scholarly journals Combining Interval Branch and Bound and Stochastic Search

2014 ◽  
Vol 2014 ◽  
pp. 1-15
Author(s):  
Dhiranuch Bunnag
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Branch And Bound ◽  
Lower Cost ◽  
Search Algorithms ◽  
Stochastic Search ◽  
Constrained Problems ◽  
Interval Branch And Bound ◽  
Unconstrained Problems

This paper presents global optimization algorithms that incorporate the idea of an interval branch and bound and the stochastic search algorithms. Two algorithms for unconstrained problems are proposed, the hybrid interval simulated annealing and the combined interval branch and bound and genetic algorithm. The numerical experiment shows better results compared to Hansen’s algorithm and simulated annealing in terms of the storage, speed, and number of function evaluations. The convergence proof is described. Moreover, the idea of both algorithms suggests a structure for an integrated interval branch and bound and genetic algorithm for constrained problems in which the algorithm is described and tested. The aim is to capture one of the solutions with higher accuracy and lower cost. The results show better quality of the solutions with less number of function evaluations compared with the traditional GA.

Restarting search algorithms with applications to simulated annealing

2001 ◽  
Vol 33 (1) ◽  
pp. 242-259 ◽  
Author(s):  
F. Mendivil ◽  
R. Shonkwiler ◽  
M. C. Spruill
Keyword(s):  
Simulated Annealing ◽  
Energy Levels ◽  
Search Algorithms ◽  
Fixed Number ◽  
Stochastic Search ◽  
Goal State ◽  
Numerical Comparisons ◽  
Cooling Schedule ◽  
Local Generation

Some consequences of restarting stochastic search algorithms are studied. It is shown under reasonable conditions that restarting when certain patterns occur yields probabilities that the goal state has not been found by the nth epoch which converge to zero at least geometrically fast in n. These conditions are shown to hold for restarted simulated annealing employing a local generation matrix, a cooling schedule Tn ∼ c/n and restarting after a fixed number r + 1 of duplications of energy levels of states when r is sufficiently large. For simulated annealing with logarithmic cooling these probabilities cannot decrease to zero this fast. Numerical comparisons between restarted simulated annealing and several modern variations on simulated annealing are also presented and in all cases the former performs better.

scholarly journals Simulated Annealing with Exploratory Sensing for Global Optimization

Algorithms ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 230
Author(s):  
Majid Almarashi ◽  
Wael Deabes ◽  
Hesham H. Amin ◽  
Abdel-Rahman Hedar
Keyword(s):  
Global Optimization ◽  
Simulated Annealing ◽  
Initial Temperature ◽  
Search Algorithm ◽  
Random Search ◽  
Search Space ◽  
Search Algorithms ◽  
Two Phases ◽  
Number Of Iterations

Simulated annealing is a well-known search algorithm used with success history in many search problems. However, the random walk of the simulated annealing does not benefit from the memory of visited states, causing excessive random search with no diversification history. Unlike memory-based search algorithms such as the tabu search, the search in simulated annealing is dependent on the choice of the initial temperature to explore the search space, which has little indications of how much exploration has been carried out. The lack of exploration eye can affect the quality of the found solutions while the nature of the search in simulated annealing is mainly local. In this work, a methodology of two phases using an automatic diversification and intensification based on memory and sensing tools is proposed. The proposed method is called Simulated Annealing with Exploratory Sensing. The computational experiments show the efficiency of the proposed method in ensuring a good exploration while finding good solutions within a similar number of iterations.

Logarithmic Convergence of Random Heuristic Search

1996 ◽  
Vol 4 (4) ◽  
pp. 395-404 ◽  
Author(s):  
Michael D. Vose
Keyword(s):  
Genetic Algorithm ◽  
Convergence Theorem ◽  
Heuristic Search ◽  
Search Algorithms ◽  
Stochastic Search ◽  
Emergent Behavior ◽  
Genetic Search ◽  
Simple Genetic Algorithm ◽  
General Convergence

This paper speaks to the inherent emergent behavior of genetic search. For completeness and generality, a class of stochastic search algorithms, random heuristic search, is reviewed. A general convergence theorem for this class is then proved. Since the simple genetic algorithm (GA) is an instance of random heuristic search, a corollary is a result concerning GAs and time to convergence.

Appropriate Geometry Parameters Determination of Anti-Slide Pile with Stochastic Search Algorithms

2012 ◽  
Vol 170-173 ◽  
pp. 819-823 ◽  
Author(s):  
Wan Bo Qu ◽  
Yan Li
Keyword(s):  
Mathematical Model ◽  
Lower Cost ◽  
Search Algorithm ◽  
Design Method ◽  
Soil Mass ◽  
Search Algorithms ◽  
Stochastic Search ◽  
Main Factors ◽  
The Cost

Based on the analysis of the main factors which influence the cost of the anti-slide pile, a mathematical model was proposed. In this model, the expenditure of anti-slide pile construction was taken as the objective function, and the constraints covered the strength and stability of anti-slide pile, the strength of the soil mass or rock mass and some other demands. A program was developed for the anti-slide pile geometry sizes determination using stochastic search algorithm. With the aid of this program, appropriate geometry sizes of anti-slide pile with lower cost and enough safety could be automatically found. An example showed that the cost of the anti-slide pile could be decreased in contrust with conventional design method.

Integral Time Absolute Error Minimization For Pi Controller On Coupled–Tank Liquid Level Control System Based On Stochastic Search Echniques

2012 ◽  
Author(s):  
Ammar Hussam Yousif Yacoub ◽  
Salinda Buyamin ◽  
Norhaliza Abdul Wahab
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Absolute Error ◽  
Pi Controller ◽  
Stochastic Search ◽  
Time Response ◽  
Liquid Level ◽  
Level Control ◽  
Proportional Integral ◽  
Liquid Level Control

Kertas ini membincangkan tentang aplikasi teknik pencarian stochastic yang dikenali sebagai Simulated Annealing bagi mengatasi masalah menala (tuning) alat pengawal Proportional plus Integral (PI) bagi pengawalan takat cecair di dalam sistem tangki berkembar. Setelah penerangan tentang prinsipal asas kepada Simulated Annealing diberikan, kertas ini mencadangkan tentang pencarian penyelesaian yang optimal bagi alat kawalan PI dengan mengoptimumkan prestasi indek, ITAE. Keberkesanan menggunakan kaedah Simulated Annealing telah dibandingkan dengan satu lagi kaedah iaitu Genetic Algorithm. Perbandingan adalah berdasarkan prestasi time response. Hasil keputusan menunjukkan perbandingan antara kaedah Simulated Annealing dan Genetic Algorithm. Kaedah yang di cadangkan tidak bergantung kepada tahap sesuatu sistem dan berupaya untuk menala walaupun tanpa diketahui parameter sesuatu proses . Di samping itu, kaedah yang di cadangkan tidak memerlukan sistem tersebut dimodelkan dalam bentuk matematik dan keseluruhan keputusan menunjukkan Simulated Annealing menghasilkan keputusan yang lebih baik dari Genetik Algorithm. Oleh itu, Simulated Annealing bolehlah dicadangkan sebagai salah satu cara bagi mengoptimumkan alat kawalan PI. Kata kunci: Penalaan proportional integral tuning; imulated annealing; genetic algorithm This paper introduces the application of a stochastic search technique, known as Simulated Annealing to the problem of tuning the proportional–integral controller for a linearized coupled tank liquid level control. After describing the basic principles of the Simulated Annealing, the proposed method concentrates on finding the optimal solution of PI controller by optimizing the performance index, the Integral Time Absolute Error, ITAE. The efficiency of Simulated Annealing algorithm for tuning the controller is compared with an evolutionary method, Genetic Algorithm. The comparison is based on the time response performance. The results shows the effectiveness and the capability of the SA to tune the proportional–integral (PI) controller for the coupled tank liquid level control. The proposed method does not depend on the system order and has the ability to tune the controller even there is unknown process parameters. In addition, the technique avoids the requirement for mathematical modeling of the system and the overall results have shown that SA yields better performance as compared to GA, hence, it is recommended for an alternative for optimizing the PI controller. Key words: Proportional integral tuning; simulated annealing; genetic algorithm

scholarly journals Scientific Applications in the Cloud: Resource Optimisation based on Metaheuristics

2020 ◽  
Vol 21 (4) ◽  
pp. 649-660
Author(s):  
Anas Mokhtari ◽  
Mostafa Azizi ◽  
Mohammed Gabli
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Cost Reduction ◽  
Complex Task ◽  
Scientific Applications ◽  
Cloud Data ◽  
Cloud Resource ◽  
Data Centres ◽  
Different Levels

The advent of emerging technologies such as 5G and Internet of Things (IoT) will generate a colossal amount ofdata that should be processed by the cloud computing. Thereby, cloud resources optimisation represents significant benefits in different levels: cost reduction for the user, saving energy consumed by cloud data centres, etc. Cloud resource optimisation is a very complex task due to its NP-hard characteristic. In this case, use of metaheuristic approaches is more rational. But the quality of metaheuristic solutions changes by changing the problem. In this paper we have dealt with the problem of determining the configuration of resources in order to minimise the payment cost and the duration of the scientific applications execution. For that, we proposed a mathematical model and three metaheuristic approaches, namely the Genetic Algorithm (GA), hybridisation of the Genetic Algorithm with Local Search (GA-LS) and the Simulated Annealing (SA). The comparison between them showed that the simulated annealing finds more optimal solutions than those proposed by the genetic algorithm and the GA-LS hybridisation.

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

scholarly journals Genetic Algorithm for the Retailers’ Shelf Space Allocation Profit Maximization Problem

2021 ◽  
Vol 11 (14) ◽  
pp. 6401
Author(s):  
Kateryna Czerniachowska ◽  
Karina Sachpazidu-Wójcicka ◽  
Piotr Sulikowski ◽  
Marcin Hernes ◽  
Artur Rot
Keyword(s):  
Genetic Algorithm ◽  
Profit Maximization ◽  
Allocation Rule ◽  
Maximization Problem ◽  
Shelf Space ◽  
Allocation Model ◽  
Space Allocation ◽  
Shelf Space Allocation ◽  
To Receive

This paper discusses the problem of retailers’ profit maximization regarding displaying products on the planogram shelves, which may have different dimensions in each store but allocate the same product sets. We develop a mathematical model and a genetic algorithm for solving the shelf space allocation problem with the criteria of retailers’ profit maximization. The implemented program executes in a reasonable time. The quality of the genetic algorithm has been evaluated using the CPLEX solver. We determine four groups of constraints for the products that should be allocated on a shelf: shelf constraints, shelf type constraints, product constraints, and virtual segment constraints. The validity of the developed genetic algorithm has been checked on 25 retailing test cases. Computational results prove that the proposed approach allows for obtaining efficient results in short running time, and the developed complex shelf space allocation model, which considers multiple attributes of a shelf, segment, and product, as well as product capping and nesting allocation rule, is of high practical relevance. The proposed approach allows retailers to receive higher store profits with regard to the actual merchandising rules.

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