Magnetotactic Bacteria Optimization Algorithm Based On Four Best-Rand Pairwise Schemes

2014 ◽  
Vol 5 (3) ◽  
pp. 22-40
Author(s):  
Hongwei Mo ◽  
Lifang Xu ◽  
Lili Liu ◽  
Yanyan Zhao
Keyword(s):  
Optimization Algorithm ◽  
Geomagnetic Field ◽  
Development Process ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Magnetotactic Bacteria ◽  
Experimental Results ◽  
Superior Performance ◽  
Standard Function ◽  
Field Lines

Magnetotactic bacteria optimization algorithm (MBOA) is an optimization algorithm based on the characteristics of magnetotactic bacteria, which is a kind of polyphyletic group of prokaryotes with the characteristics of magnetotaxis that make them orient and swim along geomagnetic field lines. MBOA mimics the development process of magnetosomes (MTSs) in magnetotactic bacteria to solve problems. In this paper, four improved MBOAs are researched. Four pairwise MTSs regulation schemes based on the best individual and randomly chosen one are proposed in order to study which scheme is more suitable for solving optimization problems. They are tested on fourteen standard function problems and compared with many popular optimization algorithms, including PSO, DE, ABC and their variants. Experimental results show that all the schemes of MBOA are effective for solving most of the benchmark functions, but have different performance on a few benchmark functions. The fourth MBOA scheme has superior performance to the compared methods on many benchmark functions.

Magnetotactic Bacteria Optimization Algorithm (MBOA) for Function Optimization

2018 ◽  
pp. 97-118
Author(s):  
Lili Liu ◽  
Hongwei Mo
Keyword(s):  
Optimization Algorithm ◽  
Development Process ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Magnetotactic Bacteria ◽  
Function Optimization ◽  
Experimental Results ◽  
Superior Performance ◽  
Test Functions ◽  
The Individual

Magnetotactic bacteria is a kind of prokaryotes with the characteristics of magnetotaxis. Magnetotactic bacteria optimization algorithm (MBOA) is an optimization algorithm based on the characteristics of magnetotaxis. It mimics the development process of magnetosomes (MTSs) in magnetotactic bacteria. In this chapter, four pairwise MTSs regulation schemes based on the best individual and randomly chosen one are proposed to study which scheme is more suitable for solving optimization problems. They are tested on 14 functions and compared with many popular optimization algorithms, including PSO, DE, ABC, and their variants. Experimental results show that all the schemes of MBOA are effective for solving most of test functions but have different performance on a few test functions. The fourth MBOA scheme has superior performance to the compared methods on many test functions. In this scheme, the algorithm searches around the current best individual to enhance the convergence of MBOA and the individual can migrate to the current best individual to enhance the diversity of the MBOA.

scholarly journals A New “Good and Bad Groups-Based Optimizer” for Solving Various Optimization Problems

2021 ◽  
Vol 11 (10) ◽  
pp. 4382
Author(s):  
Ali Sadeghi ◽  
Sajjad Amiri Doumari ◽  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Pavel Trojovský ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Gray Wolf ◽  
Good Group ◽  
Good Ability ◽  
Whale Optimization

Optimization is the science that presents a solution among the available solutions considering an optimization problem’s limitations. Optimization algorithms have been introduced as efficient tools for solving optimization problems. These algorithms are designed based on various natural phenomena, behavior, the lifestyle of living beings, physical laws, rules of games, etc. In this paper, a new optimization algorithm called the good and bad groups-based optimizer (GBGBO) is introduced to solve various optimization problems. In GBGBO, population members update under the influence of two groups named the good group and the bad group. The good group consists of a certain number of the population members with better fitness function than other members and the bad group consists of a number of the population members with worse fitness function than other members of the population. GBGBO is mathematically modeled and its performance in solving optimization problems was tested on a set of twenty-three different objective functions. In addition, for further analysis, the results obtained from the proposed algorithm were compared with eight optimization algorithms: genetic algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), teaching–learning-based optimization (TLBO), gray wolf optimizer (GWO), and the whale optimization algorithm (WOA), tunicate swarm algorithm (TSA), and marine predators algorithm (MPA). The results show that the proposed GBGBO algorithm has a good ability to solve various optimization problems and is more competitive than other similar algorithms.

scholarly journals GMBO: Group Mean-Based Optimizer for Solving Various Optimization Problems

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1190
Author(s):  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Štěpán Hubálovský
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Random Search ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Main Idea ◽  
Population Based ◽  
Grey Wolf Optimizer ◽  
Whale Optimization

There are many optimization problems in the different disciplines of science that must be solved using the appropriate method. Population-based optimization algorithms are one of the most efficient ways to solve various optimization problems. Population-based optimization algorithms are able to provide appropriate solutions to optimization problems based on a random search of the problem-solving space without the need for gradient and derivative information. In this paper, a new optimization algorithm called the Group Mean-Based Optimizer (GMBO) is presented; it can be applied to solve optimization problems in various fields of science. The main idea in designing the GMBO is to use more effectively the information of different members of the algorithm population based on two selected groups, with the titles of the good group and the bad group. Two new composite members are obtained by averaging each of these groups, which are used to update the population members. The various stages of the GMBO are described and mathematically modeled with the aim of being used to solve optimization problems. The performance of the GMBO in providing a suitable quasi-optimal solution on a set of 23 standard objective functions of different types of unimodal, high-dimensional multimodal, and fixed-dimensional multimodal is evaluated. In addition, the optimization results obtained from the proposed GMBO were compared with eight other widely used optimization algorithms, including the Marine Predators Algorithm (MPA), the Tunicate Swarm Algorithm (TSA), the Whale Optimization Algorithm (WOA), the Grey Wolf Optimizer (GWO), Teaching–Learning-Based Optimization (TLBO), the Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO), and the Genetic Algorithm (GA). The optimization results indicated the acceptable performance of the proposed GMBO, and, based on the analysis and comparison of the results, it was determined that the GMBO is superior and much more competitive than the other eight algorithms.

scholarly journals Magnetotaxis as a Means for Nanofabrication

2014 ◽  
Vol 23 (01n02) ◽  
pp. 1450008
Author(s):  
Isaac Macwan ◽  
Zihe Zhao ◽  
Omar Sobh ◽  
Jinnque Rho ◽  
Ausif Mahmood ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Geomagnetic Field ◽  
Semiconductor Manufacturing ◽  
Magnetic Field Intensity ◽  
Magnetotactic Bacteria ◽  
Semiconductor Substrate ◽  
Field Lines ◽  
A Chain ◽  
Magnetospirillum Magneticum ◽  
The Magnetic Field

Magnetotactic bacteria (MTB), discovered in early 1970s contain single-domain crystals of magnetite ( Fe 3 O 4) called magnetosomes that tend to form a chain like structure from the proximal to the distal pole along the long axis of the cell. The ability of these bacteria to sense the magnetic field for displacement, also called magnetotaxis, arises from the magnetic dipole moment of this chain of magnetosomes. In aquatic habitats, these organisms sense the geomagnetic field and traverse the oxic-anoxic interface for optimal oxygen concentration along the field lines. Here we report an elegant use of MTB where magnetotaxis of Magnetospirillum magneticum (classified as AMB-1) could be utilized for controlled navigation over a semiconductor substrate for selective deposition. We examined 50mm long coils made out of 18AWG and 20AWG copper conductors having diameters of 5mm, 10mm and 20mm for magnetic field intensity and heat generation. Based on the COMSOL simulations and experimental data, it is recognized that a compound semiconductor manufacturing technology involving bacterial carriers and carbon-based materials such as graphene and carbon nanotubes would be a desirable choice in the future.

scholarly journals Disruption-Based Multiobjective Equilibrium Optimization Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-21
Author(s):  
Hao Chen ◽  
Weikun Li ◽  
Weicheng Cui
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Novel Mutation ◽  
Optimization Algorithms ◽  
Benchmark Problems ◽  
Engineering Optimization ◽  
Test Results ◽  
Nature Inspired Computing ◽  
Disruption Method

Nature-inspired computing has attracted huge attention since its origin, especially in the field of multiobjective optimization. This paper proposes a disruption-based multiobjective equilibrium optimization algorithm (DMOEOA). A novel mutation operator named layered disruption method is integrated into the proposed algorithm with the aim of enhancing the exploration and exploitation abilities of DMOEOA. To demonstrate the advantages of the proposed algorithm, various benchmarks have been selected with five different multiobjective optimization algorithms. The test results indicate that DMOEOA does exhibit better performances in these problems with a better balance between convergence and distribution. In addition, the new proposed algorithm is applied to the structural optimization of an elastic truss with the other five existing multiobjective optimization algorithms. The obtained results demonstrate that DMOEOA is not only an algorithm with good performance for benchmark problems but is also expected to have a wide application in real-world engineering optimization problems.

scholarly journals Multi/Many-Objective Particle Swarm Optimization Algorithm Based on Competition Mechanism

2020 ◽  
Vol 2020 ◽  
pp. 1-26
Author(s):  
Wusi Yang ◽  
Li Chen ◽  
Yi Wang ◽  
Maosheng Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Benchmark Problems ◽  
Swarm Optimization ◽  
Competition Mechanism ◽  
Multiobjective Particle Swarm Optimization

The recently proposed multiobjective particle swarm optimization algorithm based on competition mechanism algorithm cannot effectively deal with many-objective optimization problems, which is characterized by relatively poor convergence and diversity, and long computing runtime. In this paper, a novel multi/many-objective particle swarm optimization algorithm based on competition mechanism is proposed, which maintains population diversity by the maximum and minimum angle between ordinary and extreme individuals. And the recently proposed θ-dominance is adopted to further enhance the performance of the algorithm. The proposed algorithm is evaluated on the standard benchmark problems DTLZ, WFG, and UF1-9 and compared with the four recently proposed multiobjective particle swarm optimization algorithms and four state-of-the-art many-objective evolutionary optimization algorithms. The experimental results indicate that the proposed algorithm has better convergence and diversity, and its performance is superior to other comparative algorithms on most test instances.

scholarly journals A New Optimization Algorithm Based on Search and Rescue Operations

2019 ◽  
Vol 2019 ◽  
pp. 1-23 ◽  
Author(s):  
Amir Shabani ◽  
Behrouz Asgarian ◽  
Saeed Asil Gharebaghi ◽  
Miguel A. Salido ◽  
Adriana Giret
Keyword(s):  
Optimization Algorithm ◽  
Real World ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Continuous Optimization ◽  
Optimization Method ◽  
Search And Rescue ◽  
Rank Test ◽  
Engineering Design Problems ◽  
Continuous Optimization Problems

In this paper, a new optimization algorithm called the search and rescue optimization algorithm (SAR) is proposed for solving single-objective continuous optimization problems. SAR is inspired by the explorations carried out by humans during search and rescue operations. The performance of SAR was evaluated on fifty-five optimization functions including a set of classic benchmark functions and a set of modern CEC 2013 benchmark functions from the literature. The obtained results were compared with twelve optimization algorithms including well-known optimization algorithms, recent variants of GA, DE, CMA-ES, and PSO, and recent metaheuristic algorithms. The Wilcoxon signed-rank test was used for some of the comparisons, and the convergence behavior of SAR was investigated. The statistical results indicated SAR is highly competitive with the compared algorithms. Also, in order to evaluate the application of SAR on real-world optimization problems, it was applied to three engineering design problems, and the results revealed that SAR is able to find more accurate solutions with fewer function evaluations in comparison with the other existing algorithms. Thus, the proposed algorithm can be considered an efficient optimization method for real-world optimization problems.

scholarly journals North-Seeking Magnetotactic Gammaproteobacteria in the Southern Hemisphere

2016 ◽  
Vol 82 (18) ◽  
pp. 5595-5602 ◽  
Author(s):  
Pedro Leão ◽  
Lia C. R. S. Teixeira ◽  
Jefferson Cypriano ◽  
Marcos Farina ◽  
Fernanda Abreu ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Southern Hemisphere ◽  
Northern Hemisphere ◽  
Geomagnetic Field ◽  
Magnetotactic Bacteria ◽  
Rrna Gene ◽  
Natural Environments ◽  
Optimal Position ◽  
Brackish Lagoon ◽  
Field Lines

ABSTRACTMagnetotactic bacteria (MTB) comprise a phylogenetically diverse group of prokaryotes capable of orienting and navigating along magnetic field lines. Under oxic conditions, MTB in natural environments in the Northern Hemisphere generally display north-seeking (NS) polarity, swimming parallel to the Earth's magnetic field lines, while those in the Southern Hemisphere generally swim antiparallel to magnetic field lines (south-seeking [SS] polarity). Here, we report a population of an uncultured, monotrichously flagellated, and vibrioid MTB collected from a brackish lagoon in Brazil in the Southern Hemisphere that consistently exhibits NS polarity. Cells of this organism were mainly located below the oxic-anoxic interface (OAI), suggesting it is capable of some type of anaerobic metabolism. Magnetosome crystalline habit and composition were consistent with elongated prismatic magnetite (Fe3O4) particles. Phylogenetic analysis based on 16S rRNA gene sequencing indicated that this organism belongs to a distinct clade of theGammaproteobacteriaclass. The presence of NS MTB in the Southern Hemisphere and the previously reported finding of SS MTB in the Northern Hemisphere reinforce the idea that magnetotaxis is more complex than we currently understand and may be modulated by factors other than O2concentration and redox gradients in sediments and water columns.IMPORTANCEMagnetotaxis is a navigational mechanism used by magnetotactic bacteria to move along geomagnetic field lines and find an optimal position in chemically stratified sediments. For that, magnetotactic bacteria swim parallel to the geomagnetic field lines under oxic conditions in the Northern Hemisphere, whereas those in the Southern Hemisphere swim antiparallel to magnetic field lines. A population of uncultured vibrioid magnetotactic bacteria was discovered in a brackish lagoon in the Southern Hemisphere that consistently swim northward, i.e., the opposite of the overwhelming majority of other Southern Hemisphere magnetotactic bacteria. This finding supports the idea that magnetotaxis is more complex than previously thought.

ON THE DESIGN OF OPTIMIZATION ALGORITHMS FOR PREDICTION OF MOLECULAR INTERACTIONS

2010 ◽  
Vol 19 (03) ◽  
pp. 267-280 ◽  
Author(s):  
DARBY TIEN-HAO CHANG ◽  
JUNG-HSIN LIN ◽  
CHIH-HUNG HSIEH ◽  
YEN-JENG OYANG
Keyword(s):  
Optimization Algorithm ◽  
Optimization Algorithms ◽  
Significant Degree ◽  
Superior Performance ◽  
Ligand Docking ◽  
Adaptive Mutation ◽  
Protein Ligand Interactions ◽  
Computer Scientists ◽  
Ligand Interactions ◽  
Conventional Optimization

This article presents a comprehensive study on the main characteristics of a novel optimization algorithm specifically designed for simulation of protein-ligand interactions. Though design of optimization algorithms has been a research issue extensively studied by computer scientists for decades, the emerging applications in bioinformatics such as simulation of protein-ligand interactions and protein folding introduce additional challenges due to (1) the high dimensionality nature of the problem and (2) the highly rugged landscape of the energy function. As a result, optimization algorithms that are not carefully designed to tackle these two challenges may fail to deliver satisfactory performance. This study has been motivated by the observation that the RAME (Rank-based Adaptive Mutation Evolutionary) optimization algorithm specifically designed for simulation of protein-ligand docking has consistently outperformed the conventional optimization algorithms by a significant degree. The experimental results reveal that the RAME algorithm is capable of delivering superior performance to several alternative versions of the genetic algorithm in handling highly-rugged functions in the high-dimensional vector space. This article also reports experiments conducted to analyze the causes of the observed performance difference. The experiences learned provide valuable clues for how the proposed algorithm can be effectively exploited to tackle other computational biology problems.

A Constrained Particle Swarm Optimization Algorithm with Oracle Penalty Method

2013 ◽  
Vol 303-306 ◽  
pp. 1519-1523 ◽  
Author(s):  
Ming Gang Dong ◽  
Xiao Hui Cheng ◽  
Qin Zhou Niu
Keyword(s):  
Particle Swarm Optimization ◽  
Constrained Optimization ◽  
Optimization Algorithm ◽  
Penalty Method ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Experimental Results ◽  
Constrained Optimization Problems ◽  
Swarm Optimization

To solve constrained optimization problems, an Oracle penalty method-based comprehensive learning particle swarm optimization (OBCLPSO) algorithm was proposed. First, original Oracle penalty was modified. Secondly, the modified Oracle penalty method was combine with comprehensive learning particle swarm optimization algorithm. Finally, experimental results and comparisons were given to demonstrate the optimization performances of OBCLPSO. The results show that the proposed algorithm is a very competitive approach for constrained optimization problems.

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