Simulated Annealing Optimization of Belt Conveyor Transmission

2010 ◽  
Vol 113-116 ◽  
pp. 2373-2378
Author(s):  
Ji Bin Ding
Keyword(s):  
Simulated Annealing ◽  
Objective Function ◽  
Optimization Model ◽  
Simulated Annealing Algorithm ◽  
Optimization Methods ◽  
Global Optimum ◽  
Local Optimum ◽  
Belt Conveyor ◽  
Boundary Constraints ◽  
Low Efficiency

The belt conveyor is a transporting machine by friction in a continuous manner. The two order helical gearing reducer may be generally used as conveyor transmission, and can reduce speed and increase torque of belt. The objective function may be specified that that total center distance of the reducer incline to minimum, so the optimization model including the property and boundary constraints is created. Then the objective function with penalty terms is converted by penalty strategy with addition type, so as to transform the constrained optimization into the unconstrained optimization model. Considering the problem of low efficiency and local optimum caused by standard optimization methods, the simulated annealing algorithm is adopted to solve the optimization model of Belt Conveyor Transmission, and neural network method is applied to fit relative coefficient, then BFGS Quasi-Newton method is recalled automatically when the setting working precision is achieved again. So that the optimization process is simplified and global optimum is acquired reliably.

Evolutionary Optimization on Turning Linkage of Automobile in Simulated Annealing

2010 ◽  
Vol 34-35 ◽  
pp. 317-321
Author(s):  
Feng Chen ◽  
Jiang Zhu
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Optimal Solution ◽  
Optimization Methods ◽  
Main Function ◽  
Turn Angle ◽  
Boundary Constraints ◽  
Steering Characteristics ◽  
Relationship Of ◽  
The Ideal

The main function of turning linkage of automobile is to realize the ideal relations of turn angle of the internal and external wheels when vehicles steering. At present the main methods on design computing and verifying turning linkage have still been the planar graphing and analysis method, therefore it is very important to adopt optimization methods to design the steering linkage. Being satisfied with the Ackerman theory steering characteristics and boundary constraints, considering the ideal relationship of steering angles between external and internal wheels in steering linkage to ensure motion accuracy of automobile, optimization model of turning linkage is established. Then the objective function with penalty terms is built by penalty strategy with addition type, so the constrained optimization is transformed into the unconstrained optimization. The simulated annealing algorithm is adopted to optimize turning linkage of automobile, so that optimization process was simplified and the global optimal solution is ensured reliably.

scholarly journals The Parameters Optimization of MCR-WPT System Based on the Improved Genetic Simulated Annealing Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Sheng Lu ◽  
Chenyang Zuo ◽  
Changhao Piao
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Global Optimum ◽  
Parameters Optimization ◽  
Selection Strategy ◽  
Local Optimum ◽  
Penalty Function Method ◽  
Genetic Simulated Annealing Algorithm ◽  
Annealing Algorithm ◽  
Optimum Solution

To solve the problem of parameter selection during the design of magnetically coupled resonant wireless power transmission system (MCR-WPT), this paper proposed an improved genetic simulated annealing algorithm. Firstly, the equivalent circuit of the system is analysis in this study and a nonlinear programming mathematical model is built. Secondly, in place of the penalty function method in the genetic algorithm, the selection strategy based on the distance between individuals is adopted to select individual. In this way, it reduces the excess empirical parameters. Meanwhile, it can improve the convergence rate and the searching ability by calculating crossover probability and mutation probability according to the variance of population’s fitness. At last, the simulated annealing operator is added to increase local search ability of the method. The simulation shows that the improved method can break the limit of the local optimum solution and get the global optimum solution faster. The optimized system can achieve the practical requirements.

Opinion Dynamics Optimization by Varying Susceptibility to Persuasion via Non-Convex Local Search

2021 ◽  
Vol 16 (2) ◽  
pp. 1-34
Author(s):  
Rediet Abebe ◽  
T.-H. HUBERT Chan ◽  
Jon Kleinberg ◽  
Zhibin Liang ◽  
David Parkes ◽  
...  
Keyword(s):  
Local Search ◽  
Objective Function ◽  
Iterative Process ◽  
Large Scale ◽  
Opinion Dynamics ◽  
Theoretical Models ◽  
Global Optimum ◽  
Local Optimum ◽  
Opinion Formation ◽  
Long Line

A long line of work in social psychology has studied variations in people’s susceptibility to persuasion—the extent to which they are willing to modify their opinions on a topic. This body of literature suggests an interesting perspective on theoretical models of opinion formation by interacting parties in a network: in addition to considering interventions that directly modify people’s intrinsic opinions, it is also natural to consider interventions that modify people’s susceptibility to persuasion. In this work, motivated by this fact, we propose an influence optimization problem. Specifically, we adopt a popular model for social opinion dynamics, where each agent has some fixed innate opinion, and a resistance that measures the importance it places on its innate opinion; agents influence one another’s opinions through an iterative process. Under certain conditions, this iterative process converges to some equilibrium opinion vector. For the unbudgeted variant of the problem, the goal is to modify the resistance of any number of agents (within some given range) such that the sum of the equilibrium opinions is minimized; for the budgeted variant, in addition the algorithm is given upfront a restriction on the number of agents whose resistance may be modified. We prove that the objective function is in general non-convex. Hence, formulating the problem as a convex program as in an early version of this work (Abebe et al., KDD’18) might have potential correctness issues. We instead analyze the structure of the objective function, and show that any local optimum is also a global optimum, which is somehow surprising as the objective function might not be convex. Furthermore, we combine the iterative process and the local search paradigm to design very efficient algorithms that can solve the unbudgeted variant of the problem optimally on large-scale graphs containing millions of nodes. Finally, we propose and evaluate experimentally a family of heuristics for the budgeted variant of the problem.

Application of Honey-Bee Mating Optimization to Naphtha Reforming Reactor

2013 ◽  
Vol 11 (1) ◽  
pp. 293-308 ◽  
Author(s):  
Somayeh Karimi ◽  
Navid Mostoufi ◽  
Rahmat Sotudeh-Gharebagh
Keyword(s):  
Objective Function ◽  
Honey Bee ◽  
Process Model ◽  
Global Optimum ◽  
Inlet Temperature ◽  
Local Optimum ◽  
Naphtha Reforming ◽  
Decision Variables ◽  
Algorithm Comparison ◽  
Honey Bee Mating Optimization

Abstract Modeling and optimization of the process of continuous catalytic reforming (CCR) of naphtha was investigated. The process model is based on a network of four main reactions which was proved to be quite effective in terms of industrial application. Temperatures of the inlet of four reactors were selected as the decision variables. The honey-bee mating optimization (HBMO) and the genetic algorithm (GA) were applied to solve the optimization problem and the results of these two methods were compared. The profit was considered as the objective function which was subject to maximization. Optimization of the CCR moving bed reactors to reach maximum profit was carried out by the HBMO algorithm and the inlet temperature reactors were considered as decision variables. The optimization results showed that an increase of 3.01% in the profit can be reached based on the results of the HBMO algorithm. Comparison of the performance of optimization by the HBMO and the GA for the naphtha reforming model showed that the HBMO is an effective and rapid converging technique which can reach a better optimum results than the GA. The results showed that the HBMO has a better performance than the GA in finding the global optimum with fewer number of objective function evaluations. Also, it was shown that the HBMO is less likely to get stuck in a local optimum.

scholarly journals Multi Objective Simulated Annealing Approach for Facility Layout Design

2018 ◽  
Vol 3 (4) ◽  
pp. 365-380 ◽  
Author(s):  
Safiye Turgay
Keyword(s):  
Simulated Annealing ◽  
Objective Function ◽  
Material Handling ◽  
Simulated Annealing Algorithm ◽  
Facility Layout ◽  
Layout Design ◽  
Local Optima ◽  
Facility Layout Design ◽  
Multi Objective ◽  
Annealing Algorithm

Facility layout design problem considers the departments’ physcial layout design with area requirements in some restrictions such as material handling costs, remoteness and distance requests. Briefly, facility layout problem related to optimization of the layout costs and working conditions. This paper proposes a new multi objective simulated annealing algorithm for solving of the unequal area in layout design. Using of the different objective weights are generated with entropy approach and used in the alternative layout design. Multi objective function takes into the objective function and constraints. The suggested heuristic algorithm used the multi-objective parameters for initialization. Then prefered the entropy approach determines the weight of the objective functions. After the suggested improved simulated annealing approach applied to whole developed model. A multi-objective simulated annealing algorithm is implemented to increase the diversity and reduce the chance of getting layout conditions in local optima.

Anchor Deployment Planning Using Simulated Annealing

2002 ◽  
Author(s):  
Oscar Brito Augusto
Keyword(s):  
Simulated Annealing ◽  
Objective Function ◽  
Deep Water ◽  
Simulated Annealing Algorithm ◽  
Production Systems ◽  
Offshore Platforms ◽  
Additional Advantage ◽  
Floating System ◽  
Deployment Planning ◽  
Planning Methodology

In this work a planning methodology for deep-water anchor deployment of anchor lines for offshore platforms and floating production systems aiming at operational resources optimization is explored, by minimizing a multi criteria objective function. A Simulated Annealing Algorithm was used to optimize the objective function. As an additional advantage, inherited from the proposed methodology, the planning automation is achieved. Planning automation overcomes the traditional way based on trial error exercise, where an engineer using an anchoring application, decides how much of work wire and anchoring line must be paid out from both the floating system and the supply boat and additionally which horizontal force must be applied to the line trying settle the anchor on a previously defined target in the ocean floor. Some cases, from anchor deployment of some MODUs operating in deep-water oil fields in Brazil, are shown demonstrating some potentialities of the proposed model.

Decline of Real Power Loss by the Combination of Ant Colony Optimization and Simulated Annealing Algorithm

2016 ◽  
Vol 23 ◽  
pp. 113-119 ◽  
Author(s):  
K. Lenin ◽  
B. Ravindhranath Reddy ◽  
M. Suryakalavathi
Keyword(s):  
Simulated Annealing ◽  
Ant Colony Optimization ◽  
Power Loss ◽  
Simulated Annealing Algorithm ◽  
Reactive Power ◽  
Test System ◽  
Ant Colony ◽  
Local Optimum ◽  
Real Power ◽  
Combined Algorithm

Combination of ant colony optimization (ACO) algorithm and simulated annealing (SA) algorithm has been done to solve the reactive power problem.In this proposed combined algorithm (CA), the leads of parallel, collaborative and positive feedback of the ACO algorithm has been used to apply the global exploration in the current temperature. An adaptive modification threshold approach is used to progress the space exploration and balance the local exploitation. When the calculation process of the ACO algorithm falls into the inactivity, immediately SA algorithm is used to get a local optimal solution. Obtained finest solution of the ACO algorithm is considered as primary solution for SA algorithm, and then a fine exploration is executed in the neighborhood. Very importantly the probabilistic jumping property of the SA algorithm is used effectively to avoid solution falling into local optimum. The proposed combined algorithm (CA) approach has been tested in standard IEEE 30 bus test system and simulation results show obviously about the better performance of the proposed algorithm in reducing the real power loss with control variables within the limits.

scholarly journals Research on airport multi-objective optimization of stand allocation based on simulated annealing algorithm

2021 ◽  
Vol 18 (6) ◽  
pp. 8314-8330
Author(s):  
Ningning Zhao ◽  
◽  
Mingming Duan
Keyword(s):  
Mathematical Model ◽  
Simulated Annealing ◽  
Objective Function ◽  
Simulated Annealing Algorithm ◽  
Allocation Scheme ◽  
Multi Objective Optimization ◽  
Practical Application ◽  
Allocation Decision ◽  
Multi Objective ◽  
Annealing Algorithm

<abstract> <p>In this study, a multi-objective optimized mathematical model of stand pre-allocation is constructed with the shortest travel distance for passengers, the lowest cost for airlines and the efficiency of stand usage as the overall objectives. The actual data of 12 flights at Lanzhou Zhongchuan Airport are analyzed by application and solved by simulated annealing algorithm. The results of the study show that the total objective function of the constructed model allocation scheme is reduced by 40.67% compared with the actual allocation scheme of the airport, and the distance traveled by passengers is reduced by a total of 4512 steps, while one stand is saved and the efficiency of stand use is increased by 31%, in addition to the reduction of airline cost by 300 RMB. In summary, the model constructed in the study has a high practical application value and is expected to be used for airport stand pre-allocation decision in the future.</p> </abstract>

scholarly journals Declining Neighborhood Simulated Annealing Algorithm to Robust Fixture Synthesis in a Point-Set Domain

2021 ◽  
Author(s):  
Taqiaden Alshameri ◽  
Yude Dong ◽  
Abdullah Alqadhi
Keyword(s):  
Simulated Annealing ◽  
Computer Aided Design ◽  
Measurement Errors ◽  
Simulated Annealing Algorithm ◽  
Application Programming Interface ◽  
Computer Code ◽  
Search Space ◽  
Global Optimum ◽  
Contact Points ◽  
Novel Variant

Abstract Fixture synthesis addresses the problem of fixture-elements placement on the workpiece surfaces. This article presents a novel variant of the Simulated Annealing (SA) algorithm called Declining Neighborhood Simulated Annealing (DNSA) specifically developed for the problem of fixture synthesis. The objective is to minimize measurement errors in the machined features induced by the misalignment at workpiece-locator contact points. The algorithm systematically evaluates different fixture layouts to reach a sufficient approximation of the global optimum robust layout. For each iteration, a set of previously accepted candidates are exploited to predict the next move. Throughout the progress of the algorithm, the search space is reduced and the new candidates are designated according to a declining Probability Density Function (PDF). To assure best performance, the DNSA parameters are configured using the Technique for Order Preference by Similarity to Ideal Solution (TOPOSIS). Moreover, the parameters are set to auto-adapt the complexity of a given input based on a Shanon entropy index. The optimization process is carried out automatically in the Computer-Aided Design (CAD) environment NX; a computer code was developed for this purpose using the Application Programming Interface (API) NXOpen. Benchmark examples from industrial partner and literature demonstrate satisfactory results.

Sign in / Sign up

Export Citation Format

Share Document