scholarly journals Rank-driven salp swarm algorithm with orthogonal opposition-based learning for global optimization

2021 ◽  
Author(s):  
Zongshan Wang ◽  
Hongwei Ding ◽  
Zhijun Yang ◽  
Bo Li ◽  
Zheng Guan ◽  
...  
Keyword(s):  
Global Optimization ◽  
Salp Swarm Algorithm ◽  
Opposition Based Learning ◽  
Swarm Algorithm

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.

Improved Salp Swarm Algorithm based on opposition based learning and novel local search algorithm for feature selection

2020 ◽  
Vol 145 ◽  
pp. 113122 ◽  
Author(s):  
Mohammad Tubishat ◽  
Norisma Idris ◽  
Liyana Shuib ◽  
Mohammad A.M. Abushariah ◽  
Seyedali Mirjalili
Keyword(s):  
Feature Selection ◽  
Local Search ◽  
Search Algorithm ◽  
Local Search Algorithm ◽  
Salp Swarm Algorithm ◽  
Opposition Based Learning ◽  
Swarm Algorithm

scholarly journals A Hybrid SSA and SMA with Mutation Opposition-Based Learning for Constrained Engineering Problems

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Shuang Wang ◽  
Qingxin Liu ◽  
Yuxiang Liu ◽  
Heming Jia ◽  
Laith Abualigah ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Hybrid Optimization ◽  
Slime Mould ◽  
Salp Swarm Algorithm ◽  
Constrained Problems ◽  
Opposition Based Learning ◽  
Hybrid Optimization Algorithm ◽  
Engineering Problems ◽  
Swarm Algorithm

Based on Salp Swarm Algorithm (SSA) and Slime Mould Algorithm (SMA), a novel hybrid optimization algorithm, named Hybrid Slime Mould Salp Swarm Algorithm (HSMSSA), is proposed to solve constrained engineering problems. SSA can obtain good results in solving some optimization problems. However, it is easy to suffer from local minima and lower density of population. SMA specializes in global exploration and good robustness, but its convergence rate is too slow to find satisfactory solutions efficiently. Thus, in this paper, considering the characteristics and advantages of both the above optimization algorithms, SMA is integrated into the leader position updating equations of SSA, which can share helpful information so that the proposed algorithm can utilize these two algorithms’ advantages to enhance global optimization performance. Furthermore, Levy flight is utilized to enhance the exploration ability. It is worth noting that a novel strategy called mutation opposition-based learning is proposed to enhance the performance of the hybrid optimization algorithm on premature convergence avoidance, balance between exploration and exploitation phases, and finding satisfactory global optimum. To evaluate the efficiency of the proposed algorithm, HSMSSA is applied to 23 different benchmark functions of the unimodal and multimodal types. Additionally, five classical constrained engineering problems are utilized to evaluate the proposed technique’s practicable abilities. The simulation results show that the HSMSSA method is more competitive and presents more engineering effectiveness for real-world constrained problems than SMA, SSA, and other comparative algorithms. In the end, we also provide some potential areas for future studies such as feature selection and multilevel threshold image segmentation.

scholarly journals An Opposition-Based Chaotic Salp Swarm Algorithm for Global Optimization

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 36485-36501 ◽  
Author(s):  
Xiaoqiang Zhao ◽  
Fan Yang ◽  
Yazhou Han ◽  
Yanpeng Cui
Keyword(s):  
Global Optimization ◽  
Salp Swarm Algorithm ◽  
Swarm Algorithm

A novel chaotic salp swarm algorithm for global optimization and feature selection

2018 ◽  
Vol 48 (10) ◽  
pp. 3462-3481 ◽  
Author(s):  
Gehad Ismail Sayed ◽  
Ghada Khoriba ◽  
Mohamed H. Haggag
Keyword(s):  
Feature Selection ◽  
Global Optimization ◽  
Salp Swarm Algorithm ◽  
Swarm Algorithm
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