Name
Title | ||
|---|---|---|
Improving the Local Search Ability of Spider Monkey Optimization Algorithm Using Quadratic Approximation for Unconstrained Optimization |
Abstract | ||
|---|---|---|
Spider monkey optimization SMO algorithm, which simulates the food searching behavior of a swarm of spider monkeys, is a new addition to the class of swarm intelligent techniques for solving unconstrained optimization problems. The purpose of this article is to study the performance of SMO after incorporating quadratic approximation QA operator in it. The proposed version is named as QA-based spider monkey optimization QASMO. An experimental study has been carried out to check the validity and applicability of QASMO. For validation purpose, the performance of QASMO is tested over a benchmark set of 46 scalable and nonscalable problems, and results are compared with the original SMO algorithm. In order to test the applicability of the proposed algorithm in solving real-life optimization problems, one of the most challenging optimization problems, namely, Lennard-Jones LJ problem is considered. LJ clusters containing atoms from three to ten have been taken into consideration, and results are presented. To the best of our knowledge, this is the first attempt to apply SMO and its proposed variant on a real-life problem. The results demonstrate that incorporation of QA in SMO has positive effects on its performance in terms of reliability, efficiency, and accuracy. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1111/coin.12081 | Computational Intelligence |
Keywords | Field | DocType |
spider monkey optimization,quadratic approximation,swarm intelligent techniques,unconstrained optimization,global optimization,Lennard-Jones problem | Swarm behaviour,Computer science,Artificial intelligence,Operator (computer programming),Optimization problem,Mathematical optimization,Quadratic equation,Algorithm,Multi-swarm optimization,Local search (optimization),Sequential minimal optimization,Machine learning,Scalability | Journal |
Volume | Issue | ISSN |
33 | 2 | 0824-7935 |
Citations | PageRank | References |
4 | 0.40 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| kavita gupta | 1 | 4 | 0.40 |
| Kusum Deep | 2 | 876 | 82.14 |
| Jagdish Chand Bansal | 3 | 651 | 52.30 |