Self-adaptive Hybrid differential evolution with simulated annealing algorithm for numerical optimization

2008 ◽  
Author(s):  
Zhong-bo Hu ◽  
Qing-hua Su ◽  
Sheng-wu Xiong ◽  
Fu-gao Hu
Keyword(s):  
Simulated Annealing ◽  
Differential Evolution ◽  
Numerical Optimization ◽  
Simulated Annealing Algorithm ◽  
Annealing Algorithm ◽  
Self Adaptive

A hybrid differential evolution and simulated annealing algorithm for global optimization

2021 ◽  
pp. 1-17
Author(s):  
Xiaobing Yu ◽  
Zhenjie Liu ◽  
XueJing Wu ◽  
Xuming Wang
Keyword(s):  
Global Optimization ◽  
Simulated Annealing ◽  
Differential Evolution ◽  
Simulated Annealing Algorithm ◽  
Optimization Problems ◽  
Local Optimum ◽  
Search Efficiency ◽  
Optimal State ◽  
Mutation Operation ◽  
Annealing Algorithm

Differential evolution (DE) is one of the most effective ways to solve global optimization problems. However, considering the traditional DE has lower search efficiency and easily traps into local optimum, a novel DE variant named hybrid DE and simulated annealing (SA) algorithm for global optimization (HDESA) is proposed in this paper. This algorithm introduces the concept of “ranking” into the mutation operation of DE and adds the idea of SA to the selection operation. The former is to improve the exploitation ability and increase the search efficiency, and the latter is to enhance the exploration ability and prevent the algorithm from trapping into the local optimal state. Therefore, a better balance can be achieved. The experimental results and analysis have shown its better or at least equivalent performance on the exploitation and exploration capability for a set of 24 benchmark functions. It is simple but efficient.

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 Incorporating simulated annealing algorithm in the Weibull distribution for valuation of investment return of Malaysian property development sector

2021 ◽  
Vol 12 ◽  
pp. 22
Author(s):  
Hamza Abubakar ◽  
Shamsul Rijal Muhammad Sabri
Keyword(s):  
Simulated Annealing ◽  
Weibull Distribution ◽  
Differential Evolution ◽  
Simulated Annealing Algorithm ◽  
Estimation Methods ◽  
Coefficient Of Determination ◽  
Property Development ◽  
Investment Return ◽  
Annealing Algorithm

In this study, a simulated annealing algorithm(SAA) has been incorporated in the Weibull Distribution (WD) for Valuation of Investment Return. The purpose is to examine the behaviour of investment's attractiveness in the Malaysian property development sector (MPDS) for a long-term investment period. The research intends is to produce parameters estimates of the WD using MIRR data extracted from the financial report of MPDS for 5 years investment period. The shape parameter of the WD reflects the effectiveness in maximizing the investment performance on MPDS with lower returns and is represented as the slope of the fitted line on a Weibull probability plot. The estimated results obtained using the Simulated annealing algorithm (SAA) has been compared with Differential Evolution (DE) and other existing estimation methods in terms of root mean square error (R-MSE) and coefficient of determination (R-Square). The findings revealed that Weibull distribution parameters estimated via Simulated annealing algorithm have good agreement with parameters estimated via Differential Evolution (DE) and other existing methods based on the transformed MIRR data from the MPDS. The study is expected to provide an overview of the investment behaviour for the long-term investment return in the MPDS. Therefore, SAA in estimating the WD parameters can serve as a good alternative approach for the assessment of the investment potential using MIRR data. The study will be extended to accommodate the growth rate arising from the financial data such as investment growth and insurance claim data.

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