Name
Abstract | ||
|---|---|---|
Salp swarm algorithm (SSA) based on the swarming behaviour of salps found in ocean, is a very competitive algorithm and has proved its worth as an excellent problem optimizer. Though SSA is a very challenging algorithm but it suffers from the problem of poor exploitation, local optima stagnation and unbalanced exploration and exploitation operations. Thus in order to mitigate these problems and improve the working properties, seven new versions of SSA are proposed in present work. All the new versions employ new set of mutation properties along with some common properties. The common properties of all the algorithms include division of generations, adaptive switching and adaptive population strategy. Overall, the proposed algorithms are self-adaptive in nature along with some added mutation properties. For performance evaluation, the proposed algorithms are subjected to variable initial population and dimension sizes. The best among the proposed is then tested on CEC 2005, CEC 2015 benchmark problems and real world problems from CEC 2011 benchmarks. Experimental and statistical results show that the proposed mutation clock SSA (MSSA) is best among all the algorithms under comparison. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.eswa.2020.114368 | Expert Systems with Applications |
Keywords | DocType | Volume |
Salp swarm algorithm,Mutation operators,Adaptive properties,Benchmark problems,Nature inspired algorithms | Journal | 169 |
ISSN | Citations | PageRank |
0957-4174 | 1 | 0.35 |
References | Authors | |
41 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rohit Salgotra | 1 | 5 | 1.74 |
| Urvinder Singh | 2 | 106 | 9.55 |
| Gurdeep Singh | 3 | 1 | 0.35 |
| Supreet Singh | 4 | 2 | 0.70 |
| Amir Hossein Gandomi | 5 | 1836 | 110.25 |