A new swarm algorithm for global optimization of multimodal functions over multi-threading architecture hybridized with simulating annealing
| Autor: | Alfonso Murillo-Suarez, Felix Martinez-Rios |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
Initialization Particle swarm optimization Swarm behaviour 020206 networking & telecommunications 02 engineering and technology Simulating annealing Multithreading 0202 electrical engineering electronic engineering information engineering General Earth and Planetary Sciences 020201 artificial intelligence & image processing Algorithm Global optimization General Environmental Science |
| Zdroj: | Procedia Computer Science. 135:449-456 |
| ISSN: | 1877-0509 |
| DOI: | 10.1016/j.procs.2018.08.196 |
| Popis: | This paper presents a new algorithm, PCLPSO, based on particle swarm optimization, which uses comprehensive learning particle swarm optimizer. Our algorithm executes C parallel CLPSO algorithms. We adopted as a criterion of completion a maximum value of evaluations of the objective function. During the execution of the CLPSO algorithms, when a certain evaluation value of the functions is reached, the best k are selected, and different initialization criteria are applied to continue the execution of the CLPSO algorithms: restarting the worst ones for the best solution or restores the worst ones to a random solution. For this restart, we use the Boltzmann criterion in a similar way as Simulating Annealing (SA) does. In this work, the experimental results obtained for the search of the minimum of 16 multimodal test functions such as Rosenbrock, Griewank, Rastrigin, Brannin, Schwefel, and others. Our algorithm proved to be more efficient than the traditional CLPSO in its experimental results, and the nonparametric Wilcoxon test confirmed this. |
| Databáze: | OpenAIRE |
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