scholarly journals Global optimization of Morse clusters with a heuristic algorithm based on the Strongin method

2018 ◽  
Vol 1096 ◽  
pp. 012088
Author(s):  
A N Kovartsev ◽  
D A Popova-Kovartseva
Keyword(s):  
Global Optimization ◽  
Heuristic Algorithm ◽  
Morse Clusters

Salp swarm algorithm with crossover scheme and Lévy flight for global optimization

2021 ◽  
pp. 1-12
Author(s):  
Heming Jia ◽  
Chunbo Lang
Keyword(s):  
Global Optimization ◽  
Heuristic Algorithm ◽  
Experimental Results ◽  
High Dimensional ◽  
Lévy Flight ◽  
Test Functions ◽  
Levy Flight ◽  
Salp Swarm Algorithm ◽  
Swarm Algorithm ◽  
Accuracy And Stability

Salp swarm algorithm (SSA) is a meta-heuristic algorithm proposed in recent years, which shows certain advantages in solving some optimization tasks. However, with the increasing difficulty of solving the problem (e.g. multi-modal, high-dimensional), the convergence accuracy and stability of SSA algorithm decrease. In order to overcome the drawbacks, salp swarm algorithm with crossover scheme and Lévy flight (SSACL) is proposed. The crossover scheme and Lévy flight strategy are used to improve the movement patterns of salp leader and followers, respectively. Experiments have been conducted on various test functions, including unimodal, multimodal, and composite functions. The experimental results indicate that the proposed SSACL algorithm outperforms other advanced algorithms in terms of precision, stability, and efficiency. Furthermore, the Wilcoxon’s rank sum test illustrates the advantages of proposed method in a statistical and meaningful way.

scholarly journals Global Optimization of Morse Clusters by Potential Energy Transformations

2004 ◽  
Vol 16 (4) ◽  
pp. 371-379 ◽  
Author(s):  
Jonathan P. K. Doye ◽  
Robert H. Leary ◽  
Marco Locatelli ◽  
Fabio Schoen
Keyword(s):  
Global Optimization ◽  
Potential Energy ◽  
Morse Clusters

scholarly journals A Novel Bat Algorithm with Multiple Strategies Coupling for Numerical Optimization

Mathematics ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 135 ◽  
Author(s):  
Yechuang Wang ◽  
Penghong Wang ◽  
Jiangjiang Zhang ◽  
Zhihua Cui ◽  
Xingjuan Cai ◽  
...  
Keyword(s):  
Global Optimization ◽  
Heuristic Algorithm ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Bat Algorithm ◽  
Global Search ◽  
Excellent Performance ◽  
Multiple Strategies ◽  
Search Capability

A bat algorithm (BA) is a heuristic algorithm that operates by imitating the echolocation behavior of bats to perform global optimization. The BA is widely used in various optimization problems because of its excellent performance. In the bat algorithm, the global search capability is determined by the parameter loudness and frequency. However, experiments show that each operator in the algorithm can only improve the performance of the algorithm at a certain time. In this paper, a novel bat algorithm with multiple strategies coupling (mixBA) is proposed to solve this problem. To prove the effectiveness of the algorithm, we compared it with CEC2013 benchmarks test suits. Furthermore, the Wilcoxon and Friedman tests were conducted to distinguish the differences between it and other algorithms. The results prove that the proposed algorithm is significantly superior to others on the majority of benchmark functions.

scholarly journals A Novel Hybrid Meta-Heuristic Algorithm Based on the Cross-Entropy Method and Firefly Algorithm for Global Optimization

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 494 ◽  
Author(s):  
Guocheng Li ◽  
Pei Liu ◽  
Chengyi Le ◽  
Benda Zhou
Keyword(s):  
Global Optimization ◽  
Heuristic Algorithm ◽  
Hybrid Algorithm ◽  
Firefly Algorithm ◽  
Entropy Method ◽  
Cross Entropy ◽  
Local Optimum ◽  
Cross Entropy Method ◽  
The Cross ◽  
Searching Ability

Global optimization, especially on a large scale, is challenging to solve due to its nonlinearity and multimodality. In this paper, in order to enhance the global searching ability of the firefly algorithm (FA) inspired by bionics, a novel hybrid meta-heuristic algorithm is proposed by embedding the cross-entropy (CE) method into the firefly algorithm. With adaptive smoothing and co-evolution, the proposed method fully absorbs the ergodicity, adaptability and robustness of the cross-entropy method. The new hybrid algorithm achieves an effective balance between exploration and exploitation to avoid falling into a local optimum, enhance its global searching ability, and improve its convergence rate. The results of numeral experiments show that the new hybrid algorithm possesses more powerful global search capacity, higher optimization precision, and stronger robustness.

ABCluster: the artificial bee colony algorithm for cluster global optimization

2015 ◽  
Vol 17 (37) ◽  
pp. 24173-24181 ◽  
Author(s):  
Jun Zhang ◽  
Michael Dolg
Keyword(s):  
Global Optimization ◽  
Swarm Intelligence ◽  
Heuristic Algorithm ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Applied Mathematics ◽  
Fundamental Importance ◽  
Interesting Problem ◽  
Bee Colony

Global optimization of cluster geometries is of fundamental importance in chemistry and an interesting problem in applied mathematics. We apply a swarm-intelligence based heuristic algorithm, i.e. the artificial bee colony algorithm to solve this problem for various kinds of clusters.

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