Hybrid gradient simulated annealing algorithm for finding the global optimal of a nonlinear unconstrained optimization problem

The S-λ Curves have become an important research subject in computer aided geometric design (CAGD), which owes to its good geometric properties (such as affine invariance, symmetry, and locality). This paper presents a new method to approximate an S-λ curve of degree n by using an S-λ curve of degree n-1. We transform this degree reduction problem into the function optimization problem first, and then using a new genetic simulated annealing algorithm to determine the global optimal solution of the optimization problem. The method can be used to approximate S-λ curves with fixed or unconstrained endpoints. Examples are given to verify the effectiveness of the presented algorithm; and these numeric examples show that the algorithm is not only easy to implement, but also offers high precision, which makes it valuable in practical applications.

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A Knowledge-Based Simulated Annealing Algorithm to Multiple Satellites Mission Planning Problems

Journal of Applied Mathematics ◽

10.1155/2013/913450 ◽

2013 ◽

Vol 2013 ◽

pp. 1-7 ◽

Cited By ~ 1

Author(s):

Da-Wei Jin ◽

Li-Ning Xing

Keyword(s):

Simulated Annealing ◽

Optimization Problem ◽

Simulated Annealing Algorithm ◽

Mission Planning ◽

Knowledge Based ◽

Combination Optimization ◽

Annealing Algorithm ◽

The Given ◽

Planning Problems ◽

Annealing Method

The multiple satellites mission planning is a complex combination optimization problem. A knowledge-based simulated annealing algorithm is proposed to the multiple satellites mission planning problems. The experimental results suggest that the proposed algorithm is effective to the given problem. The knowledge-based simulated annealing method will provide a useful reference for the improvement of existing optimization approaches.

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Optimal Tolerance Allocation Over Multiple Manufacturing Alternatives

18th Design Automation Conference: Volume 2 — Geometric Modeling, Mechanisms, and Mechanical Systems Analysis ◽

10.1115/detc1992-0162 ◽

1992 ◽

Author(s):

Jonathan Cagan ◽

Thomas R. Kurfess

Keyword(s):

Simulated Annealing ◽

Optimization Problem ◽

Simulated Annealing Algorithm ◽

Minimum Cost ◽

Tolerance Allocation ◽

Hyperbolic Function ◽

Manufacturing Processes ◽

Worst Case ◽

Annealing Algorithm ◽

Manufacturing Alternatives

Abstract We introduce a methodology for concurrent design that considers the allocation of tolerances and manufacturing processes for minimum cost. Cost is approximated as a hyperbolic function over tolerance, and worst-case stack-up tolerance is assumed. Two simulated annealing techniques are introduced to solve the optimization problem. The first assumes independent, unordered, manufacturing processes and uses a Monte-Carlo simulation; the second assumes well known individual process cost functions which can be manipulated to create a single continuous function of cost versus tolerance with discontinuous derivatives solved with a continuous simulated annealing algorithm. An example utilizing a system of friction wheels over the manufacturing processes of turning, grinding, and saw cutting bar stock demonstrates excellent results.

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Artificial Intelligence Application in Modern Management

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.37-38.203 ◽

2010 ◽

Vol 37-38 ◽

pp. 203-206

Author(s):

Rong Jiang

Keyword(s):

Genetic Algorithm ◽

Simulated Annealing ◽

Simulated Annealing Algorithm ◽

Optimization Problems ◽

Optimal Solution ◽

Local Optimum ◽

Efficient Operation ◽

Modern Management ◽

Global Optimal ◽

Annealing Algorithm

Modern management is a science of technology that adopts analysis, test and quantification methods to make a comprehensive arrangement of the limited resources to realize an efficient operation of a practical system. Simulated annealing algorithm has become one of the important tools for solving complex optimization problems, because of its intelligence, widely used and global search ability. Genetic algorithm may prevent effectively searching process from restraining in local optimum, thus it is more possible to obtains the global optimal solution.This paper solves unconstrained programming by simulated annealing algorithm and calculates constrained nonlinear programming by genetic algorithm in modern management. So that optimization process was simplified and the global optimal solution is ensured reliably.

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Three Dimensional Offline Packing Optimization Problem Based on Genetic Simulated Annealing Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.744-746.1919 ◽

2015 ◽

Vol 744-746 ◽

pp. 1919-1923

Author(s):

Zhan Zhong Wang ◽

Jing Fu ◽

Lan Fang Liu ◽

Rui Rui Liu

Keyword(s):

Genetic Algorithm ◽

Simulated Annealing ◽

Optimization Problem ◽

Simulated Annealing Algorithm ◽

Three Dimensional ◽

Packing Problem ◽

Genetic Simulated Annealing Algorithm ◽

Annealing Algorithm ◽

Final Optimization ◽

Packing Optimization

In this paper, we try to solve 3D offline packing optimization problem by combining two methods-genetic algorithm’ global performance and simulated annealing algorithm’ local performance. Given Heuristic rules in loading conditions, we use the optimal preservation strategy and the roulette wheel method to choose selection operator, integrating simulated annealing algorithm into genetic algorithm , and achieving code programming and algorithms by Matlab.This paper carries out an actual loading in a vehicle company in Changchun City, then makes a contrast between the final optimization results and each suppliers’ current packing data.The experimental results show that the algorithm has a certain validity and practicability in multiple container packing problem.

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Research on Fish Slicing Method Based on Simulated Annealing Algorithm

Applied Sciences ◽

10.3390/app11146503 ◽

2021 ◽

Vol 11 (14) ◽

pp. 6503

Author(s):

Shuo Liu ◽

Hao Wang ◽

Yong Cai

Keyword(s):

Simulated Annealing ◽

Multiobjective Optimization ◽

Optimization Problem ◽

Simulated Annealing Algorithm ◽

Collaborative Optimization ◽

Sequential Algorithm ◽

Multiobjective Optimization Problem ◽

Skilled Workers ◽

Mutation Strategy ◽

Annealing Algorithm

Multiobjective optimization is a common problem in the field of industrial cutting. In actual production settings, it is necessary to rely on the experience of skilled workers to achieve multiobjective collaborative optimization. The process of industrial intelligence is to perceive the parameters of a cut object through sensors and use machines instead of manual decision making. However, the traditional sequential algorithm cannot satisfy multiobjective optimization problems. This paper studies the multiobjective optimization problem of irregular objects in the field of aquatic product processing and uses the information guidance strategy to develop a simulated annealing algorithm to solve the problem according to the characteristics of the object itself. By optimizing the mutation strategy, the ability of the simulated annealing algorithm to jump out of the local optimal solution is improved. The project team developed an experimental prototype to verify the algorithm. The experimental results show that compared with the traditional sequential algorithm method, the simulated degradation algorithm designed in this paper effectively improves the quality of the target solution and greatly enhances the economic value of the product by addressing the multiobjective optimization problem of squid cutting. At the end of the article, the cutting error is analyzed.

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Simulated Annealing Algorithm for Wind Farm Layout Optimization: A Benchmark Study

Energies ◽

10.3390/en12234403 ◽

2019 ◽

Vol 12 (23) ◽

pp. 4403 ◽

Cited By ~ 3

Author(s):

Yang ◽

Cho

Keyword(s):

Genetic Algorithm ◽

Simulated Annealing ◽

Wind Turbines ◽

Wind Farm ◽

Optimization Problem ◽

Simulated Annealing Algorithm ◽

Layout Optimization ◽

Wind Farm Layout ◽

Wind Farm Layout Optimization ◽

Annealing Algorithm

The optimal layout of wind turbines is an important factor in the wind farm design process, and various attempts have been made to derive optimal deployment results. For this purpose, many approaches to optimize the layout of turbines using various optimization algorithms have been developed and applied across various studies. Among these methods, the most widely used optimization approach is the genetic algorithm, but the genetic algorithm handles many independent variables and requires a large amount of computation time. A simulated annealing algorithm is also a representative optimization algorithm, and the simulation process is similar to the wind turbine layout process. However, despite its usefulness, it has not been widely applied to the wind farm layout optimization problem. In this study, a wind farm layout optimization method was developed based on simulated annealing, and the performance of the algorithm was evaluated by comparing it to those of previous studies under three wind scenarios; likewise, the applicability was examined. A regular layout and optimal number of wind turbines, never before observed in previous studies, were obtained and they demonstrated the best fitness values for all the three considered scenarios. The results indicate that the simulated annealing (SA) algorithm can be successfully applied to the wind farm layout optimization problem.

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Query Optimization Based on the Simulated Annealing and Particle Swarm Optimization

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.229-231.1870 ◽

2012 ◽

Vol 229-231 ◽

pp. 1870-1873

Author(s):

Ren Jie Song ◽

Yan Wang

Keyword(s):

Simulated Annealing ◽

Query Optimization ◽

Simulated Annealing Algorithm ◽

Particle Swarm ◽

Optimization Method ◽

Particle Swarm Algorithm ◽

Algorithm Optimization ◽

Optimal Value ◽

Global Optimal ◽

Annealing Algorithm

In order to allow the user to quickly and accurately search the required information, a query optimization method based on a simulated annealing and particle swarm hybrid algorithm is proposed. The basic idea is: the query population into two flat sub populations, a sub population by using simulated annealing algorithm optimization, another sub populations by using particle swarm algorithm optimization, comparison of two adaptive values, to find the global optimal value. The experimental results show that the mixed algorithm, can further improve the precision and recall of query optimization.

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Simulated annealing method for equilibrium placement problem

Physico-mathematical modelling and informational technologies ◽

10.15407/fmmit2021.32.152 ◽

2021 ◽

pp. 152-158

Author(s):

Igor Kozin ◽

Natalia Maksyshko ◽

Yaroslav Tereshko

Keyword(s):

Simulated Annealing ◽

Optimization Problem ◽

Simulated Annealing Algorithm ◽

Optimal Solution ◽

Placement Problem ◽

Fragmentary Structure ◽

Simulating Annealing ◽

Set Of Permutations ◽

Annealing Algorithm ◽

The Traveling Salesman Problem

The paper proposes a modification of the simulated annealing algorithm as applied to problems that have a fragmented structure. An algorithm for simulating annealing for the traveling salesman problem is considered and its applicability to the optimization problem on a set of permutations is shown. It is proved that the problem of equilibrium placement of point objects on a plane has a fragmentary structure and, therefore, reduces to an optimization problem on a set of permutations. The results of numerical experiments for various types of algorithms for finding the optimal solution in the equilibrium placement problem are presented.

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Data Mining Method of Enterprise Human Resource Management Based on Simulated Annealing Algorithm

Security and Communication Networks ◽

10.1155/2021/6342970 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Mingwei Xu ◽

Chuang Li

Keyword(s):

Data Mining ◽

Simulated Annealing ◽

Resource Management ◽

Human Resource Management ◽

Human Resource ◽

Optimization Problem ◽

Simulated Annealing Algorithm ◽

Mining Method ◽

Nurse Scheduling ◽

Annealing Algorithm

The human resources department of an enterprise relies on the “mining” of big data when carrying out human resource management and proposes a data mining method for enterprise human resource management based on the simulated annealing algorithm. Applying the simulated annealing algorithm, using the Metropolis algorithm to generate the sequence of solutions to the combinatorial optimization problem, finding the overall optimal solution of the combinatorial optimization problem, using big data directional mining and analysis to help companies establish and find a “radar” system suitable for talents, the maximum tree method is adopted; that is, a special graph is constructed to realize the effective application of data mining technology in enterprise human resource management. The optimization of nurse scheduling in a hospital was used for case analysis. The results show that the target value of the nurse scheduling model is 43.43% lower than the actual manual scheduling target value, the salary cost is reduced by 10.8%, and the nurse’s satisfaction with the shift is increased by 35.24%. After several iterations based on the simulated annealing algorithm, the optimal value of the solution of the simulated annealing algorithm remains unchanged at the 60th generation. Then, the search process is stopped when the 100th generation is reached, and the solution at this time is the optimal optimization value found by the algorithm.

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