scholarly journals Cell-specific expression and individual function of prohormone convertase PC1/3 in Tribolium larval growth highlights major evolutionary changes between beetle and fly neuroendocrine systems

EvoDevo ◽  
2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Sonja Fritzsche ◽  
Vera S. Hunnekuhl
Keyword(s):  
Larval Growth ◽  
Neuroendocrine System ◽  
Small Subset ◽  
Specific Cell ◽  
Specific Expression ◽  
Individual Function ◽  
Evolutionary Changes ◽  
Cell Groups ◽  
Cell Type Specific Expression ◽  
And Function

Abstract Background The insect neuroendocrine system acts in the regulation of physiology, development and growth. Molecular evolution of this system hence has the potential to allow for major biological differences between insect groups. Two prohormone convertases, PC1/3 and PC2, are found in animals and both function in the processing of neuropeptide precursors in the vertebrate neurosecretory pathway. Whereas PC2-function is conserved between the fly Drosophila and vertebrates, ancestral PC1/3 was lost in the fly lineage and has not been functionally studied in any protostome. Results In order to understand its original functions and the changes accompanying the gene loss in the fly, we investigated PC1/3 and PC2 expression and function in the beetle Tribolium castaneum. We found that PC2 is broadly expressed in the nervous system, whereas surprisingly, PC1/3 expression is restricted to specific cell groups in the posterior brain and suboesophageal ganglion. Both proteases have parallel but non-redundant functions in adult beetles’ viability and fertility. Female infertility following RNAi is caused by a failure to deposit sufficient yolk to the developing oocytes. Larval RNAi against PC2 produced moulting defects where the larvae were not able to shed their old cuticle. This ecdysis phenotype was also observed in a small subset of PC1/3 knockdown larvae and was strongest in a double knockdown. Unexpectedly, most PC1/3-RNAi larvae showed strongly reduced growth, but went through larval moults despite minimal to zero weight gain. Conclusions The cell type-specific expression of PC1/3 and its essential requirement for larval growth highlight the important role of this gene within the insect neuroendocrine system. Genomic conservation in most insect groups suggests that it has a comparable individual function in other insects as well, which has been replaced by alternative mechanisms in flies.

Tail length evolution in deer mice: linking morphology, behavior and function

2021 ◽  
Author(s):  
Emily R Hager ◽  
Hopi E Hoekstra
Keyword(s):  
Computational Models ◽  
Deer Mouse ◽  
Center Of Mass ◽  
Tail Length ◽  
Single Species ◽  
Deer Mice ◽  
Length Variation ◽  
Caudal Vertebrae ◽  
Long Tails ◽  
And Function

Abstract Determining how variation in morphology affects animal performance (and ultimately fitness) is key to understanding the complete process of evolutionary adaptation. Long tails have evolved many times in arboreal and semi-arboreal rodents; in deer mice, long tails have evolved repeatedly in populations occupying forested habit even within a single species (Peromyscus maniculatus). Here we use a combination of functional modeling, laboratory studies, and museum records to test hypotheses about the function of tail-length variation in deer mice. First, we use computational models, informed by museum records documenting natural variation in tail length, to test whether differences in tail morphology between forest and prairie subspecies can influence performance in behavioral contexts relevant for tail use. We find that the deer mouse tail plays little role in statically adjusting center of mass or in correcting body pitch and yaw, but rather it can affect body roll during arboreal locomotion. In this context, we find that even intraspecific tail-length variation could result in substantial differences in how much body rotation results from equivalent tail motions (i.e., tail effectiveness), but the relationship between commonly-used metrics of tail-length variation and effectiveness is non-linear. We further test whether caudal vertebra length, number, and shape are associated with differences in how much the tail can bend to curve around narrow substrates (i.e., tail curvature) and find that, as predicted, the shape of the caudal vertebrae is associated with intervertebral bending angle across taxa. However, although forest and prairie mice typically differ in both the length and number of caudal vertebrae, we do not find evidence that this pattern is the result of a functional trade-off related to tail curvature. Together, these results highlight how even simple models can both generate and exclude hypotheses about the functional consequences of trait variation for organismal-level performance.

Opposition-based learning Multi-Verse Optimizer with disruption operator for optimization problems

2021 ◽  
Author(s):  
Mohammad Shehab ◽  
Laith Abualigah
Keyword(s):  
Real World ◽  
Optimization Problems ◽  
Second Stage ◽  
Opposition Based Learning ◽  
High Fitness ◽  
Fitness Value ◽  
Speed Up ◽  
Disruption Operator ◽  
The Given ◽  
Real World Problems

Abstract Multi-Verse Optimizer (MVO) algorithm is one of the recent metaheuristic algorithms used to solve various problems in different fields. However, MVO suffers from a lack of diversity which may trapping of local minima, and premature convergence. This paper introduces two steps of improving the basic MVO algorithm. The first step using Opposition-based learning (OBL) in MVO, called OMVO. The OBL aids to speed up the searching and improving the learning technique for selecting a better generation of candidate solutions of basic MVO. The second stage, called OMVOD, combines the disturbance operator (DO) and OMVO to improve the consistency of the chosen solution by providing a chance to solve the given problem with a high fitness value and increase diversity. To test the performance of the proposed models, fifteen CEC 2015 benchmark functions problems, thirty CEC 2017 benchmark functions problems, and seven CEC 2011 real-world problems were used in both phases of the enhancement. The second step, known as OMVOD, incorporates the disruption operator (DO) and OMVO to improve the accuracy of the chosen solution by giving a chance to solve the given problem with a high fitness value while also increasing variety. Fifteen CEC 2015 benchmark functions problems, thirty CEC 2017 benchmark functions problems and seven CEC 2011 real-world problems were used in both phases of the upgrade to assess the accuracy of the proposed models.

scholarly journals Parallelization of computational algorithms for optimization problems with high time consumption

2018 ◽  
Vol 157 ◽  
pp. 02054 ◽  
Author(s):  
Milan Vaško ◽  
Marián Handrik ◽  
Alžbeta Sapietová ◽  
Jana Handriková
Keyword(s):  
Computational Models ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Fe Analysis ◽  
Function Evaluation ◽  
Computational Algorithms ◽  
Distributed Computing Systems ◽  
Computing Systems ◽  
Time Consumption ◽  
Design Variables

The paper presents an analysis of the use of optimization algorithms in parallel solutions and distributed computing systems. The primary goal is to use evolutionary algorithms and their implementation into parallel calculations. Parallelization of computational algorithms is suitable for the following cases - computational models with a large number of design variables or cases where the objective function evaluation is time consuming (FE analysis). As the software platform for application of distributed optimization algorithms is using MATLAB and BOINC software package.

scholarly journals From Mice to Mainframes: Experimental Models for Investigation of the Intracardiac Nervous System

2021 ◽  
Vol 8 (11) ◽  
pp. 149
Author(s):  
Matthew R. Stoyek ◽  
Luis Hortells ◽  
T. Alexander Quinn
Keyword(s):  
Cardiovascular Disease ◽  
Nervous System ◽  
Computational Models ◽  
Neural Circuitry ◽  
Experimental Models ◽  
Incomplete Knowledge ◽  
Fundamental Understanding ◽  
Health And Disease ◽  
And Function ◽  
Intracardiac Nervous System

The intracardiac nervous system (IcNS), sometimes referred to as the “little brain” of the heart, is involved in modulating many aspects of cardiac physiology. In recent years our fundamental understanding of autonomic control of the heart has drastically improved, and the IcNS is increasingly being viewed as a therapeutic target in cardiovascular disease. However, investigations of the physiology and specific roles of intracardiac neurons within the neural circuitry mediating cardiac control has been hampered by an incomplete knowledge of the anatomical organisation of the IcNS. A more thorough understanding of the IcNS is hoped to promote the development of new, highly targeted therapies to modulate IcNS activity in cardiovascular disease. In this paper, we first provide an overview of IcNS anatomy and function derived from experiments in mammals. We then provide descriptions of alternate experimental models for investigation of the IcNS, focusing on a non-mammalian model (zebrafish), neuron-cardiomyocyte co-cultures, and computational models to demonstrate how the similarity of the relevant processes in each model can help to further our understanding of the IcNS in health and disease.

scholarly journals An integrated platform for intuitive mathematical programming modeling using LaTeX

2018 ◽  
Vol 4 ◽  
pp. e161 ◽  
Author(s):  
Charalampos P. Triantafyllidis ◽  
Lazaros G. Papageorgiou
Keyword(s):  
Mathematical Programming ◽  
Error Detection ◽  
Development Process ◽  
Optimization Problems ◽  
Scientific Work ◽  
Input Language ◽  
Speed Up ◽  
Cross Platform ◽  
Algebraic Modeling Language ◽  
Friendly Graphical User Interface

This paper presents a novel prototype platform that uses the same LaTeX mark-up language, commonly used to typeset mathematical content, as an input language for modeling optimization problems of various classes. The platform converts the LaTeX model into a formal Algebraic Modeling Language (AML) representation based on Pyomo through a parsing engine written in Python and solves by either via NEOS server or locally installed solvers, using a friendly Graphical User Interface (GUI). The distinct advantages of our approach can be summarized in (i) simplification and speed-up of the model design and development process (ii) non-commercial character (iii) cross-platform support (iv) easier typo and logic error detection in the description of the models and (v) minimization of working knowledge of programming and AMLs to perform mathematical programming modeling. Overall, this is a presentation of a complete workable scheme on using LaTeX for mathematical programming modeling which assists in furthering our ability to reproduce and replicate scientific work.

scholarly journals Hybrid Genetic Algorithm and Simulated Annealing for Function Optimization

2017 ◽  
Vol 1 (2) ◽  
pp. 82 ◽  
Author(s):  
Tirana Noor Fatyanosa ◽  
Andreas Nugroho Sihananto ◽  
Gusti Ahmad Fanshuri Alfarisy ◽  
M Shochibul Burhan ◽  
Wayan Firdaus Mahmudy
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Function Optimization ◽  
Non Linear ◽  
Speed Up ◽  
Linear Problems

The optimization problems on real-world usually have non-linear characteristics. Solving non-linear problems is time-consuming, thus heuristic approaches usually are being used to speed up the solution’s searching. Among of the heuristic-based algorithms, Genetic Algorithm (GA) and Simulated Annealing (SA) are two among most popular. The GA is powerful to get a nearly optimal solution on the broad searching area while SA is useful to looking for a solution in the narrow searching area. This study is comparing performance between GA, SA, and three types of Hybrid GA-SA to solve some non-linear optimization cases. The study shows that Hybrid GA-SA can enhance GA and SA to provide a better result

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