The Mathematical Model, Implementation and the Parameter-Tuning of the African Buffalo Optimization Algorithm

2020 ◽  
Author(s):  
Julius Beneoluchi Odili ◽  
Johnson Oladele Fatokun
Keyword(s):  
Mathematical Model ◽  
Optimization Algorithm ◽  
Parameter Tuning ◽  
African Buffalo ◽  
The Mathematical Model

scholarly journals Solving the Traveling Salesman’s Problem Using the African Buffalo Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Julius Beneoluchi Odili ◽  
Mohd Nizam Mohmad Kahar
Keyword(s):  
Mathematical Model ◽  
Optimization Algorithm ◽  
Search Space ◽  
Metaheuristic Algorithm ◽  
African Buffalo ◽  
Exploration And Exploitation ◽  
Organizational Skills ◽  
Careful Observation ◽  
Good Memory

This paper proposes the African Buffalo Optimization (ABO) which is a new metaheuristic algorithm that is derived from careful observation of the African buffalos, a species of wild cows, in the African forests and savannahs. This animal displays uncommon intelligence, strategic organizational skills, and exceptional navigational ingenuity in its traversal of the African landscape in search for food. The African Buffalo Optimization builds a mathematical model from the behavior of this animal and uses the model to solve 33 benchmark symmetric Traveling Salesman’s Problem and six difficult asymmetric instances from the TSPLIB. This study shows that buffalos are able to ensure excellent exploration and exploitation of the search space through regular communication, cooperation, and good memory of its previous personal exploits as well as tapping from the herd’s collective exploits. The results obtained by using the ABO to solve these TSP cases were benchmarked against the results obtained by using other popular algorithms. The results obtained using the African Buffalo Optimization algorithm are very competitive.

Cooperative Target Allocation for UCAV Team Air-to-Ground Attack Based on Decision Graph Bayesian Optimization Algorithm

2012 ◽  
Vol 457-458 ◽  
pp. 655-662
Author(s):  
Lu Cao ◽  
An Zhang ◽  
Feng Juan Guo
Keyword(s):  
Mathematical Model ◽  
Decision Making ◽  
Optimization Algorithm ◽  
Optimal Solution ◽  
Bayesian Optimization ◽  
Bayesian Optimization Algorithm ◽  
Decision Graph ◽  
Target Allocation ◽  
Cooperative Decision Making ◽  
The Mathematical Model

In order to control and optimize cooperative air-to-ground attack decision-making of the unmanned combat aerial vehicle (UCAV) team, the principle of income maximum and loss minimum of UCAV team is built firstly. Accordingly, the mathematical model of cooperative target allocation is built based on the decision variables and constraints. Then Bayesian optimization algorithm (BOA) is introduced which is one kind of the evolution algorithm. For improving the ability of the BOA, decision graph is introduced to enhance the represent and learn of Bayesian network and compress the parameter saving. Finally decision graph Bayesian optimization algorithm (DBOA) is utilized to optimize and analyze the model. The simulation results verify that the mathematical model of cooperative target allocation can reflect the importance of cooperative decision-making, the DBOA can converge quickly to the global optimal solution and can effectively solve the cooperative target allocation problem of UCAV team air-to-ground attack.

Study on the Optimization of Absorption Baffle with Coating Material

2013 ◽  
Vol 644 ◽  
pp. 165-170
Author(s):  
Wen Yong Guo ◽  
Lin Gen Chen ◽  
Yu Cao
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Optimization Algorithm ◽  
Cylindrical Cavity ◽  
Material Characteristic ◽  
Sound Reflection ◽  
Viscoelastic Structure ◽  
Simulation Results ◽  
The Mathematical Model ◽  
Selection Of

The absorption baffle making of viscoelastic structure with varying material can decrease effectively the sound reflection on the considerable bandwidth of sound-absorbing frequencies. In order to design the varying sectional cylindrical cavity which has better absorption result in more wide bandwidth, the mathematical model of the perforated ratio and optimization algorithm based on the genetic algorithm was designed. The simulation results based on the different rubber materials reveal that the selection of the shape of the cavity depends on the material characteristic. And the result from the optimization algorithm can be regarded as the effective referenced data.

scholarly journals The predictability of seismicity and large earthquakes: Kamchatka 1962 to 2014

2019 ◽  
pp. 52-66
Author(s):  
A. I. Malyshev
Keyword(s):  
Differential Equation ◽  
Mathematical Model ◽  
Nonlinear Differential Equation ◽  
Optimization Algorithm ◽  
Seismic Activity ◽  
Second Order ◽  
High Predictability ◽  
The Mathematical Model ◽  
Large Earthquakes ◽  
Over Time

This paper reports the estimation of the predictability of seismicity and large earthquakes in Kamchatka as inferred from data in the Kamchatka regional catalog for 1962–2014. The mathematical model uses a second-order nonlinear differential equation, while the optimization algorithm and the estimates of predictability are the author’s own. The estimates show a high predictability of seismicity; the extrema of prediction nonlinearity typical of large earthquakes usually occur simultaneously with similar extrema of seismicity as a whole. Overall, 220 large (K ≥ 13.3) Kamchatka earthquakes were analyzed to find that foreshock predictability was available for 200 earthquakes (~30000 determinations) and aftershock predictability for 215 earthquakes (~300000 determinations). The predictability related to large earthquakes began to be seen and was rapidly increasing at intermediate (7.5–30 km) radii of hypocenter samples. The prediction distances over time were some tens and hundreds of days for foreshock predictability and some hundreds and thousands of days for aftershock predictability. These results demonstrate very good promise for the approximation extrapolation approach to the prediction of both large earthquakes themselves and of subsequent aftershock decay of seismic activity.

scholarly journals The predictability of seismicity and large earthquakes: Kamchatka 1962 to 2014

2019 ◽  
pp. 52-66
Author(s):  
A. I. Malyshev
Keyword(s):  
Differential Equation ◽  
Mathematical Model ◽  
Nonlinear Differential Equation ◽  
Optimization Algorithm ◽  
Seismic Activity ◽  
Second Order ◽  
High Predictability ◽  
The Mathematical Model ◽  
Large Earthquakes ◽  
Over Time

This paper reports the estimation of the predictability of seismicity and large earthquakes in Kamchatka as inferred from data in the Kamchatka regional catalog for 1962–2014. The mathematical model uses a second-order nonlinear differential equation, while the optimization algorithm and the estimates of predictability are the author’s own. The estimates show a high predictability of seismicity; the extrema of prediction nonlinearity typical of large earthquakes usually occur simultaneously with similar extrema of seismicity as a whole. Overall, 220 large (K ≥ 13.3) Kamchatka earthquakes were analyzed to find that foreshock predictability was available for 200 earthquakes (~30000 determinations) and aftershock predictability for 215 earthquakes (~300000 determinations). The predictability related to large earthquakes began to be seen and was rapidly increasing at intermediate (7.5–30 km) radii of hypocenter samples. The prediction distances over time were some tens and hundreds of days for foreshock predictability and some hundreds and thousands of days for aftershock predictability. These results demonstrate very good promise for the approximation extrapolation approach to the prediction of both large earthquakes themselves and of subsequent aftershock decay of seismic activity.

scholarly journals The Optimization Algorithm of the Forced Current Cathodic Protection Base on Simulated Annealing

Algorithms ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 83 ◽  
Author(s):  
Song ◽  
Wang ◽  
Qin ◽  
Wang ◽  
Liu
Keyword(s):  
Mathematical Model ◽  
Simulated Annealing ◽  
Cathodic Protection ◽  
Optimization Algorithm ◽  
Simulated Annealing Algorithm ◽  
Element Model ◽  
Parameter Design ◽  
Variable Metric ◽  
Grounding Grid ◽  
The Mathematical Model

The grounding grid of a substation is important for the safety of substation equipment. Especially to address the difficulty of parameter design in the auxiliary anode system of a grounding grid, an algorithm is proposed that is an optimization algorithm for the auxiliary anode system of a grounding grid based on improved simulated annealing. The mathematical model of the auxiliary anode system is inferred from the mathematical model of cathodic protection. On that basis, the parameters of the finite element model are optimized with the improved simulated annealing algorithm, thereby the auxiliary anode system of a grounding grid with optimized parameters is structured. Then the algorithm is proven as valid through experiments. The precision of the optimized parameters is improved by about 1.55% with respect to the Variable Metric Method and the Genetic Algorithm, so it can provide a basis for parameter design in the auxiliary anode system of a grounding grid.

Curriculum Schedule Arrangement Solved by an Improved Immune Optimization Algorithm

2013 ◽  
Vol 756-759 ◽  
pp. 3556-3561
Author(s):  
Xiao Feng Li ◽  
An Shi ◽  
Jian Guo Luo
Keyword(s):  
Mathematical Model ◽  
Optimization Algorithm ◽  
Immune Algorithm ◽  
Experimental Result ◽  
Simulation Experiments ◽  
Immune Optimization ◽  
University Timetabling ◽  
Immune Optimization Algorithm ◽  
The University ◽  
The Mathematical Model

To solve the university timetabling problem (UTP) effectively, a immune algorithm-based solution for UTP was proposed. The mathematical model of UTP was expounded, a framework of immune algorithm was given, and simulation experiments were done to validate algorithm. Experimental result shows that proposed algorithm can solve the UTP effectively, and has the advantage of good application value.

An Optimization Algorithm for Selecting Cloud Service Partner Based on Graph Theory

2013 ◽  
Vol 347-350 ◽  
pp. 3391-3396
Author(s):  
Yan Fang Kang ◽  
Gui Hua Nie ◽  
Dong Lin Chen ◽  
Zhong Wu
Keyword(s):  
Mathematical Model ◽  
Cloud Computing ◽  
Graph Theory ◽  
Optimization Algorithm ◽  
Service Providers ◽  
Cloud Service ◽  
Cloud Service Provider ◽  
Cloud Service Providers ◽  
The Mathematical Model ◽  
Theory Solution

The cloud computing market is composed by many cloud service providers, It is a key problem about how to choose and to determine the appropriate the competitiveness partners between the major cloud service providers. For this problem we propose a optimization algorithm based on graph theory solution. First we should clearly consider the factors in choosing the partner's .On this basis we establish the mathematical model of the cloud service provider partners. How to select the tender of cloud service providers in the problem, the paper gives the optimization algorithm based on graph theory, last we give an example to verify the effectiveness of the algorithm.

scholarly journals Sizing a Hybrid Renewable Energy System by a Coevolutionary Multiobjective Optimization Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Wenhua Li ◽  
Guo Zhang ◽  
Xu Yang ◽  
Zhang Tao ◽  
Hu Xu
Keyword(s):  
Mathematical Model ◽  
Renewable Energy ◽  
Multiobjective Optimization ◽  
Optimization Algorithm ◽  
Energy System ◽  
Mixed Integer ◽  
Hybrid Renewable Energy System ◽  
Renewable Energy System ◽  
Hybrid Renewable Energy ◽  
The Mathematical Model

Hybrid renewable energy system (HRES) arises regularly in real life. By optimizing the capacity and running status of the microgrid (MG), HRES can decrease the running cost and improve the efficiency. Such an optimization problem is generally a constrained mixed-integer programming problem, which is usually solved by linear programming method. However, as more and more devices are added into MG, the mathematical model of HRES refers to nonlinear, in which the traditional method is incapable to solve. To address this issue, we first proposed the mathematical model of an HRES. Then, a coevolutionary multiobjective optimization algorithm, termed CMOEA-c, is proposed to handle the nonlinear part and the constraints. By considering the constraints and the objective values simultaneously, CMOEA-c can easily jump out of the local optimal solution and obtain satisfactory results. Experimental results show that, compared to other state-of-the-art methods, the proposed algorithm is competitive in solving HRES problems.

Optimal Scheduling of Multiple-Line Collaborative Manufacturing Based on Teaching-Learning-Based Optimization Algorithm

2014 ◽  
Vol 1006-1007 ◽  
pp. 432-436
Author(s):  
San Qiang Zhang ◽  
Dao Yuan Yu ◽  
Xin Yu Shao ◽  
Chao Yong Zhang ◽  
Wen Wen Lin ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Optimization Algorithm ◽  
Optimal Scheduling ◽  
Collaborative Manufacturing ◽  
Multiple Line ◽  
Teaching Learning Based Optimization ◽  
Production Orders ◽  
Teaching Learning ◽  
The Mathematical Model

A mathematical model of multiple-linecollaborative manufacturing was formulated in this paper, and teaching-learning-based optimization algorithm was applied to solve the problem. A set of production orders are taken as example. The results prove that the mathematical model and algorithm can perform well in optimal scheduling of multiple-linecollaborative manufacturing.

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