A Simulated Annealing Algorithm for Noisy Multiobjective Optimization

2012 ◽  
Vol 20 (5-6) ◽  
pp. 255-276 ◽  
Author(s):  
Ville Mattila ◽  
Kai Virtanen ◽  
Raimo P. Hämäläinen
Keyword(s):  
Simulated Annealing ◽  
Multiobjective Optimization ◽  
Simulated Annealing Algorithm ◽  
Annealing Algorithm

A SIMULATED ANNEALING ALGORITHM FOR MULTIOBJECTIVE OPTIMIZATION

2000 ◽  
Vol 33 (1) ◽  
pp. 59-85 ◽  
Author(s):  
A. SUPPAPITNARM ◽  
K. A. SEFFEN ◽  
G. T. PARKS ◽  
P. J. CLARKSON
Keyword(s):  
Simulated Annealing ◽  
Multiobjective Optimization ◽  
Simulated Annealing Algorithm ◽  
Annealing Algorithm

scholarly journals An Enhanced Differential Evolution Based Algorithm with Simulated Annealing for Solving Multiobjective Optimization Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Bili Chen ◽  
Wenhua Zeng ◽  
Yangbin Lin ◽  
Qi Zhong
Keyword(s):  
Simulated Annealing ◽  
Multiobjective Optimization ◽  
Differential Evolution ◽  
Simulated Annealing Algorithm ◽  
Optimization Problems ◽  
Search Space ◽  
Acceptance Probability ◽  
Nondominated Solutions ◽  
Multiobjective Optimization Problems ◽  
Annealing Algorithm

An enhanced differential evolution based algorithm, named multi-objective differential evolution with simulated annealing algorithm (MODESA), is presented for solving multiobjective optimization problems (MOPs). The proposed algorithm utilizes the advantage of simulated annealing for guiding the algorithm to explore more regions of the search space for a better convergence to the true Pareto-optimal front. In the proposed simulated annealing approach, a new acceptance probability computation function based on domination is proposed and some potential solutions are assigned a life cycle to have a priority to be selected entering the next generation. Moreover, it incorporates an efficient diversity maintenance approach, which is used to prune the obtained nondominated solutions for a good distributed Pareto front. The feasibility of the proposed algorithm is investigated on a set of five biobjective and two triobjective optimization problems and the results are compared with three other algorithms. The experimental results illustrate the effectiveness of the proposed algorithm.

scholarly journals Research on Fish Slicing Method Based on Simulated Annealing Algorithm

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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