scholarly journals A new hybrid algorithm based on MVO and SA for function optimization

2022 ◽  
Vol 13 (2) ◽  
pp. 237-254 ◽  
Author(s):  
Ömer Yılmaz ◽  
Adem Alpaslan Altun ◽  
Murat Köklü
Keyword(s):  
Black Hole ◽  
Simulated Annealing ◽  
Hybrid Algorithm ◽  
Hybrid Algorithms ◽  
Function Optimization ◽  
Metaheuristic Algorithms ◽  
New Method ◽  
Exploration And Exploitation ◽  
Acceptance Probability

Hybrid algorithms are widely used today to increase the performance of existing algorithms. In this paper, a new hybrid algorithm called IMVOSA that is based on multi-verse optimizer (MVO) and simulated annealing (SA) is used. In this model, a new method called the black hole selection (BHS) is proposed, in which exploration and exploitation can be increased. In the BHS method, the acceptance probability feature of the SA algorithm is used to increase exploitation by searching for the best regions found by the MVO algorithm. The proposed IMVOSA algorithm has been tested on 50 benchmark functions. The performance of IMVOSA has been compared with other latest and well-known metaheuristic algorithms. The consequences show that IMVOSA produces highly successful and competitive results.

scholarly journals Improved Salp Swarm Algorithm with Simulated Annealing for Solving Engineering Optimization Problems

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1092
Author(s):  
Qing Duan ◽  
Lu Wang ◽  
Hongwei Kang ◽  
Yong Shen ◽  
Xingping Sun ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Optimization Problems ◽  
Chaotic Sequence ◽  
Engineering Optimization ◽  
Exploration And Exploitation ◽  
Salp Swarm Algorithm ◽  
Symmetric Perturbation ◽  
Swarm Algorithm ◽  
Search Capability ◽  
Engineering Optimization Problems

Swarm-based algorithm can successfully avoid the local optimal constraints, thus achieving a smooth balance between exploration and exploitation. Salp swarm algorithm(SSA), as a swarm-based algorithm on account of the predation behavior of the salp, can solve complex daily life optimization problems in nature. SSA also has the problems of local stagnation and slow convergence rate. This paper introduces an improved salp swarm algorithm, which improve the SSA by using the chaotic sequence initialization strategy and symmetric adaptive population division. Moreover, a simulated annealing mechanism based on symmetric perturbation is introduced to enhance the local jumping ability of the algorithm. The improved algorithm is referred to SASSA. The CEC standard benchmark functions are used to evaluate the efficiency of the SASSA and the results demonstrate that the SASSA has better global search capability. SASSA is also applied to solve engineering optimization problems. The experimental results demonstrate that the exploratory and exploitative proclivities of the proposed algorithm and its convergence patterns are vividly improved.

scholarly journals Hybrid Genetic Algorithm and Simulated Annealing for Function Optimization

2017 ◽  
Vol 1 (2) ◽  
pp. 82 ◽  
Author(s):  
Tirana Noor Fatyanosa ◽  
Andreas Nugroho Sihananto ◽  
Gusti Ahmad Fanshuri Alfarisy ◽  
M Shochibul Burhan ◽  
Wayan Firdaus Mahmudy
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Function Optimization ◽  
Non Linear ◽  
Speed Up ◽  
Linear Problems

The optimization problems on real-world usually have non-linear characteristics. Solving non-linear problems is time-consuming, thus heuristic approaches usually are being used to speed up the solution’s searching. Among of the heuristic-based algorithms, Genetic Algorithm (GA) and Simulated Annealing (SA) are two among most popular. The GA is powerful to get a nearly optimal solution on the broad searching area while SA is useful to looking for a solution in the narrow searching area. This study is comparing performance between GA, SA, and three types of Hybrid GA-SA to solve some non-linear optimization cases. The study shows that Hybrid GA-SA can enhance GA and SA to provide a better result

Analytical Review on the Modern Optimization Algorithms in Logistics

2020 ◽  
Vol 14 (1) ◽  
pp. 25-31
Author(s):  
Mohammad Zaher Akkad ◽  
Tamás Bányai
Keyword(s):  
Content Analysis ◽  
Optimization Algorithms ◽  
Hybrid Algorithms ◽  
Metaheuristic Algorithms ◽  
Complex Problems ◽  
Analytical Review ◽  
Optimum Solution ◽  
Short Time ◽  
Modern Optimization

Optimization algorithms are used to reach the optimum solution from a set of available alternatives within a short time relatively. With having complex problems in the logistics area, the optimization algorithms evolved from traditional mathematical approaches to modern ones that use heuristic and metaheuristic approaches. Within this paper, the authors present an analytical review that includes illustrative and content analysis for the used modern algorithms in the logistics area. The analysis shows accelerated progress in using the heuristic/metaheuristic algorithms for logistics applications. It also shows the strong presence of hybrid algorithms that use heuristic and metaheuristic approaches. Those hybrid algorithms are providing very efficient results.

scholarly journals Research on Material Allocation Path Based on Hopfield Neural Network and Simulated Annealing Hybrid Algorithm

2020 ◽  
Vol 1682 ◽  
pp. 012003
Author(s):  
Junpeng Xi ◽  
Xudong Zhao ◽  
Yingshuang Zhu ◽  
Baoqiang Xiao ◽  
Shun Chen
Keyword(s):  
Neural Network ◽  
Simulated Annealing ◽  
Hybrid Algorithm ◽  
Hopfield Neural Network
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