A multiprocess Salp swarm optimization with a heuristic based on crossing partial solutions
| Autor: | Alfonso Murillo-Suarez, Felix Martinez-Rios |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Optimization problem
Computer science Wireless network business.industry Heuristic (computer science) Heuristic Swarm behaviour 020206 networking & telecommunications Image processing Robotics 02 engineering and technology Genetic algorithm 0202 electrical engineering electronic engineering information engineering Benchmark (computing) General Earth and Planetary Sciences Salp swarm algorithm 020201 artificial intelligence & image processing Artificial intelligence business Algorithm General Environmental Science |
| Zdroj: | Procedia Computer Science. 179:440-447 |
| ISSN: | 1877-0509 |
| DOI: | 10.1016/j.procs.2021.01.027 |
| Popis: | The Salp swarm algorithm (SSA) is one of the most recent metaheuristic optimization algorithms. SSA has been used successfully to solve optimization problems in different research areas such as machine learning, engineering design, wireless networks, image processing, mobile robotics, and energy. In this article, we present a multi-threaded implementation of the SSA algorithm. Each thread executes an SSA algorithm that shares information among the swarms to get a better solution. The best partial solutions of each swarm intersect in a similar way of genetic algorithms. The experiments with nineteen benchmark functions (unimodal, multimodal, and composite) show the results obtained with this new algorithm are better than those achieved with the original algorithm. |
| Databáze: | OpenAIRE |
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